Complex Microbiome Research Articles

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial dynamics, facilitates beta-diversity analysis, and enhances reproducibility by transferring learned representations to new data. In simulations, it achieves 90% accuracy in phenotype classification, significantly outperforming existing methods. In real data, TEMPTED identifies vaginal microbial markers linked to term and preterm births, demonstrating robust performance across datasets and sequencing platforms.

Quantitative mapping of pseudouridines in bacteria RNA.

RNA pseudouridylation is one of the most prevalent post-transcriptional modifications, occurring universally across all organisms. Although pseudouridines have been extensively studied in bacterial tRNAs and rRNAs, their presence and role in bacterial mRNA remain poorly characterized. Here, we used a bisulfite-based sequencing approach to provide a comprehensive and quantitative measurement of bacteria pseudouridines. As a proof of concept in E. coli, we identified 1,954 high-confidence sites in 1,331 transcripts, covering almost 30% of the transcriptome. Furthermore, pseudouridine mapping enabled the detection of differentially expressed genes associated with stress response that were unidentified using conventional RNA-seq approach. We also demonstrate that in addition to pseudouridine profiling, our approach can facilitate the discovery of previously unidentified transcripts. As an example, we identified a small RNA transcribed from the antisense strand of tRNA-Tyr which represses expression of distal genes. Finally, we mapped pseudouridines in oral microbiome samples of human subjects, demonstrating the broad applicability of our approach in complex microbiomes. Altogether, our work highlights the advantages of mapping bacterial pseudouridines and provides a tool to study posttranscription regulation in microbial communities.

Monitoring Fish Bacterial Pathogens of Wild Fish Species From the South China Sea by Applying Next-Generation Sequencing on Gill Tissue.

The classic epidemiological triangle model of host-environment-pathogen is recently being reshaped into a tetrahedron, with the growing understanding of the importance of the microbiome in this array. The gills, being a gateway into the fish body, bearing an important role in fish homeostasis, host a complex microbiome that reflects the ambient water, while also showing resemblance to gut microbiome. Next-generation sequencing (NGS) and improvements in data analysis tools enable researchers to gather and analyse a lot more data than ever before, take a closer, more detailed look at microbiota, and gain a much better understanding of the biological processes at work in these complex relations. Here, 16S rRNA amplicons of bacterial DNA extracted from the gills of 36 asymptomatic specimens of three wild fish species from the South China Sea (Nemipterus japonicus, Alepes djebaba, and Saurida tumbil) were sequenced using NGS. Data analyses revealed the presence of 20 potentially pathogenic species, including several zoonotic agents. Gill microbiota exhibited host species-specificity, and expressed a significant difference between demersal and pelagic-amphidromous fish. It is suggested that this method be more widely implemented, in order to gain more insight on ocean ecosystems' health status, as well as fish stocks of commercial importance.

The microbiome and metatranscriptome of a panel from the Sarracenia mapping population reveal complex assembly and function involving host influence.

Sarracenia provide an optimal system for deciphering the host-microbiome interactions at various levels. We analyzed the pitcher microbiomes and metatranscriptomes of the parental species, and F1 and F2 generations from the mapping population (Sarracenia purpurea X Sarracenia psittacina) utilizing high-throughput sequencing methods. This study aimed to examine the host influences on the microbiome structure and function and to identify the key microbiome traits. Our quality datasets included 8,892,553 full-length bacterial 16s rRNA gene sequences and 65,578 assembled metatranscripts with microbial protein annotations. The correlation network of the bacterial microbiome revealed the presence of 3-7 distinct community clusters, with 8 hub and 19 connector genera. The entire microbiome consisted of viruses, bacterial, archaea, and fungi. The richness and diversity of the microbiome varied among the parental species and offspring genotypes despite being under the same greenhouse environmental conditions. We have discovered certain microbial taxa that are genotype-enriched, including the community hub and connector genera. Nevertheless, there were no significant differences observed in the functional enrichment analysis of the metatranscriptomes across the different genotypes, suggesting a functional convergence of the microbiome. We found that the pitcher microcosm harbors both rhizosphere and phyllosphere microbiomes within its boundaries, resulting in a structurally diverse and functionally complex microbiome community. A total of 50,424 microbial metatranscripts were linked to plant growth-promoting microbial proteins. We show that this complex pitcher microbiome possesses various functions that contribute to plant growth promotion, such as biofertilization, bioremediation, phytohormone signaling, stress regulation, and immune response stimulation. Additionally, the pitcher microbiome exhibits traits related to microbe-microbe interactions, such as colonization of plant systems, biofilm formation, and microbial competitive exclusion. In summary, the demonstrated taxonomical divergence and functionally convergence of the pitcher microbiome are impacted by the host genetics, making it an excellent system for discovering novel beneficial microbiome traits.

Microbes as medicine.

Over the last two decades, advancements in sequencing technologies have significantly deepened our understanding of the human microbiome's complexity, leading to increased concerns about the detrimental effects of antibiotics on these intricate microbial ecosystems. Concurrently, the rise in antimicrobial resistance has intensified the focus on how beneficial microbes can be harnessed to treat diseases and improve health and offer potentially promising alternatives to traditional antibiotic treatments. Here, we provide a comprehensive overview of both established and emerging microbe-centric therapies, from probiotics to advanced microbial ecosystem therapeutics, examine the sophisticated ways in which microbes are used medicinally, and consider their impacts on microbiome homeostasis and health outcomes through a microbial ecology lens. In addition, we explore the concept of rewilding the human microbiome by reintroducing "missing microbes" from nonindustrialized societies and personalizing microbiome modulation to fit individual microbial profiles-highlighting several promising directions for future research. Ultimately, the advancements in sequencing technologies combined with innovative microbial therapies and personalized approaches herald a new era in medicine poised to address antibiotic resistance and improve health outcomes through targeted microbiome management.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Diversity and characteristics of the oral microbiome influenced by race and ethnicity.

Periodontitis disproportionately affects racial/ethnic populations. Besides social determinants contributing to disparities in periodontal health, variations of oral microbial communities may also be a key factor influencing oral immune responses. To characterize the oral microbiome from different racial/ethnic populations, we collected 161 dental plaque samples from African Americans (AAs), Caucasian Americans (CAs), and Hispanic Americans (HAs) with clinical gingival health or biofilm-induced gingivitis on an intact periodontium. Using metagenomic sequencing, we found significant difference in diversity and abundance of microbial taxa in the dental plaque samples from AA, CA, and HA groups and unique microbial species that can only be detected in a particular racial/ethnic group. Moreover, we revealed racial/ethnic associated variations in functional potential of the oral microbiome, showing that diversity and abundance of antibiotic resistant genes were greater in the oral microbiome of the AAs than those in CAs or HAs, and that the AAs exhibited higher levels of genes involving in modification of glycoconjugates, oligo- and polysaccharides. These findings indicate more complex and higher virulence potential oral microbiome in AA and HA populations, which likely contributes to higher prevalence of periodontitis in AAs and HAs.

Metabolic interactions shape emergent biofilm structures in a conceptual model of gut mucosal bacterial communities

The gut microbiome plays a major role in human health; however, little is known about the structural arrangement of microbes and factors governing their distribution. In this work, we present an in silico agent-based model (ABM) to conceptually simulate the dynamics of gut mucosal bacterial communities. We explored how various types of metabolic interactions, including competition, neutralism, commensalism, and mutualism, affect community structure, through nutrient consumption and metabolite exchange. Results showed that, across scenarios with different initial species abundances, cross-feeding promotes species coexistence. Morphologically, competition and neutralism resulted in segregation, while mutualism and commensalism fostered high intermixing. In addition, cooperative relations resulted in community properties with little sensitivity to the selective uptake of metabolites produced by the host. Moreover, metabolic interactions strongly influenced colonization success following the invasion of newcomer species. These results provide important insights into the utility of ABM in deciphering complex microbiome patterns.

Insights into the culturable fungal endophytes of Dioscorea bulbifera L. in terms of their diversity, antidiabetic and antioxidant activity

The research on endophytes is focused on understanding the complex microbiome compositions, their interactions with host plants and their bioactive potential. Our study provided an overview of the diversity, distribution and bioactivities of culturable endophytic fungi associated with fungal endophytes of Dioscorea bulbifera L. The phylogenetic analysis depicted the evolutionary relationship among taxa of endophytic fungal isolates. The isolated fungal endophytes belonged to twenty-five genera and thirty-eight species. Further, diversity indices demonstrated significant diversity of fungal endophytes in the different tissues: stem, leaf, tuber and bulbil. A higher consistency of endophytic fungal isolates in the bulbils was found using Shannon index. Furthermore, Simpson's index revealed that the leaf tissue harboured highly diverse fungal endophytes. Likewise, Margalef's index depicted high taxonomic richness in bulbils. The isolates such as Acrocalymma medicaginis, Curvularia lycoperscii, Talaromyces macrospora, Fusarium laceratum, Paecilomyces formosus and Microascus cirrosus isolated in this study have been reported as endophytes for the first time from any plant. In-vitro antioxidant and antidiabetic activities of the ethyl acetate extracts revealed that Nigrospora oryzae (Z2) effectively inhibited α-glucosidase activity with IC50 value of 0.6 µg/ml whereas IC50 value of the acarbose, the positive control was reported to be 0.040 µg/ml. The results of antioxidant activity demonstrated that H15A (Acrocalymma medicaginis) and BD5 (Phomopsis longicolla) could be used as potential sources of antioxidant agents. Moreover, H15A (Acrocalymma medicaginis) was reported to produce 687.1 ± 0.17 µg GAE/mg of TPC and 78.55 ± 0.29 µg QE/mg of TFC quantitatively. These findings suggest that the potential endophytes could be explored using systematic bio-guided investigations to further discover novel natural products. Our study provides an important resource for a deeper understanding of endophyte-plant interactions at molecular and genetic levels.

Gut Microbiota Diversity of Local Egyptian Cattle Managed in Different Ecosystems.

The animal gastrointestinal tract contains a complex microbiome whose composition ultimately reflects the co-evolution of microorganisms with their animal host and their host's environment. This study aimed to gain insights into the adaptation of the microbiota of local Egyptian cattle to three different ecosystems (Upper Egypt, Middle Egypt, and Lower Egypt) distributed across 11 governorates (with an average of 12 animals per governorate) using amplicon sequencing. We analyzed the microbiota from 136 fecal samples of local Egyptian cattle through a 16S rRNA gene sequencing approach to better understand the fecal microbial diversity of this breed which developed under different ecosystems. An alpha diversity analysis showed that the fecal microbiota of the Egyptian cattle was not significantly diverse across areas, seasons, sexes, or farm types. Meanwhile, microbiota data revealed significant differences in richness among age groups (p = 0.0018). The microbial community differed significantly in the distribution of its relative abundance rather than in richness across different ecosystems. The taxonomic analysis of the reads identified Firmicutes and Actinobacteriota as the dominant phyla, accounting for over 93% of the total bacterial community in Egyptian cattle. Middle Egypt exhibited a different microbial community composition compared to Upper and Lower Egypt, with a significantly higher abundance of Firmicutes and Euryarchaeota and a lower abundance of Actinobacteriota in this region than the other two ecosystems. Additionally, Middle Egypt had a significantly higher relative abundance of the Methanobacteriaceae family and the Methanobrevibacter genera than Lower and Upper Egypt. These results suggest a difference in the adaptation of the fecal microbial communities of Egyptian cattle raised in Middle Egypt. At the genus level, eleven genera were significantly different among the three ecosystems including Bacillus, DNF00809, Kandleria, Lachnospiraceae_NK3A20_group, Methanobrevibacter, Mogibacterium, Olsenella, Paeniclostridium, Romboutsia, Turicibacter, and UCG-005. These significant differences in microbiota composition may impact the animal's adaptation to varied environments.

The affinity towards the hydrophobic region of biomimicking bacterial membranes drives the antimicrobial activity of EFV12 peptide from Lactobacillus gasseri gut microbiota

The gut microbiota consists of a large variety of microorganisms, which interact with the immune system and exert essential roles for the human body health. Many of these microorganisms are also capable of producing various bioactive molecules, such as selective antimicrobial peptides, thus promoting the proliferation of only certain bacterial strains. These result in the shaping of the composition of the local microbiome and the co-evolution with a complex microbiome. Recently, a small peptide, named EFV12 and deriving from the bacterium Lactobacillus gasseri SF1109 regularly placed in the human intestine, showed a significant antimicrobial activity. Here we discuss a biophysical study on the structural changes induced by the peptide on lipid bilayers mimicking bacterial membranes with the aim of shedding light on the molecular features driving the biocidal activity against Gram(+) and Gram(−) strains. Supported Lipid Bilayers and liposomes composed of 1,2-oleoyl-sn-glycero-3-phosphocholine and 1,2-oleoyl-sn-glycero-3-rac-phosphoglycerol, both in the absence and presence of cardiolipin and lipopolysaccharides (LPSs), were selected to investigate the peptide-lipid interactions through a combination of specular Neutron Reflectometry, Dynamic Light Scattering, Small-Angle X-ray Scattering and Circular Dichroism measurements. The obtained results indicated association of EFV12 peptide with the hydrophobic region of lipid bilayers, which caused their destabilization, and is thus driving the antimicrobial activity against bacterial cells.

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Toward Multilabel Classification for Multiple Disease Prediction Using Gut Microbiota Profiles.

Advancements in high-throughput technologies have yielded large-scale human gut microbiota profiles, sparking considerable interest in exploring the relationship between the gut microbiome and complex human diseases. Through extracting and integrating knowledge from complex microbiome data, existing machine learning (ML)-based studies have demonstrated their effectiveness in the precise identification of high-risk individuals. However, these approaches struggle to address the heterogeneity and sparsity of microbial features and explore the intrinsic relatedness among human diseases. In this work, we reframe human gut microbiome-based disease detection as a multilabel classification (MLC) problem and integrate a range of innovative techniques within the proposed MLC framework, aptly named GutMLC. Specifically, the entity semantic similarity as priori knowledge is incorporated into multilabel feature selection and loss functions by capturing the shared attributes and inherent associations among diseases and microbes. To tackle the issue of label imbalance, both within and between labels, we adapt the focal loss (FL) function for MLC using debiased inverse weighting. Extensive experiment results consistently demonstrate the competitive performance of GutMLC in comparison with commonly used MLC and single-label classification (SLC) algorithms. This work seeks to unlock the potential of gut microbiota as robust biomarkers for multiple disease prediction.

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

A microbiome-biochar composite synergistically eliminates the environmental risks of antibiotic mixtures and their toxic byproducts

This study developed a continuous reactor system employing a hybrid hydrogel composite synthesized using a complex sludge microbiome and an adsorbent (HSA). This HSA-based system effectively eliminated the environmental risks associated with a mixture of the antibiotics ciprofloxacin and sulfamethoxazole, which exhibited higher toxicity in combination than individually at environmentally relevant levels. Analytical chemistry experiments revealed the in-situ generation of various byproducts (BPs) within the bioreactor system, with two of these BPs recording toxicity levels that surpassed those of their parent compound. The HSA approach successfully prevented the functional microbiome from being washed out of the reactor, while HSA efficiently removed antibiotic residues in their original and BP forms through synergistic adsorptive and biotransformation mechanisms, ultimately reducing the overall ecotoxicity. The use of HSA thus demonstrates promise not only as a mean to reduce the threat posed by toxic antibiotic residues to aquatic ecosystems but also as a practical solution to operational challenges, such as biomass loss/washout, that are frequently encountered in various environmental bioprocesses.

Fabricated devices for performing bacterial-fungal interaction experiments across scales.

Diverse and complex microbiomes are found in virtually every environment on Earth. Bacteria and fungi often co-dominate environmental microbiomes, and there is growing recognition that bacterial-fungal interactions (BFI) have significant impacts on the functioning of their associated microbiomes, environments, and hosts. Investigating BFI in vitro remains a challenge, particularly when attempting to examine interactions at multiple scales of system complexity. Fabricated devices can provide control over both biotic composition and abiotic factors within an experiment to enable the characterization of diverse BFI phenotypes such as modulation of growth rate, production of biomolecules, and alterations to physical movements. Engineered devices ranging from microfluidic chips to simulated rhizosphere systems have been and will continue to be invaluable to BFI research, and it is anticipated that such devices will continue to be developed for diverse applications in the field. This will allow researchers to address specific questions regarding the nature of BFI and how they impact larger microbiome and environmental processes such as biogeochemical cycles, plant productivity, and overall ecosystem resilience. Devices that are currently used for experimental investigations of bacteria, fungi, and BFI are discussed herein along with some of the associated challenges and several recommendations for future device design and applications.

Mobility and growth in confined spaces are important mechanisms for the establishment of Bacillus subtilis in the rhizosphere.

The rhizosphere hosts complex and abundant microbiomes whose structure and composition are now well described by metagenomic studies. However, the dynamic mechanisms that enable micro-organisms to establish along a growing plant root are poorly characterized. Here, we studied how a motile bacterium utilizes the microhabitats created by soil pore space to establish in the proximity of plant roots. We have established a model system consisting of Bacillus subtilis and lettuce seedlings co-inoculated in transparent soil microcosms. We carried out live imaging experiments and developed image analysis pipelines to quantify the abundance of the bacterium as a function of time and position in the pore space. Results showed that the establishment of the bacterium in the rhizosphere follows a precise sequence of events where small islands of mobile bacteria were first seen forming near the root tip within the first 12-24 h of inoculation. Biofilm was then seen forming on the root epidermis at distances of about 700-1000 µm from the tip. Bacteria accumulated predominantly in confined pore spaces within 200 µm from the root or the surface of a particle. Using probabilistic models, we could map the complete sequence of events and propose a conceptual model of bacterial establishment in the pore space. This study therefore advances our understanding of the respective role of growth and mobility in the efficient colonization of bacteria in the rhizosphere.

Microbiome mapping in dairy industry reveals new species and genes for probiotic and bioprotective activities

The resident microbiome in food industries may impact on food quality and safety. In particular, microbes residing on surfaces in dairy industries may actively participate in cheese fermentation and ripening and contribute to the typical flavor and texture. In this work, we carried out an extensive microbiome mapping in 73 cheese-making industries producing different types of cheeses (fresh, medium and long ripened) and located in 4 European countries. We sequenced and analyzed metagenomes from cheese samples, raw materials and environmental swabs collected from both food contact and non-food contact surfaces, as well as operators’ hands and aprons. Dairy plants were shown to harbor a very complex microbiome, characterized by high prevalence of genes potentially involved in flavor development, probiotic activities, and resistance to gastro-intestinal transit, suggesting that these microbes may potentially be transferred to the human gut microbiome. More than 6100 high-quality Metagenome Assembled Genomes (MAGs) were reconstructed, including MAGs from several Lactic Acid Bacteria species and putative new species. Although microbial pathogens were not prevalent, we found several MAGs harboring genes related to antibiotic resistance, highlighting that dairy industry surfaces represent a potential hotspot for antimicrobial resistance (AR) spreading along the food chain. Finally, we identified facility-specific strains that can represent clear microbial signatures of different cheesemaking facilities, suggesting an interesting potential of microbiome tracking for the traceability of cheese origin.

Cosmeceuticals: A Review of Clinical Studies Claiming to Contain Specific, Well-Characterized Strains of Probiotics or Postbiotics.

The skin serves as a critical barrier against external threats-dehydration, ultraviolet exposure, and infections-playing a significant role in internal homeostasis and moisture retention. Additionally, and equally importantly, it interacts dynamically with the complex microbiome resident in it, which is essential for maintaining skin health. Recent interest has focused on the use of probiotics and postbiotics, besides their ability to modulate the skin microbiome, to enhance barrier function, and exhibit anti-inflammatory properties, to be involved in skincare, by having the potential to improve skin hydration, elasticity, and overall appearance, as well as in reducing signs of aging, such as wrinkles and fine lines. The products-being a combination of a cosmetic regime plus probiotic[s] or postbiotic[s]-are named cosmeceuticals. However, to comply with the regulations for the characterization of a microorganism as a specific probiotic strain, the pro- or postbiotics incorporated into the cosmetic regime should be both genetically and phenotypically defined. Thus, in this review, we present 14 published clinical trials using such cosmetic products with specific, well-characterized strains of probiotics or postbiotics applied to volunteers with healthy skin. Looking at the results of these studies collectively, we can say that these genetically and phenotypically defined strains of either live or inanimate bacteria and/or their components seem to keep the treated skin at least fully hydrated, with intact epithelial tone, increased radiance, and with decreased wrinkle depth, while normalizing the commensal skin microbiota. Future advancements in personalized skin care may lead to genomic sequencing and metabolomics to tailor probiotic and postbiotic treatments to individual skin microbiomes, promising a new frontier in cosmeceuticals.

Insights into nitrogen biogeochemical cycling in mangrove wetland from Genome-Resolved metagenomic sequencing

The critical role of nitrogen (N) in sustaining the vitality of mangrove wetlands is widely recognized, yet research elucidating complex microbiome structures and functional genes driving N biogeochemical cycles are markedly limited. This study leverages high-throughput and metagenomic sequencing to investigate N biogeochemical cycling from the Dongzhai Harbor mangrove wetland. Results reveal that Proteobacteria (29.95–49.26%), Chloroflexi (15.68–35.33%), and Acidobacteria (5.18–12.46%) are the predominant phyla, with Deltaproteobacteria (3.71–7.95%), Gammaproteobacteria (0.83–8.73%), and Alphaproteobacteria (1.73–14.13%) leading at the order level. Microbial diversity and the prevalence of functional genes diminish with increasing sediment depth, indicating that N transformation predominantly occurs in surface sediments. The identified N metabolic pathways in mangrove sediments encompass N fixation (nifD, nifK, nifH genes), nitrification (amoA, amoB, amoC, hao, nxrA and nxrB genes), denitrification (narG, nirK, nirS, norB and nosZ genes), dissimilatory nitrate reduction to ammonium (hzo、hzsA and hzsB genes), and organic N mineralization (gdhA and ureC genes). Organic N mineralization and N fixation are most pronounced in mangrove sediments, with N fixation exhibiting greater intensity in the mangrove than mudflat zone. Salinity, TOC, TN, and SO42- predominantly foster N cycling facilitated by microbial activities, while Fe appears to impede it. These insights offer a scientific foundation for the ecological restoration and conservation of mangrove wetlands.