634 JOURNAL OF COSMETIC SCIENCE
microbiome.2 Understanding these interactions is crucial for deciphering their impact on
health and disease, paving the way for targeted interventions and therapies that harness the
microbiome’s potential to support overall well-being.
Furthermore, the interactions between the gut microbiome and the skin, commonly referred
to as the “gut-skin axis,” have been increasingly clarified. Research has firmly established
that the gut-skin axis represents a pathway of communication between the gastrointestinal
(GI) tract and the skin, highlighting the interconnectedness of these two organ systems.3
While the terms “skin-gut axis” and “gut-skin axis” are often used interchangeably in
research, this review will specifically use “gut-skin axis” to denote the communication
pathway from the gut microbiome to the skin. The mechanisms underlying this link
from the gut microbiome to the skin include immunomodulation, wherein the gut
microbiome influences the systemic immune response, impacting skin inflammation and
immune function.4 Hormonal pathways also play a crucial role, as hormones produced in
response to gut microbial activity can affect skin physiology.3 Additionally, the production
of metabolites, such as short-chain fatty acids (SCFAs) by gut bacteria, can enter the
bloodstream and exert effects on skin cells, contributing to the regulation of skin barrier
function and inflammatory processes.5 Interestingly, emerging research has postulated the
bidirectional nature of this axis, suggesting that the state of the skin may, in turn, influence
the gut microbiome, known in this review as the “skin-gut axis.”6
Historically, microbiome studies primarily relied on culture-based techniques to identify
microorganisms present in samples.2Whilevaluableatthetime,thesemethodshadsignificant
limitations, including an inability to accurately represent the endogenous microbiome
environment and difficulties in culturing many microorganisms.7 Therefore, the introduction
of DNA-based culture-independent molecular techniques in the 1980s revolutionized
microbiome research.7 This paradigm shift involving analyzing the microbial DNA directly
from samples, instead of DNA from lab-grown cultures, redirected researchers’ focus toward
understanding microbial functions and interactions. Hence, metagenomics, which refers to
the study of the genetic material of entire communities of organisms, has emerged as a
pivotal technique.7 Ultimately, the “multi-omics” approach—comprising metagenomics and
additionally, metatranscriptomics, proteomics, lipidomics, and metabolomics—has become
standard practice for microbiome studies and these comprehensive methodologies are crucial
for advancing the understanding of the microbiome (summarized in Table I).
THE MULTI-OMICS APPROACH
Metagenomics is the unbiased study of the sequences and functions of all genetic information
extracted from a specific environment, such as the human skin microbiome. By employing
diverse bioinformatic systems and genomic technologies, metagenomics enables researchers
to explore the entire genome of various environmental communities and gives insight into
the diversity, structure and functional potential thereof.8 This approach was first introduced
in 1998 in reference to shotgun metagenomic sequencing. Nowadays, it is largely applied
to studies of marker genes, such as the 16S ribosomal RNA (rRNA) gene in the form
of targeted bacterial profiling (summarized in Table II).9,10 This method of microbiome
sequencing targets the 16S rRNA gene, which is highly conserved among bacteria and
archaea, although it contains hypervariable regions that can distinguish between different
species.11 Similarly, internal transcribed spacer (ITS) region amplification focuses on the
ITS region of the rRNA gene cluster, which is highly variable among different fungal

635 Bidirectional Gut-Skin Axis
species and is therefore used for the identification of fungi.12 Both 16S rRNA and ITS
targeted profiling provide high taxonomic resolution for the identification and classification
of bacteria/archaea and fungi, respectively, but their limited scopes necessitate additional
sequencing approaches.12
Conversely, shotgun metagenomic sequencing involves randomly sequencing all the DNA
present in a sample, without any prior amplification or targeting of specific genes. This
enables the cataloguing of genes from organisms within a community, as well as the analysis
of individual genomes within the ecosystem.11 Shotgun metagenomic sequencing has evolved
from utilizing Sanger sequencing to Next Generation Sequencing (NGS), which reduces
costs and increases sequencing depth.13 This process involves fragmenting DNA into smaller
pieces, which requires sophisticated computational systems to reconstruct and align the
sequences to reference genomes.11 Therefore, this method can be limited by the complexity
of data analysis, potential biases in DNA extraction and sequencing, and the need for high
computational resources and robust reference databases for accurate interpretation.11
Long-read sequencing has the advantage of generating long continuous sequences directly
from DNA instead of using amplified fragments and products as sequencing templates,
resulting in more uniform and biologically significant data.14 Examples of this technology
include the competitive Oxford Nanopore Technology™ (ONT) and Pacific Biosciences™
(PacBio), which are useful to sequence highly repetitive genome regions and may
independently be applied for the identification of the entire genome.
Quantitative PCR (qPCR) is an additional molecular technique employed for targeted detection
and quantification within microbiome studies. It involves amplifying and quantifying a
targeted DNA molecule and is, therefore, useful to detect absolute levels of bacteria, viruses,
fungi and protozoa down to the species or strain level, depending on the experimental design.15
Ultimately, metagenomic data is limited in its mechanistic insights and necessitates further
studies into how microbiome functions affect the host. This has led to the integration of other
functional “omics” or “multi-omics” approaches, such as transcriptomics, metabolomics,
proteomics and lipidomics.1
Transcriptomics is the study of the transcriptome, which is the complete set of RNA molecules
expressed by a genome in a specific cell or tissue at a given time. In transcriptomics, qPCR
may also be utilized following RNA sequencing (RNA-seq) to quantify specific microbial
gene expression.1 Alongside metagenomics, which identifies the genetic composition and
functionality of the microbiome, metatranscriptomics provides complementary insights by
revealing which genes are actively expressed by the microorganisms within the community.8
Metabolomics identifies and quantifies metabolites in various environments in order to
understand metabolic changes under different conditions.8 Host metabolomics focuses
on metabolites within the host organism, including compounds like amino acids and
lipids produced by the host’s metabolism. Meanwhile, microbial metabolomics examines
metabolites produced by microbial communities residing in or on the host, such as
antibiotics, toxins, and primary metabolites. Both approaches are crucial for understanding
the interactions between the host and microbial metabolites, and their impact on health
and disease.1 These metabolites are detected using techniques such as mass spectrometry
(MS) or nuclear magnetic resonance (NMR), followed by identification and quantification
by means of chromatography methods, including liquid chromatography (LC) and gas
chromatography (GC).8 This process ultimately provides a practical perspective on
metagenomic analysis and insights into the pathways linked to microbe-host interactions.1