Inflammatory bowel diseases (IBD), including ulcerative colitis and Crohn's disease, arise from various factors such as dietary, genetic, immunological, and microbiological influences. The gut microbiota plays a crucial role in the development and treatment of IBD, though the exact mechanisms remain uncertain. Current research has yet to definitively establish the beneficial effects of the microbiome on IBD. Bacteria and viruses (both prokaryotic and eukaryotic) are key components of the microbiome uniquely related to IBD. Numerous studies suggest that dysbiosis of the microbiota, including bacteria, viruses, and bacteriophages, contributes to IBD pathogenesis. Conversely, some research indicates that bacteria and bacteriophages may positively impact IBD outcomes. Additionally, plant metabolites play a crucial role in alleviating IBD due to their anti-inflammatory and microbiome-modulating properties. This systematic review discusses the role of the microbiome in IBD pathogenesis and evaluates the potential connection between plant metabolites and the microbiome in the context of IBD pathophysiology.

The gut is exposed to a wide range of proteins, including ingested proteins and those produced by the resident microbiota. While ingested prion-like proteins can propagate across species, their implications for disease development remain largely unknown. Here, we apply a multidisciplinary approach to examine the relationship between the biophysical properties of exogenous prion-like proteins and the phenotypic consequences of ingesting them. Through computational analysis of gut bacterial proteins, we identified an enrichment of prion-like sequences in Helicobacter pylori. Based on these findings, we rationally designed a set of synthetic prion-like sequences that form amyloid fibrils, interfere with amyloid-beta-peptide aggregation, and trigger prion propagation when introduced in the yeast Sup35 model. When C. elegans were fed bacteria expressing these prion-like proteins, they lost associative memory and exhibited increased lipid oxidation. These data suggest a link between memory impairment, the conformational state of aggregates, and oxidative stress. Overall, this work supports gut microbiota as a reservoir of exogenous prion-like sequences, especially H. pylori, and the gut as an entry point for molecules capable of triggering cognitive dysfunction.

The urobiome, or urinary tract microbiome, has emerged as a crucial component in maintaining urinary health and defending against infections. Recent advances in next-generation sequencing (NGS) have debunked the long-held belief that the urinary tract is sterile, revealing a unique ecosystem of microorganisms. The urobiome interacts with the urothelium and mucosa-associated lymphoid tissue (MALT) to support local immunity, playing an integral role in defending the urinary tract against pathogens. Through complex communication processes like quorum sensing, the urobiome regulates microbial behavior and controls interactions with host tissues, helping to prevent pathogen colonization and infection. However, dysbiosis in the urobiome can disrupt this balance, making the urinary tract more susceptible to infections, including urinary tract infections (UTIs). Studies have highlighted specific microbial compositions associated with both healthy and disease states, suggesting that shifts in the urobiome may correlate with various urological diseases. Furthermore, microbial diversity within the urinary tract differs by factors such as age and gender, reflecting the dynamic nature of the urobiome. Future research focusing on the interplay between the urobiome, host immune defenses, and pathogenic mechanisms may lead to innovative diagnostic and therapeutic approaches. Understanding how microbial composition changes during disease states could enable targeted treatments, potentially reducing reliance on antibiotics and minimizing resistance issues. The urobiome thus represents a promising frontier in urology, with implications for enhancing urinary health and treating infections more effectively.

Microscopic colitis (MC) is a chronic inflammatory disease of the large intestine that primarily affects older adults and presents with chronic diarrhea. The etiology is unknown and there are currently no FDA approved medications or biomarkers for treatment or monitoring of the disease. Emerging evidence have implicated the gut microbiome and metabolome disturbances in MC pathogenesis. We conduct a comprehensive analysis of gut microbial and metabolic changes in a cohort of 683 participants, including 131 patients with active MC, 159 with chronic diarrhea, and 393 age- and sex-matched controls without diarrhea. Stool microbiome and metabolome are profiled using whole-genome shotgun metagenomic sequencing and ultra-high performance liquid chromatography-mass spectrometry, respectively. Compared to controls, eight microbial species including pro-inflammatory oral-typical Veillonella dispar and Haemophilus parainfluenzae, and 11 species, including anti-inflammatory Blautia glucerasea and Bacteroides stercoris are enriched and depleted in MC, respectively. Pro-inflammatory metabolites, including lactosylceramides, ceramides, lysophospholipids, and lysoplasmalogens, are enriched in active MC. Multi-omics analyses reveal robust associations between microbial species, metabolic pathways, and metabolites, suggesting concordant disruptions in MC. Here, we show distinct shifts in gut microbiome and metabolome in MC that can inform the development of non-invasive biomarkers and novel therapeutics.

The increasing number and volume of genomic and metagenomic data necessitates scalable and robust computational models for precise analysis. Sketching techniques utilizing k -mers from a biological sample have proven to be useful for large-scale analyses. In recent years, FracMinHash has emerged as a popular sketching technique and has been used in several useful applications. Recent studies on FracMinHash proved unbiased estimators for the containment and Jaccard indices. However, theoretical investigations for other metrics are still lacking.

In this paper, we present a theoretical framework for estimating similarity/distance metrics by using FracMinHash sketches, when the metric is expressible in a certain form. We establish conditions under which such an estimation is sound and recommend a minimum scale factor s for accurate results. Experimental evidence supports our theoretical findings.

We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. frac-kmc is also the first parallel tool for this task, allowing for speeding up sketch generation using multiple CPU cores - an option lacking in existing serialized tools. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.

Hepatic encephalopathy (HE) is a frequent decompensation in patients with cirrhosis, which significantly affects morbidity and mortality. Ammonia is a major neurotoxin implicated in the pathogenesis, progression, and severity of HE, and various organs including the gut, muscle, kidney, and brain are involved in its metabolism. Therefore, therapeutic management involves reducing ammonia production and increasing its elimination from the blood and the brain. Prevention of HE in patients at high risk of first and recurrent episodes is important for prolonging survival. Various anti-ammonia therapies with synergistic and complementary actions have been attempted for overt HE and for prophylaxis of the first and recurrent episodes of HE. In the current review, we summarize the currently used and under-development pharmacotherapies/procedure(s) for HE in cirrhosis and their mechanism of action. Primary and secondary prophylaxis with monotherapies and combination therapies are also discussed.

The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.

The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.

Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles.

This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Menopause is characterized by the cessation of menstruation and a decline in reproductive function, which is an intrinsic component of the aging process. However, it has been a frequently overlooked field of women's health. The oral and gut microbiota, constituting the largest ecosystem within the human body, are important for maintaining human health and notably contribute to the healthy aging of menopausal women. Therefore, a comprehensive review elucidating the impact of the gut and oral microbiota on menopause for healthy aging is of paramount importance. This paper presents the current understanding of the microbiome during menopause, with a particular focus on alterations in the oral and gut microbiota. Our study elucidates the complex interplay between the microbiome and sex hormone levels, explores microbial crosstalk dynamics, and investigates the associations between the microbiome and diseases linked to menopause. Additionally, this review explores the potential of microbiome-targeting therapies for managing menopause-related diseases. Given that menopause can last for approximately 30 years, gaining insights into how the microbiome and menopause interact could pave the way for innovative interventions, which may result in symptomatic relief from menopause and an increase in quality of life in women.

Infertility, adverse pregnancy outcomes and genital infections are global concerns. The reproductive tract microbiome appears to play a crucial role in the physiology of both the female and male reproductive tracts. Despite the presence of thousands of microbes in body fluids shared during unprotected sexual intercourse, they have traditionally been studied separately, with greater emphasis on the female (mostly vaginal) microbiome, and the interaction between these microbiomes in a sexually active couple has been overlooked. This review introduces the concept of the 'seminovaginal microbiome' - the collective microbiota of both partners, transferred and shared during sexual interaction. By synthesizing the existing body of next-generation sequencing-based literature, this review establishes the first holistic view of how these microbiomes interact, influence reproductive health and affect assisted reproductive technique outcomes, as well as the occurrence of microbe-associated diseases such as sexually transmitted infections, prostatitis, bacterial vaginosis and candidiasis. Additionally, the microbial interplay in homosexual couples and transgender individuals is discussed.

Understanding the temporal variability of the microbiome is critical for translating associations of the microbiome with health and disease into clinical practice. The aim of this study is to assess the extent of temporal variability of the human urinary microbiota. A pair of urine samples were collected from study participants at 3-40-month interval. DNA was extracted and the bacterial V4 hypervariable region of the 16S rRNA gene was sequenced on the Illumina MiSeq platform. The alpha diversity of paired samples was analyzed using Chao1 and Shannon indices and PERMANOVA was used to test the factors influencing beta diversity.

A total of 63 participants (43 men and 20 women with a mean age of 63.0 and 57.1 years, respectively) were included in the final analysis. An average of 152 ± 128 bacterial operational taxonomic units (OTUs) were identified in each urine sample from the entire cohort. There was an average of 41 ± 32 overlapping OTUs in each sample pair, accounting for 66.3 ± 29.4% of the relative abundance. There was a clear correlation between the number of overlapping OTUs and the relative abundance covered. The difference in Chao1 index between paired samples was statistically significant; the difference in Shannon index was not. Beta diversity did not differ significantly within the paired samples. Neither age nor sex of the participants influenced the variation in community composition. With a longer interval between the collections, the relative abundance covered by the overlapping OTUs changed significantly but not the number of OTUs.

Our findings demonstrated that, while the relative abundance of dominant bacteria varied, repeated collections generally shared more than 60% of the bacterial community. Furthermore, we observed little variation in the alpha and beta diversity of the microbial community in human urine. These results help to understand the dynamics of human urinary microbiota and enable interpretation of future studies.

As per the recent research findings, there is a significant difference between the bacteriome of normal tissue (NT) and tumor tissues (TT) of oral squamous cell carcinoma (OSCC). Identifying this distinct bacteriome is crucial for understanding their potential contribution to oral carcinogenesis. This systematic review (SR) aims to identify exclusive and relative bacterial abundance and bacterial diversity in TT and NT.

The review was conducted following the PRISMA guidelines. PUBMED and SCOPUS databases were searched for studies in English published till 31st August 2024. The inclusion criteria focused on identifying bacteriome in NT versus TT at either species,/genus, and/or phylum level through 16 s ribosomal RNA sequencing. Quality assessment was performed using an eleven-parameter tool combining the Newcastle-Ottawa Scale and customized criteria.

Evaluating the selected 13 articles, we have identified the exclusive and relative abundance of bacteriome in TT and NT at phylum, genus, and species levels. Three phyla such as Chloroflexota, Deinococcus-Thermus, and Mycoplasmatota, are found exclusively in TT. Seven genus such as Eubacterium, Campylobacter, Aeromonas, Oceanivigra, Rheinheimera, Weissella, and Catonella are exclusively found in TT. Ten species such as Micrococcus luteus, Prevotella melaninogenica, Exiguobacterium oxidotolerans, Fusobacterium naviforme, Staphylococcus aureus, Veillonella parvula, Parvimonas sp oral taxon 110, Eubacterium II G1 infirmum, Eubacterium XI G3 Brachy, Weissella viridescens are found in TT. Six genus such as Capnocytophaga, Selenomonas, Leptothrix, Desulfovibrio, Desulfoplanes, Pelospora are found exclusively in NT. Eleven species, such as Streptococcus sp. Oral taxon 071,Selenomonas sputigena, Treponema pedis, Acholeplasmatales bacterium, Capnocytophaga haemolytica, Eubacterium sp., Syntrophomonadaceae genomosp.,Treponema putidum, Mitsuokella sp., Actinomyces sp. Oral taxon 848 str. F0332, p- 2534 - 18B5-gut-group are found in NT. Seven common genera within which different species are identified in TT and NT, suggesting differences in bacterial behaviour and characteristics within the same genus. A total of 12 phyla, 35 genera, and 54 species were found to be relatively more abundant in TT compared to NT. Conversely, 7 phyla, 32 genera, and 45 species were relatively more abundant in NT than in TT. Considerable variations in diversity metrics were found between TT and NT.

This systematic review is the first to identify a distinct bacteriome exclusive to OSCC tumour tissue compared to normal tissue using 16S ribosomal RNA sequencing. This pioneering work lays the foundation for future studies on the oral microbiome as a potential diagnostic or therapeutic target in oral cancer management. It emphasizes the importance of exploring species-level differences for a deeper understanding of their roles in OSCC.

Not applicable.

Tuberculosis, primarily caused by Mycobacterium tuberculosis, is an airborne lung disease and continues to pose a significant global health threat, resulting in millions of deaths annually. The current treatment for tuberculosis involves a prolonged regimen of antibiotics, which leads to complications such as recurrence, drug resistance, reinfection, and a range of side effects. This scenario underscores the urgent need for novel therapeutic strategies to combat this lethal pathogen. Over the last two decades, microbiome therapeutics have emerged as promising next-generation drug candidates, offering advantages over traditional medications. In 2022, the Food and Drug Administration approved the first microbiome therapeutic for recurrent Clostridium infections, and extensive research is underway on microbiome treatments for various challenging diseases, including metabolic disorders and cancer. Research on microbiomes concerning tuberculosis commenced roughly a decade ago, and the scope of this research has broadened considerably over the last five years, with microbiome therapeutics now viewed as viable options for managing drug-resistant tuberculosis. Nevertheless, the understanding of their mechanisms is still in its infancy. Although autophagy has been extensively studied in other diseases, research into its role in tuberculosis is just beginning, with preliminary developments in progress. Against this backdrop, this comprehensive review begins by succinctly outlining tuberculosis' characteristics and assessing existing treatments' strengths and weaknesses, followed by a detailed examination of microbiome-based therapeutic approaches for drug-resistant tuberculosis. Additionally, this review focuses on establishing a basic understanding of microbiome treatments for tuberculosis, mainly through the lens of autophagy as a mechanism of action. Ultimately, this review aims to contribute to the foundational comprehension of microbiome-based therapies for tuberculosis, thereby setting the stage for the further advancement of microbiome therapeutics for drug-resistant tuberculosis.

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

The microbiota of the female genital tract is crucial for reproductive health. This study aims to investigate the impact of the lower genital tract microbiota on in vitro fertilization and frozen embryo transfer (IVF-FET) outcomes. This study included 131 women aged 20-35 years who underwent their first or second IVF-FET cycle with no obvious unfavorable factors for implantation. Cervical microbiota samples were collected on the embryo transfer day and analyzed using 16S rDNA full-length sequencing. Clinical outcomes were followed up for analysis. Clinical pregnancy (CP) was achieved in 84 patients, and 47 patients experienced non-pregnancy (NP). The cervical microbiota diversity between NP and CP groups showed no significant differences, but some genera such as Halomonas (P = 0.018), Klebsiella (P = 0.039), Atopobium (P = 0.016), and Ligilactobacillus (P = 0.021) were obviously different between the two groups. Notably, there was no significant difference in the abundance of Lactobacillus between the two groups. A nomogram prediction model was developed using the random forest algorithm and logistic regression, including the classification of Halomonas, Atopobium, and Veillonella, as well as the relative abundance of Lactobacillus, to identify high-risk patients with embryo implantation failure. Both internal (area under the curve [AUC] = 0.718, 95% confidence interval [CI]: 0.628-0.807, P < 0.001) and external validation (AUC = 0.654, 95% CI: 0.553-0.755, P = 0.037) of the model performed well. In conclusion, this study established a correlation between cervical microbiota and embryo implantation failure in infertile women undergoing IVF-FET and developed a prediction model that could help in early identification of patients at high risk of implantation failure.IMPORTANCEThis study investigated the potential role of abnormal cervical microbiota in the pathology of implantation failure after in vitro fertilization and frozen embryo transfer (IVF-FET) treatment. Despite nearly half a century of advancements in assisted reproductive technology (ART), the implantation rate of high-quality embryos still hovered around 50%. Moreover, unexplained recurrent implantation failure (RIF) remains a significant challenge in ART. To our knowledge, we first discovered a prediction model for embryo implantation failure, identifying Halomonas and Veillonella as significantly adverse factors for embryo implantation. Despite some limitations, the internal and external validation of the model could bode well for its clinical application prospect. The insights gained from this study pave the way for intervention in the genital tract microbiota prior to IVF-FET, particularly in patients with RIF and RSA.

Inferring microbial interaction networks from microbiome data is a core task of computational ecology. An avenue of research to create reliable inference methods is based on a stylized view of microbiome data, starting from the assumption that the presences and absences of microbiomes, rather than the quantitative abundances, are informative about the underlying interaction network. With this starting point, inference algorithms can be based on the notion of attractors (asymptotic states) in Boolean networks. Boolean network framework offers a computationally efficient method to tackle this problem. However, often existing algorithms operating under a Boolean network assumption, fail to provide networks that can reproduce the complete set of initial attractors (abundance patterns). Therefore, there is a need for network inference algorithms capable of reproducing the initial stable states of the system.

We study the change of attractors in Boolean threshold dynamics on signed undirected graphs under small changes in network architecture and show, how to leverage these relationships to enhance network inference algorithms. As an illustration of this algorithmic approach, we analyse microbial abundance patterns from stool samples of humans with inflammatory bowel disease (IBD), with colorectal cancer and from healthy individuals to study differences between the interaction networks of the three conditions. The method reveals strong diversity in IBD interaction networks. The networks are first partially deduced by an earlier inference method called ESABO, then we apply the new algorithm developed here, EDAME, to this result to generate a network that comes nearest to satisfying the original attractors.

Implementation code is freely available at https://github.com/Jojo6297/edame.git.

Polymorphic light eruption (PLE) is the most frequent photodermatosis in Europe, with an estimated prevalence of 10-20%, particularly in temperate climates. Itching or burning lesions appear only in sun-exposed areas, predominantly on the chest, arms and forearms, within a few hours following exposure. The cause of the disease remains unknown, yet studies have suggested that microbial elements in the skin may play a role in its pathogenesis.

To investigate the skin microbiome of a cohort of patients with PLE upon exposure to ultraviolet radiation (UVR), to assess its role in the onset of PLE lesions.

Forty-one skin swabs were collected from 11 patients with PLE at baseline and after 3 days of exposure to UVR, and from healthy control participants. The collected swabs were analysed for their microbial composition using a 16S amplicon sequencing approach.

PLE skin showed a dysbalanced microbiome at baseline, with significantly reduced microbial diversity and noticeable colonization by bacterial pathogens, including Staphylococcus aureus. Upon UVR exposure, the PLE microbiome exhibited further loss of diversity and a reduction in beneficial skin commensals. In line with this, we found that UVR exerted strong antimicrobial effects in vitro against representative skin residents.

UVR can lead to profound changes in the skin microbiome, allowing the proliferation of dysbiotic members that can release a variety of elements able to trigger PLE lesions. This is the first study to investigate the cutaneous microbiome changes in patients with PLE upon UVR exposure, offering new insights into disease pathogenesis that has so far been unexplored.

This study aimed to compare oral microbiome profiles between obese and lean individuals without clinical periodontitis, and to assess changes in the oral microbiome of obese subjects following bariatric surgery.

Individuals with a body mass index (BMI) > 30 were enrolled in the obese group, whereas those with a BMI < 23 served as controls. The obese surgery group, which consented to bariatric surgery, was followed up at 1, 3, and 6 months with clinical examinations. Oral examinations were conducted and periodontal disease was classified based on probing results. Saliva, buccal and subgingival microbiome samples were analyzed for community diversity, relative bacterial abundance, and differential abundance between control (n = 24) and obese group (n = 31). To evaluate effect size and statistical power, we used micropower, a simulation-based method for Permutational Multivariate Analysis of Variance-based β-diversity comparisons.

The obese group exhibited distinct alpha diversity (buccal: Chao1 p = 0.0002, Shannon p = 0.0003, supragingival: Shannon p < 0.0001) compared with the control group. Bray-Curtis distance analysis indicated significant disparities in microbiome composition distribution in saliva (p = 0.003), buccal (p = 0.002), and subgingival plaque samples (p = 0.001). Although the obese and normal weight groups exhibited no significant periodontal differences, the obese group showed distinct species associated with periodontal disease, especially in subgingival plaque including Filifactor alocis, Peptostreptococcaceae spp., Prevotella spp., and Treponema maltophilum. Cluster analysis of the obese surgery group indicated the emergence of microbiomes associated with a healthy state that increased over time including Streptococcus salivarious and various Veillonella spp., whereas clusters containing periodontal pathogens including Porphyromonas spp., tended to diminish.

The oral microbiome at 6 months post-bariatric surgery indicates a potential shift toward a healthy periodontal state, suggesting that weight loss interventions may positively impact oral microbial communities even in the absence of clinical periodontitis.

The human microbiome plays a vital role in maintaining human homeostasis, acting as a key regulator of host immunity and defense mechanisms. However, dysbiotic microbial communities may cause disruption of the symbiotic relationship between the host and the local microbiota, leading to the pathogenesis of various diseases, including viral infections and cancers. One of the most common infectious agents causing cancer is the human papilloma virus (HPV), which accounts for more than 90% of cervical cancers. In most cases, the host immune system is activated and clears HPV, whereas in some cases, the infection persists and can lead to precancerous lesions. Over the last two decades, the advent of next-generation sequencing (NGS) technology and bioinformatics has allowed a thorough and in-depth analysis of the microbial composition in various anatomical niches, allowing researchers to unveil the interactions and the underlying mechanisms through which the human microbiota could affect HPV infection establishment, persistence, and progression. Accordingly, the present narrative review aims to shed light on our understanding of the role of the human microbiome in the context of HPV infection and its progression, mainly to cervical cancer. Furthermore, we explore the mechanisms by which the composition and balance of microbial communities exert potential pathogenic or protective effects, leading to either HPV persistence and disease outcomes or clearance. Special interest is given to how the microbiome can modulate host immunity to HPV infection. Lastly, we summarize the latest findings on the therapeutic efficacy of probiotics and prebiotics in preventing and/or treating HPV infections and the potential of vaginal microbiota transplantation while highlighting the significance of personalized medicine approaches emerging from NGS-based microbiome profiling and artificial intelligence (AI) for the optimal management of HPV-related diseases.

Microbes have a profound impact on human health. Identifying disease-associated microbes would provide helpful guidance for drug development and disease treatment. Through an enormous experimental effort, limited disease-associated microbes have been determined. Accurate computational approaches are needed to predict potential microbe-disease associations for biomedical screening. In this study, we present an ensemble learning framework entitled SABMDA to improve microbe-disease association inference. We first integrate multi-source of information from both microbes and diseases, and develop two matrix completion algorithms to predict microbe-disease associations successively. Ablation tests show combining the two matrix completion algorithms can receive better prediction performance. Moreover, comprehensive experiments, including cross-validations and independent test, demonstrate that SABMDA outperforms seven recent baseline methods significantly. Finally, we apply SABMDA to three diseases to predict their associated microbes, and results show SABMDA's remarkable prediction ability in real situations.

The endometrial and vaginal microbiota have co-evolved with the reproductive tract and play a key role in both health and disease. However, the difference between endometrial and vaginal microbiota, as well as their association with reproductive outcomes in women undergoing frozen embryo transfer, remains unclear. 120 women who underwent in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) and whole embryo freezing were enrolled. The vaginal and uterine microbiome were sequenced during the first frozen thawed embryo transfer. Based on whether or not they were pregnant after embryo transfer, women were assigned into two groups, and the microbiome of their reproductive tract was compared. The V3-V4 regions of the 16S rRNA gene were examined in the samples using the Next Generation Sequencing method. In the vagina, the non-pregnant group had higher bacterial species richness and diversity, with significantly lower Lactobacillus levels (91.66 % & 74.50 %) and higher Gardnerella levels (3.92 % & 12.12 %) than pregnant group (P < 0.05). In the uterine cavity, the diversity of uterine microbiome between pregnant group and non-pregnant group showed no significant differences. However, dramatic decrease in Lactobacillus (37.27 % & 33.45 %) and Pseudomonas (9.80 % & 4.08 %) were observed in the non-pregnant group (P < 0.05). There may be a correlation between the composition of female reproductive tract microbiome and the reproductive outcomes of patients with frozen-thawed embryo transfer. Lactobacillus-dominated microbiome in reproductive tract is more likely to be associated with higher clinical and ongoing pregnancy rate.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), characterized by neurodegeneration, axonal damage, demyelination, and inflammation. Recently, gut dysbiosis has been linked to MS and other autoimmune conditions. Namely, gut microbiota has a vital role in regulating immune function by influencing immune cell development, cytokine production, and intestinal barrier integrity. While balanced microbiota fosters immune tolerance, dysbiosis disrupts immune regulation, damages intestinal permeability, and heightens the risk of autoimmune diseases. The critical factor in shaping the gut microbiota and modulating immune response is diet. Research shows that high-fat diets rich in saturated fats are associated with disease progression. Conversely, diets rich in fruits, yogurt, and legumes may lower the risk of MS onset and progression. Specific dietary interventions, such as the Mediterranean diet (MD) and ketogenic diet, have shown potential to reduce inflammation, support neuroprotection, and promote CNS repair. Probiotics, by restoring microbial balance, may also help mitigate immune dysfunction noted in MS. Personalized dietary strategies targeting the gut microbiota hold promise for managing MS by modulating immune responses and slowing disease progression. Optimizing nutrient intake and adopting anti-inflammatory diets could improve disease control and quality of life. Understanding gut-immune interactions is essential for developing tailored nutritional therapies for MS patients.

The oral cavity is known to harbor hundreds of microorganisms, belonging to various genera, constituting a peculiar flora called the oral microbiome. The change in the relative distribution of the constituents of this microbial flora, due to any reason, leads to oral dysbiosis. For centuries, oral dysbiosis has been linked to the etiopathogenesis of several medical illnesses, both locally and systemically-. However, aided by the recent advent of bio-technological capabilities, several reports have re-emerged that link oral dysbiosis to oral carcinogenesis, and numerous studies are currently exploring their association and plausible mechanisms. Some of the proposed mechanisms of oral dysbiosis-induced carcinogenesis (ODIC) include-a bacteria-induced chronic inflammatory state leading to direct cellular damage, inflammatory-cytokine-mediated promotion of cellular proliferation and invasion, release of bacterial products that are carcinogenic, and suppression of local immunity by alteration of the tumor microenvironment. However, the actual interactions between these cellular mechanisms and their role in carcinogenesis are not yet fully understood. This review provides a comprehensive overview of the various hypotheses and mechanisms implicated in the ODIC, along with the corresponding molecular aberrations. Apart from discussing the usual constituents of the oral microbiome profile, the review also summarizes the various dysbiosis profiles implicated in ODIC. The review also sheds light on the potential clinical implications of the research on oral microbiome in the prevention and management of oral cancer.

Genome foundation models hold transformative potential for precision medicine, drug discovery, and understanding complex biological systems. However, existing models are often inefficient, constrained by suboptimal tokenization and architectural design, and biased toward reference genomes, limiting their representation of low-abundance, uncultured microbes in the rare biosphere. To address these challenges, we developed GenomeOcean, a 4-billion-parameter generative genome foundation model trained on over 600 Gbp of high-quality contigs derived from 220 TB of metagenomic datasets collected from diverse habitats across Earth's ecosystems. A key innovation of GenomeOcean is training directly on large-scale co-assemblies of metagenomic samples, enabling enhanced representation of rare microbial species and improving generalizability beyond genome-centric approaches. We implemented a byte-pair encoding (BPE) tokenization strategy for genome sequence generation, alongside architectural optimizations, achieving up to 150× faster sequence generation while maintaining high biological fidelity. GenomeOcean excels in representing microbial species and generating protein-coding genes constrained by evolutionary principles. Additionally, its fine-tuned model demonstrates the ability to discover novel biosynthetic gene clusters (BGCs) in natural genomes and perform zero-shot synthesis of biochemically plausible, complete BGCs. GenomeOcean sets a new benchmark for metagenomic research, natural product discovery, and synthetic biology, offering a robust foundation for advancing these fields.

Infertility affects 8-12% of couples worldwide, and 30-75% of preclinical pregnancy losses are due to a failure during the implantation process. Exposure to endocrine disruptors, like bisphenols, among others, has been associated with the increase in infertility observed in the past decades. An increase in infertility has correlated with exposure to endocrine disruptors like bisphenols. The uterus harbors its own microbiota, and changes in this microbiota have been linked to several gynecological conditions, including reproductive failure. There are no studies on the effects of bisphenols on the uterine-microbiota composition, but some inferences can be gleaned by looking at the gut. Bisphenols can alter the gut microbiota, and the molecular mechanism by which gut microbiota regulates intestinal permeability involves Toll-like receptors (TLRs) and tight junction (TJ) proteins. TJs participate in embryo implantation in the uterus, but bisphenol exposure disrupts the expression and localization of TJ proteins. The aim of this review is to summarize the current knowledge on the microbiota of the female reproductive tract (FRT), its association with different reproductive diseases-particularly reproductive failure-the effects of bisphenols on microbiota composition and reproductive health, and the molecular mechanisms regulating uterine-microbiota interactions crucial for embryo implantation. This review also highlights existing knowledge gaps and outlines research needs for future risk assessments regarding the effects of bisphenols on reproduction.

Xanthine oxidase (XO) and lactoperoxidase (LPO) are highly abundant enzymes in milk. Their substrates, xanthine and thiocyanate, are found in elevated amounts in infant saliva, leading to a proposed interaction between milk and saliva referred to as the XO-LPO system. This system is suggested to generate reactive oxygen and nitrogen species with potential antibacterial effects. The antibacterial activity of the XO-LPO system was assessed on bacteria cultured from the oral cavities of five infants, where a reduction in bacterial growth rate was observed at 40 µg mL-1 of each enzyme and with complete inhibition achieved at 200 µg mL-1. Gene expression analysis showed that XO-LPO treatment led to downregulation of several reactive oxygen species-related genes, suggesting a transient bacterial stress response. The study also observed downregulation of key glycolytic enzymes, indicating that XO-LPO treatment affects bacterial metabolism at transcriptional level, suggesting a possible mechanism of action for the XO-LPO system. Collectively, these findings offer new insights into the XO-LPO system, revealing novel aspects of the interaction between lactation and microbiome influence.

To evaluate the uterine microbiome among women with endometrial polyps and submucosal fibroids and to compare results between endometrial sampling techniques.

Patients with polyps or fibroids were prospectively recruited before hysteroscopy, whereas patients undergoing retrieval for planned oocyte cryopreservation were recruited prospectively as controls. Three specimen types obtained for each patient were the distal 5 mm of an embryo catheter passed to the uterine fundus (C), endometrial tissue from an endometrial biopsy (T), and formalin-fixed paraffin-embedded (FFPE) endometrial tissue from the same endometrial biopsy. 16S ribosomal RNA gene amplicon sequencing was performed to analyze the structure of the endometrial microbiome.

Thirty-seven participants including 28 women with polyps and/or fibroids and 9 controls.

None.

Microbial taxonomic alpha and beta diversity; differential abundance of taxa.

Across all sample types, participants with polyps had higher microbial alpha diversity than controls (4.3 vs. 5.1, q = 0.049), and microbial communities were significantly different (pairwise Permutational Multivariate Analysis of Variance (PERMANOVA) pseudo-F = 2.1, q = 0.003). These differences were observed when examining C specimens alone (5.4 vs. 6.4, q = 0.001; pairwise PERMANOVA pseudo-F = 2.5, q = 0.003), although they did not reach significance when examining either T or FFPE specimens alone. Participants with fibroids had similar alpha diversity yet significant differences in beta diversity compared with controls in analyses combining all specimens (pairwise PERMANOVA pseudo-F = 1.475, q = 0.030); however, these differences did not achieve significance when analyzing C, T, or FFPE specimens alone. When comparing C and T specimens vs. FFPE specimens overall, alpha diversity was significantly higher (q < 0.001 and q < 0.001, respectively) and there were significant differences in beta diversity (q < 0.003 and q < 0.003, respectively). Analyses of C specimens generated a larger number of significantly differentially abundant taxa compared with other sampling methods. Although not statistically significant, relative abundance of putative pathogens was higher in participants with polyps than controls regardless of sampling technique.

Results of this exploratory study suggest that significant microbial differences exist among patients with endometrial polyps vs. healthy controls. However, results varied by sampling technique, highlighting a need to identify optimal sampling methods before validating findings in larger prospective cohort studies.

The complex pathophysiology of Alzheimer's disease (AD) poses challenges for the development of therapies. Recently, neuroinflammation has been identified as a key pathogenic mechanism underlying AD, while inflammation has emerged as a possible target for the management and prevention of AD. Several prior studies have demonstrated that medications modulating neuroinflammation might lessen AD symptoms, mostly by controlling neuroinflammatory signaling pathways such as the NF-κB, MAPK, NLRP3, etc, and their respective signaling cascade. Moreover, targeting these inflammatory modalities with inhibitors, natural products, and metabolites has been the subject of intensive research because of their anti-inflammatory characteristics, with many studies demonstrating noteworthy pharmacological capabilities and potential clinical applications. Therefore, targeting inflammation is considered a promising strategy for treating AD. This review comprehensively elucidates the neuroinflammatory mechanisms underlying AD progression and the beneficial effects of inhibitors, natural products, and metabolites in AD treatment.

Infertility affects couples at reproductive age, with in vitro fertilization (IVF) being the most effective treatment. Success rates of IVF are influenced by several factors, including a healthy female reproductive system microbiome, which can improve implantation rates and pregnancy outcomes. This study evaluated the impact of Ligilactobacillus salivarius PS11610 on IVF outcomes. This strain showed antimicrobial activity against pathogens related to dysbiosis, commonly observed in women undergoing assisted reproductive treatment.

The administration of L. salivarius PS11610 at a dose of 1 × 109 CFU every 12 h for at least one month before IVF procedures, particularly in the frozen embryo transfer (FET) group, appears to enhance the success rate of IVF. IVF procedures without embryo transfer showed no significant differences between the groups. However, there were statistically significant differences in the quality of embryos, specifically in category 2, which were higher in the group without L. salivarius PS11610 supplementation (p = 0.042). Similar results were seen in the IVF with embryo transfer group, where the quality of embryos in categories 2 and 3 was higher in the group without L. salivarius PS11610 (p = 0.019 and p = 0.05, respectively). IVF with FET showed notable improvements, where intake of L. salivarius PS11610 was associated with a significant increase in live birth infants (26.4% with L. salivarius PS11610 vs. 17.9% without, p = 0.034) and higher biochemical pregnancy rates (42.6% vs. 34%, p = 0.071).

Despite some differences in embryo quality, the overall positive impact on pregnancy and birth outcomes highlights L. salivarius PS11610 as a promising supplement in assisted reproductive treatments.

Covering: 2016 to 2024Natural products, particularly cyclic peptides, are a promising source of bioactive compounds. Nonribosomal peptide synthetases (NRPSs) play a key role in biosynthesizing these compounds, which include antibiotic and anticancer agents, immunosuppressants, and others. Traditional methods of discovering natural products have limitations including cryptic biosynthetic gene clusters (BGCs), low titers, and currently unculturable organisms. This has prompted the exploration of alternative approaches. Synthetic-bioinformatic natural products (syn-BNPs) are one such alternative that utilizes bioinformatics techniques to predict nonribosomal peptides (NRPs) followed by chemical synthesis of the predicted peptides. This approach has shown promise, resulting in the discovery of a variety of bioactive compounds including peptides with antibacterial, antifungal, anticancer, and proteasome-stimulating activities. Despite the success of this approach, challenges remain especially in the accurate prediction of fatty acid incorporation, tailoring enzyme modifications, and peptide release mechanisms. Further work in these areas will enable the discovery of many bioactive peptides that are currently inaccessible.

Women's health is essential to global societal and economic wellbeing, yet health disparities remain prevalent. The vaginal microbiota plays a critical role in health, with research indicating that reduced levels of core bacteria, such as lactobacilli, are associated with conditions like bacterial vaginosis (BV) and increased infection susceptibility. Lower levels of vaginal lactobacilli are reported more frequently in women of African and Latin American descent compared with women of European and Asian descent. However, geographical and other study inclusion and analysis biases influence current research. This opinion highlights the need for a more comprehensive understanding of a 'healthy' vaginal microbiome. It underscores efforts to broaden global research on microbiome diversity in socially relevant contexts, avoiding inappropriate applications of terms such as race and ethnicity.

The term "gut microbiota" primarily refers to the ecological community of various microorganisms in the gut, which constitutes the largest microbial community in the human body. Although adequate bowel preparation can improve the results of colonoscopy, it may interfere with the gut microbiota. Bowel preparation for colonoscopy can lead to transient changes in the gut microbiota, potentially affecting an individual's health, especially in vulnerable populations, such as patients with inflammatory bowel disease. However, measures such as oral probiotics may ameliorate these adverse effects. We focused on the bowel preparation-induced changes in the gut microbiota and host health status, hypothesized the factors influencing these changes, and attempted to identify measures that may reduce dysbiosis, thereby providing more information for individualized bowel preparation for colonoscopy in the future.

Microorganisms, including bacteria, fungi, and viruses, that reside on and within the human body are collectively known as the human microbiome. Dysbiosis, or disruption in the microbiome, has been implicated in several disease processes, including asthma, obesity, autoimmune diseases, and numerous other conditions. While the Human Microbiome Project and the generation of descriptive studies it inspired established correlations between characteristic patterns in the composition of the microbiome and specific disease phenotypes, current research has begun to focus on elucidating the causal role of the microbiome in disease pathogenesis. Within the field of orthopaedic surgery, researchers have proposed the concept of a "gut-joint axis," whereby the intestinal microbiome influences joint health and the development of diseases, such as osteoarthritis and periprosthetic joint infection (PJI). It is theorized that intestinal dysbiosis increases gut permeability, leading to the translocation of bacteria and their metabolic products into the systemic circulation and the stimulation of proinflammatory response cascades throughout the body, including within the joints. While correlative studies have identified patterns of dysbiotic derangement associated with osteoarthritis and PJI, translational research is needed to clarify the precise mechanisms by which these changes influence disease processes. Additionally, an emerging body of literature has challenged the previously held belief that certain body sites are sterile and do not possess a microbiome, with studies identifying distinct microbial genomic signatures and a core microbiome that varies between anatomic sites. A more thorough characterization of the joint microbiome may have profound implications for our understanding of PJI pathogenesis and our ability to stratify patients based on risk. The purpose of this review was to outline our current understanding of the human microbiome to describe the gut-joint axis and its role in specific pathologies, including PJI, and to highlight the potential of microbiome-based therapeutic interventions in the field of orthopaedics.

The human microbiome consists of diverse microorganisms that inhabit various body sites. As these microbes are increasingly recognized as key determinants of health, there is significant interest in leveraging individual microbiome profiles for early disease detection, prevention, and drug efficacy prediction. However, the complexity of microbiome data, coupled with conflicting study outcomes, has hindered its integration into clinical practice. This challenge is partially due to demographic and technological biases that impede the development of reliable disease classifiers. Here, we examine recent advances in 16S rRNA and shotgun-metagenomics sequencing, along with bioinformatics tools designed to enhance microbiome data integration for precision diagnostics and personalized treatments. We also highlight progress in microbiome-based therapies and address the challenges of establishing causality to ensure robust diagnostics and effective treatments for complex diseases.

To evaluate the impact of Endometrial Microbiome Metagenomic Analysis and Analysis of Infectious Chronic Endometritis (EMMA & ALICE) on pregnancy outcomes following recommended treatments in women with recurrent implantation failure (RIF) or recurrent pregnancy loss (RPL).

This prospective, multicenter cohort study included 527 women under 42 years old with RIF or RPL across 14 IVF centers in Japan. Endometrial samples were analyzed using EMMA & ALICE, and patients received antibiotics, probiotics, or no treatment based on test results. Pregnancy outcomes were assessed using Kaplan-Meier survival analysis and multivariate generalized linear models.

Amongst participants, 43.4% had a normal Lactobacillus-dominated microbiota, 20.9% had dysbiosis, and 35.7% had mild dysbiosis or ultralow biomass. Kaplan-Meier analysis revealed significantly higher ongoing pregnancy rates in the dysbiosis group treated with antibiotics and probiotics compared to other groups (p = 0.031). Post-treatment, ongoing pregnancy rates in the dysbiosis and mild dysbiosis groups were comparable to the normal group.

EMMA & ALICE-guided antimicrobial and probiotic treatments improved pregnancy outcomes, enabling the dysbiosis group to achieve pregnancy earlier than the normal group. Addressing uterine dysbiosis may reduce the time to pregnancy in patients with RIF and RPL.

University Hospital Medical Information Network (UMIN), UMIN000036917.

DNA-based technologies have been used in forensic practice since the mid-1980s. While PCR-based STR genotyping using Capillary Electrophoresis remains the gold standard for generating DNA profiles in routine casework worldwide, the research community is continually seeking alternative methods capable of providing additional information to enhance discrimination power or contribute with new investigative leads. Oxford Nanopore Technologies (ONT) and PacBio third-generation sequencing have revolutionized the field, offering real-time capabilities, single-molecule resolution, and long-read sequencing (LRS). ONT, the pioneer of nanopore sequencing, uses biological nanopores to analyze nucleic acids in real-time. Its devices have revolutionized sequencing and may represent an interesting alternative for forensic research and routine casework, given that it offers unparalleled flexibility in a portable size: it enables sequencing approaches that range widely from PCR-amplified short target regions (e.g., CODIS STRs) to PCR-free whole transcriptome or even ultra-long whole genome sequencing. Despite its higher error rate compared to Illumina sequencing, it can significantly improve accuracy in read alignment against a reference genome or de novo genome assembly. This is achieved by generating long contiguous sequences that correctly assemble repetitive sections and regions with structural variation. Moreover, it allows real-time determination of DNA methylation status from native DNA without the need for bisulfite conversion. LRS enables the analysis of thousands of markers at once, providing phasing information and eliminating the need for multiple assays. This maximizes the information retrieved from a single invaluable sample. In this review, we explore the potential use of LRS in different forensic genetics approaches.

The gut microbiome, a complex ecosystem of microorganisms in the digestive tract, has emerged as a critical factor in human health, influencing metabolic, immune, and neurological functions. This review explores the connection between the gut microbiome and orthopedic health, examining how gut microbes impact bone density, joint integrity, and skeletal health. It highlights mechanisms linking gut dysbiosis to inflammation in conditions such as rheumatoid arthritis and osteoarthritis, suggesting microbiome modulation as a potential therapeutic strategy. Key findings include the microbiome's role in bone metabolism through hormone regulation and production of short-chain fatty acids, crucial for mineral absorption. The review also considers the effects of diet, probiotics, and fecal microbiota transplantation on gut microbiome composition and their implications for orthopedic health. While promising, challenges in translating microbiome research into clinical practice persist, necessitating further exploration and ethical consideration of microbiome-based therapies. This interdisciplinary research aims to link digestive health with musculoskeletal integrity, offering new insights into the prevention and management of bone and joint diseases.

Rheumatoid arthritis (RA) represents an autoimmune condition impacted by a combination of genetic and environmental factors, with the gut microbiome (GMB) being one of the influential environmental factors. Patients with RA display notable modifications in the composition of their GMB, characterised by decreased diversity and distinct bacterial alterations. The GMB, comprising an extensive array of approximately 35,000 bacterial species residing within the gastrointestinal tract, has garnered considerable attention as a pivotal contributor to both human health and the pathogenesis of diseases. This article provides an in-depth exploration of the intricate involvement of the GMB in the context of RA. The oral-GMB axis highlights the complex role of bacteria in RA pathogenesis by producing antibodies to citrullinated proteins (ACPAs) through molecular mimicry. Dysbiosis affects Tregs, cytokine levels, and RA disease activity, suggesting that regulating cytokines could be a strategy for managing inflammation in RA. The GMB also has significant implications for drug responses and toxicity, giving rise to the field of pharmacomicrobiomics. The composition of the microbiota can impact the efficacy and toxicity of drugs, while the microbiota's metabolites can influence drug response. Recent research has identified specific bacteria, metabolites, and immune responses associated with RA, offering potential targets for personalised management. However, several challenges, including the variation in microbial composition, establishing causality, accounting for confounding factors, and translating findings into clinical practice, need to be addressed. Microbiome-targeted therapy is still in its early stages and requires further research and standardisation for effective implementation.