Host and environmental factors shape upper airway microbiota and respiratory health across the human lifespan.

Our understanding of the normal variation in the upper respiratory tract (URT) microbiota across the human lifespan and how these relate to host, environment, and health is limited. We studied the microbiota of 3,104 saliva (<10 year-olds)/oropharynx (≥10 year-olds) and 2,485 nasopharynx samples of 3,160 Dutch individuals 0-87 years of age, participating in a cross-sectional population-wide study (PIENTER-3) using 16S-rRNA sequencing. The microbiota composition was strongly related to age, especially in the nasopharynx, with maturation occurring throughout childhood and adolescence. Clear niche- and age-specific associations were found between the microbiota composition and host/environmental factors and health outcomes. Among others, social interaction, sex, and season were associated with the nasopharyngeal microbial community. By contrast, the oral microbiota was more related to antibiotics, tobacco, and alcohol use. We present an atlas of the URT microbiota across the lifespan in association with environment and health, establishing a baseline for future research.

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients.

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

Transcription Increases the Cooperativity of Ribonucleoprotein Assembly.

The synthesis of new ribosomes begins during transcription of the rRNA and is widely assumed to follow an orderly 5' to 3' gradient. To visualize co-transcriptional assembly of ribosomal protein-RNA complexes in real time, we developed a single-molecule platform that simultaneously monitors transcription and protein association with the elongating transcript. Unexpectedly, the early assembly protein uS4 binds newly made pre-16S rRNA only transiently, likely due to non-native folding of the rRNA during transcription. Stable uS4 binding became more probable only in the presence of additional ribosomal proteins that bind upstream and downstream of protein uS4 by allowing productive assembly intermediates to form earlier. We propose that dynamic sampling of elongating RNA by multiple proteins overcomes heterogeneous RNA folding, preventing assembly bottlenecks and initiating assembly within the transcription time window. This may be a common feature of transcription-coupled RNP assembly.

Expanding Archaeal Diversity and Phylogeny: Past, Present, and Future.

The discovery of the Archaea is a major scientific hallmark of the twentieth century. Since then, important features of their cell biology, physiology, ecology, and diversity have been revealed. Over the course of some 40 years, the diversity of known archaea has expanded from 2 to about 30 phyla comprising over 20,000 species. Most of this archaeal diversity has been revealed by environmental 16S rRNA gene amplicon sequencing surveys using a broad range of universal and targeted primers. Of the few primers that target a large fraction of known archaeal diversity, all display a bias against recently discovered lineages, which limits studies aiming to survey overall archaeal diversity. Induced by genomic exploration of archaeal diversity, and improved phylogenomics approaches, archaeal taxonomic classification has been frequently revised. Due to computational limitations and continued discovery of new lineages, a stable archaeal phylogeny is not yet within reach. Obtaining phylogenetic and taxonomic consensus of archaea should be a high priority for the archaeal research community.

Nlrp6- and ASC-Dependent Inflammasomes Do Not Shape the Commensal Gut Microbiota Composition.

The gut microbiota regulate susceptibility to multiple human diseases. The Nlrp6-ASC inflammasome is widely regarded as a hallmark host innate immune axis that shapes the gut microbiota composition. This notion stems from studies reporting dysbiosis in mice lacking these inflammasome components when compared with non-littermate wild-type animals. Here, we describe microbial analyses in inflammasome-deficient mice while minimizing non-genetic confounders using littermate-controlled Nlrp6-deficient mice and ex-germ-free littermate-controlled ASC-deficient mice that were all allowed to shape their gut microbiota naturally after birth. Careful microbial phylogenetic analyses of these cohorts failed to reveal regulation of the gut microbiota composition by the Nlrp6- and ASC-dependent inflammasomes. Our results obtained in two geographically separated animal facilities dismiss a generalizable impact of Nlrp6- and ASC-dependent inflammasomes on the composition of the commensal gut microbiota and highlight the necessity for littermate-controlled experimental design in assessing the influence of host immunity on gut microbial ecology.

Integrated genomic and functional analyses of human skin-associated Staphylococcus reveal extensive inter- and intra-species diversity.

Human skin is stably colonized by a distinct microbiota that functions together with epidermal cells to maintain a protective physical barrier. Staphylococcus, a prominent genus of the skin microbiota, participates in colonization resistance, tissue repair, and host immune regulation in strain-specific manners. To unlock the potential of engineering skin microbial communities, we aim to characterize the diversity of this genus within the context of the skin environment. We reanalyzed an extant 16S rRNA amplicon dataset obtained from distinct body sites of healthy volunteers, providing a detailed biogeographic depiction of staphylococcal species that colonize our skin. S. epidermidis, S. capitis, and S. hominis were the most abundant staphylococcal species present in all volunteers and were detected at all body sites. Pan-genome analysis of isolates from these three species revealed that the genus-core was dominated by central metabolism genes. Species-restricted-core genes encoded known host colonization functions. The majority (~68%) of genes were detected only in a fraction of isolate genomes, underscoring the immense strain-specific gene diversity. Conspecific genomes grouped into phylogenetic clades, exhibiting body site preference. Each clade was enriched for distinct gene sets that are potentially involved in site tropism. Finally, we conducted gene expression studies of select isolates showing variable growth phenotypes in skin-like medium. In vitro expression revealed extensive intra- and inter-species gene expression variation, substantially expanding the functional diversification within each species. Our study provides an important resource for future ecological and translational studies to examine the role of shared and strain-specific staphylococcal genes within the skin environment.

An inhibitory immunoreceptor Allergin-1 regulates the intestinal dysbiosis and barrier function in mice.

The intestinal barrier consists of mucosal, epithelial, and immunological barriers and serves as a dynamic interface between the host and its environment. Disruption of the intestinal barrier integrity is a leading cause of various gastrointestinal diseases, such as inflammatory bowel disease. The homeostasis of the intestinal barrier is tightly regulated by crosstalk between gut microbes and the immune system; however, the implication of the immune system on the imbalance of gut microbes that disrupts barrier integrity remains to be fully elucidated. An inhibitory immunoglobulin-like receptor, Allergin-1, is expressed on mast cells and dendritic cells and inhibits Toll-like receptor (TLR)-2 and TLR-4 signaling in these cells. Since TLRs are major sensors of microbiota and are involved in local epithelial homeostasis, we investigated the role of Allergin-1 in maintaining intestinal homeostasis. Allergin-1-deficient (Milr1-/-) mice exhibited more severe dextran sulfate sodium (DSS)-induced colitis than did wild-type (WT) mice. Milr1-/- mice showed an enhanced intestinal permeability compared with WT mice even before DSS administration. Treatment of Milr1-/- mice with neomycin, but not ampicillin, restored intestinal barrier integrity. The 16S rRNA gene sequencing analysis demonstrated that Bifidobacterium pseudolongum was the dominant bacterium in Milr1-/- mice after treatment with ampicillin. Although the transfer of B. pseudolongum to germ-free WT mice had no effect on intestinal permeability, its transfer into ampicillin-treated WT mice enhanced intestinal permeability. These results demonstrated that Allergin-1 deficiency enhanced intestinal dysbiosis with expanded B. pseudolongum, which contributes to intestinal barrier dysfunction in collaboration with neomycin-sensitive and ampicillin-resistant microbiota.

Gut microbiome correlates with plasma lipids in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a complex, fatal neurodegenerative disease. Disease pathophysiology is incompletely understood but evidence suggests gut dysbiosis occurs in ALS, linked to impaired gastrointestinal integrity, immune system dysregulation and altered metabolism. Gut microbiome and plasma metabolome have been separately investigated in ALS, but little is known about gut microbe-plasma metabolite correlations, which could identify robust disease biomarkers and potentially shed mechanistic insight. Here, gut microbiome changes were longitudinally profiled in ALS and correlated to plasma metabolome. Gut microbial structure at the phylum level differed in ALS versus control participants, with differential abundance of several distinct genera. Unsupervised clustering of microbe and metabolite levels identified modules, which differed significantly in ALS versus control participants. Network analysis found several prominent amplicon sequence variants strongly linked to a group of metabolites, primarily lipids. Similarly, identifying the features that contributed most to case versus control separation pinpointed several bacteria correlated to metabolites, predominantly lipids. Mendelian randomization indicated possible causality from specific lipids related to fatty acid and acylcarnitine metabolism. Overall, the results suggest ALS cases and controls differ in their gut microbiome, which correlates with plasma metabolites, particularly lipids, through specific genera. These findings have the potential to identify robust disease biomarkers and shed mechanistic insight into ALS.

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.

BACKGROUND: The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult. RESULTS: In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states. CONCLUSIONS: We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data.

BACKGROUND: With the emergence of Oxford Nanopore technology, now the on-site sequencing of 16S rRNA from environments is available. Due to the error level and structure, the analysis of such data demands some database of reference sequences. However, many taxa from complex and diverse environments, have poor representation in publicly available databases. In this paper, we propose the METASEED pipeline for the reconstruction of full-length 16S sequences from such environments, in order to improve the reference for the subsequent use of on-site sequencing. RESULTS: We show that combining high-precision short-read sequencing of both 16S and full metagenome from the same samples allow us to reconstruct high-quality 16S sequences from the more abundant taxa. A significant novelty is the carefully designed collection of metagenome reads that matches the 16S amplicons, based on a combination of uniqueness and abundance. Compared to alternative approaches this produces superior results. CONCLUSION: Our pipeline will facilitate numerous studies associated with various unknown microorganisms, thus allowing the comprehension of the diverse environments. The pipeline is a potential tool in generating a full length 16S rRNA gene database for any environment.

PrimerEvalPy: a tool for in-silico evaluation of primers for targeting the microbiome.

BACKGROUND: The selection of primer pairs in sequencing-based research can greatly influence the results, highlighting the need for a tool capable of analysing their performance in-silico prior to the sequencing process. We therefore propose PrimerEvalPy, a Python-based package designed to test the performance of any primer or primer pair against any sequencing database. The package calculates a coverage metric and returns the amplicon sequences found, along with information such as their average start and end positions. It also allows the analysis of coverage for different taxonomic levels. RESULTS: As a case study, PrimerEvalPy was used to test the most commonly used primers in the literature against two oral 16S rRNA gene databases containing bacteria and archaea. The results showed that the most commonly used primer pairs in the oral cavity did not match those with the highest coverage. The best performing primer pairs were found for the detection of oral bacteria and archaea. CONCLUSIONS: This demonstrates the importance of a coverage analysis tool such as PrimerEvalPy to find the best primer pairs for specific niches. The software is available under the MIT licence at https://gitlab.citius.usc.es/lara.vazquez/PrimerEvalPy .

Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Integrative Analyses Reveal the Correlation Between the Airway Microbiome and Host Metabolism in Severe Community-acquired Pneumonia.

Community-acquired pneumonia (CAP) is a significant global health concern, responsible for high mortality and morbidity. Recent research has revealed a potential link between disordered microbiome and metabolism in pneumonia, although the precise relationship between these factors and severe CAP remains unclear. To address this knowledge gap, we conducted a comprehensive analysis utilizing 16S sequencing and LC-MS/MS metabolomics data to characterize the microbial profile in sputum and metabolic profile in serum in patients with severe community-acquired pneumonia (sCAP). Our analysis identified 13 genera through LEfSe analysis and 15 metabolites meeting specific criteria (P < 0.05, VIP ≥ 2, and |Log2(FC)| ≥ 2). The findings of this study demonstrate the presence of altered coordination between the microbiome of the lower respiratory tract and host metabolism in patients with sCAP. The observed concentration trends of specific metabolites across different disease stages further support the potential involvement of the serum metabolism in the development of sCAP. These correlations between the airway microbiome and host metabolism in sCAP patients have important implications for optimizing early diagnosis and developing individualized therapeutic strategies.

Multiomics Analysis Revealed Colorectal Cancer Pathogenesis.

Gut microbiota-derived microbial compounds may link to the pathogenesis of colorectal cancer (CRC). However, the role of the host-microbiome in the incidence and progression of CRC remains elusive. We performed 16S rRNA sequencing, metabolomics, and proteomic studies on samples from 85 CRC patients who underwent colonoscopy examination and found two distinct changed patterns of microbiome in CRC patients. The relative abundances of Catabacter and Mogibacterium continuously increased from intramucosal carcinoma to advanced stages, whereas Clostridium, Anaerostipes, Vibrio, Flavonifractor, Holdemanella, and Hungatella were significantly altered only in intermediate lesions. Fecal metabolomics analysis exhibited consistent increases in bile acids, indoles, and urobilin as well as a decrease in heme. Serum metabolomics uncovered the highest levels of bilin, glycerides, and nucleosides together with the lowest levels of bile acids and amino acids in the stage of intermediate lesions. Three fecal and one serum dipeptides were elevated in the intermediate lesions. Proteomics analysis of colorectal tissues showed that oxidation and autophagy through the PI3K/Akt-mTOR signaling pathway contribute to the development of CRC. Diagnostic analysis showed multiomics features have good predictive capability, with AUC greater than 0.85. Our overall findings revealed new candidate biomarkers for CRC, with potentially significant diagnostic and prognostic capabilities.

Cooperative role of AtRsmD and AtRimM proteins in modification and maturation of 16S rRNA in plastids.

Chloroplast pre-ribosomal RNA (rRNA) undergoes maturation, which is critical for ribosome assembly. While the central and auxiliary factors in rRNA maturation have been elucidated in bacteria, their mode of action remains largely unexplored in chloroplasts. We now reveal chloroplast-specific factors involved in 16S rRNA maturation, Arabidopsis thaliana orthologs of bacterial RsmD methyltransferase (AtRsmD) and ribosome maturation factor RimM (AtRimM). A forward genetic screen aimed to find suppressors of the Arabidopsis yellow variegated 2 (var2) mutant defective in photosystem II quality control found a causal nonsense mutation in AtRsmD. The substantially impaired 16S rRNA maturation and translation due to the mutation rescued the leaf variegation phenotype by lowering the levels of chloroplast-encoded proteins, including photosystem II core proteins, in var2. The subsequent co-immunoprecipitation coupled with mass spectrometry analyses and bimolecular fluorescence complementation assay found that AtRsmD interacts with AtRimM. Consistent with their interaction, loss of AtRimM also considerably impairs 16S rRNA maturation with decelerated m2 G915 modification in 16S rRNA catalyzed by AtRsmD. The atrimM mutation also rescued var2 mutant phenotypes, corroborating the functional interplay between AtRsmD and AtRimM towards modification and maturation of 16S rRNA and chloroplast proteostasis. The maturation and post-transcriptional modifications of rRNA are critical to assembling ribosomes responsible for protein translation. Here, we revealed that the cooperative regulation of 16S rRNA m2 G915 modifications by AtRsmD methyltransferase and ribosome assembly factor AtRimM contributes to 16S rRNA maturation, ribosome assembly, and proteostasis in chloroplasts.

New investigation of encoding secondary metabolites gene by genome mining of a marine bacterium, Pseudoalteromonas viridis BBR56.

Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425-CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA-DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors.

Full-length 16S rRNA gene sequencing by PacBio improves taxonomic resolution in human microbiome samples.

BACKGROUND: Sequencing variable regions of the 16S rRNA gene (≃300 bp) with Illumina technology is commonly used to study the composition of human microbiota. Unfortunately, short reads are unable to differentiate between highly similar species. Considering that species from the same genus can be associated with health or disease it is important to identify them at the lowest possible taxonomic rank. Third-generation sequencing platforms such as PacBio SMRT, increase read lengths allowing to sequence the whole gene with the maximum taxonomic resolution. Despite its potential, full length 16S rRNA gene sequencing is not widely used yet. The aim of the current study was to compare the sequencing output and taxonomic annotation performance of the two approaches (Illumina short read sequencing and PacBio long read sequencing of 16S rRNA gene) in different human microbiome samples. DNA from saliva, oral biofilms (subgingival plaque) and faeces of 9 volunteers was isolated. Regions V3-V4 and V1-V9 were amplified and sequenced by Illumina Miseq and by PacBio Sequel II sequencers, respectively. RESULTS: With both platforms, a similar percentage of reads was assigned to the genus level (94.79% and 95.06% respectively) but with PacBio a higher proportion of reads were further assigned to the species level (55.23% vs 74.14%). Regarding overall bacterial composition, samples clustered by niche and not by sequencing platform. In addition, all genera with > 0.1% abundance were detected in both platforms for all types of samples. Although some genera such as Streptococcus tended to be observed at higher frequency in PacBio than in Illumina (20.14% vs 14.12% in saliva, 10.63% vs 6.59% in subgingival plaque biofilm samples) none of the differences were statistically significant when correcting for multiple testing. CONCLUSIONS: The results presented in the current manuscript suggest that samples sequenced using Illumina and PacBio are mostly comparable. Considering that PacBio reads were assigned at the species level with higher accuracy than Illumina, our data support the use of PacBio technology for future microbiome studies, although a higher cost is currently required to obtain an equivalent number of reads per sample.

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota.

BACKGROUND: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models. RESULTS: As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra. CONCLUSIONS: Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

Acute Meningoencephalitis Associated with Borrelia miyamotoi, Minnesota, USA.

Borrelia miyamotoi is an emerging tickborne pathogen that has been associated with central nervous system infections in immunocompromised patients, albeit infrequently. We describe a case-patient in Minnesota, USA, who had meningeal symptoms of 1 month duration. B. miyamotoi infection was diagnosed by Gram staining on cerebrospinal fluid and confirmed by sequencing.

Suppression of UVB-Induced MMP-1 Expression in Human Skin Fibroblasts Using Lysate of Lactobacillus iners Derived from Korean Women's Skin in Their Twenties.

The process of skin aging is intricate, involving intrinsic aging, influenced by internal factors, and extrinsic aging, mainly caused by exposure to UV radiation, resulting in photoaging. Photoaging manifests as skin issues such as wrinkles and discoloration. The skin microbiome, a diverse community of microorganisms on the skin's surface, plays a crucial role in skin protection and can be affected by factors like humidity and pH. Probiotics, beneficial microorganisms, have been investigated for their potential to enhance skin health by regulating the skin microbiome. This can be accomplished through oral probiotics, impacting the gut-skin axis, or topical applications introducing live bacteria to the skin. Probiotics mitigate oxidative stress, suppress inflammation, and maintain the skin's extracellular matrix, ultimately averting skin aging. However, research on probiotics derived from human skin is limited, and there is no established product for preventing photoaging. The mechanism by which probiotics shield the skin microbiome and skin layers from UV radiation remains unclear. Recently, researchers have discovered Lactobacillus in the skin, with reports indicating a decrease in this microorganism with age. In a recent study, scientists isolated Lactobacillus iners KOLBM20 from the skin of individuals in their twenties and confirmed its effectiveness. A comparative analysis of genetic sequences revealed that strain KOLBM20 belongs to the Lactobacillus genus and closely relates to L. iners DSM13335(T) with a 99.20% similarity. Importantly, Lactobacillus iners KOLBM20 displayed anti-wrinkle properties by inhibiting MMP-1. This investigation demonstrated the inhibitory effect of KOLBM20 strain lysate on MMP-1 expression. Moreover, the data suggest that KOLBM20 strain lysate may prevent UVB-induced MMP-1 expression by inhibiting the activation of the ERK, JNK, and p38 signaling pathways induced by UVB. Consequently, KOLBM20 strain lysate holds promise as a potential therapeutic agent for preventing and treating skin photoaging.