Non-viable bacteria, referred to as “paraprobiotics,” have attracted attention as potentially safer alternatives to probiotics. The aim of this study was to investigate the efficacy of heat-killed Lactobacillus casei DKGF7 on the symptomatic improvement of irritable bowel syndrome (IBS) in a rat disease model and to elucidate the underlying mechanisms that contribute to the beneficial effects of heat-killed probiotics. Seven male Wistar rats were induced with IBS by restraint stress and administered heat-killed L. casei DKGF7 for four weeks and then compared with seven rats in the control group. Stool consistency measured four weeks after initial treatment was the primary outcome measure. To investigate the mechanism of action of the heat-killed bacteria on IBS, we measured serum corticosterone levels, inflammatory cytokines in colon tissue, and expression of tight junction proteins (TJPs) in the epithelium. The treatment group showed significantly better stool consistency scores than the control group at week 4, as well as at every measured time point (all p values < 0.05). The treatment group showed lower serum corticosterone levels, lower colonic inflammatory cytokine levels, and higher expression of TJPs compared with the control group. Paraprobiotics such as heat-killed L. casei DKGF7 can improve stool consistency in a rat IBS model, which may indicate a potential therapeutic strategy for IBS treatment.

The administration of a combination of probiotics and prebiotics is expected to be a promising strategy for improving irritable bowel syndrome (IBS) symptoms. This study aimed to investigate the efficacy of a synbiotic containing Lactobacillus paracasei and Opuntia humifusa extract for symptomatic improvement of IBS in a murine model and to evaluate the mechanism underlying the beneficial effects of this synbiotic. A total of 20 male Wistar rats aged 8 weeks with IBS induced by restraint stress were assigned into four groups and administered L. paracasei as a probiotic and O. humifusa extract as a prebiotic for 4 weeks. The primary outcome was stool consistency at week 4. To evaluate the mechanism underlying the beneficial effects of the synbiotic, fecal microbial analysis was conducted, and the serum corticosterone levels, tumor necrosis factor-α (TNF-α) levels in the colon tissue, and expression of tight junction proteins were investigated. All three treatment groups showed significantly lower scores for stool consistency than the control group at week 4 (all p < 0.001). When compared with the control group, the synbiotic groups showed a significantly greater abundance of L. paracasei in fecal microbial analysis, lower serum corticosterone levels, lower TNF-α levels in the colon tissue, and higher expression of tight junction proteins. This novel synbiotic containing L. paracasei and O. humifusa extract can improve the stool consistency in a murine model of IBS. It may be a promising treatment option for IBS, and human studies are warranted.

How does the gut bacteriome differ based on mood disorders (MDs) in women with polycystic ovary syndrome (PCOS), and how can the gut bacteriome contribute to the associations between these two conditions? Women with PCOS who also have MDs exhibited a distinct gut bacteriome with reduced alpha diversity and a significantly lower abundance of Butyricicoccus compared to women with PCOS but without MDs. Women with PCOS have a 4- to 5-fold higher risk of having MDs compared to women without PCOS. The gut bacteriome has been suggested to influence the pathophysiology of both PCOS and MDs. This population-based cohort study was derived from the Northern Finland Birth Cohort 1966 (NFBC1966), which includes all women born in Northern Finland in 1966. Women with PCOS who donated a stool sample at age 46 years (n = 102) and two BMI-matched controls for each case (n = 205), who also responded properly to the MD criteria scales, were included. A total of 102 women with PCOS and 205 age- and BMI-matched women without PCOS were included. Based on the validated MD criteria, the subjects were categorized into MD or no-MD groups, resulting in the following subgroups: PCOS no-MD (n = 84), PCOS MD (n = 18), control no-MD (n = 180), and control MD (n = 25). Clinical characteristics were assessed at age 31 years and age 46 years, and stool samples were collected from the women at age 46 years, followed by the gut bacteriome analysis using 16 s rRNA sequencing. Alpha diversity was assessed using observed features and Shannon’s index, with a focus on genera, and beta diversity was characterized using principal components analysis (PCA) with Bray–Curtis Dissimilarity at the genus level. Associations between the gut bacteriome and PCOS-related clinical features were explored by Spearman’s correlation coefficient. A P-value for multiple testing was adjusted with the Benjamini–Hochberg false discovery rate (FDR) method. We observed changes in the gut bacteriome associated with MDs, irrespective of whether the women also had PCOS. Similarly, PCOS MD cases showed a lower alpha diversity (Observed feature, PCOS no-MD, median 272; PCOS MD, median 208, FDR = 0.01; Shannon, PCOS no-MD, median 5.95; PCOS MD, median 5.57, FDR = 0.01) but also a lower abundance of Butyricicoccus (log-fold change_{Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC)}=−0.90, FDR_ANCOM-BC=0.04) compared to PCOS no-MD cases. In contrast, in the controls, the gut bacteriome did not differ based on MDs. Furthermore, in the PCOS group, Sutterella showed positive correlations with PCOS-related clinical parameters linked to obesity (BMI, r²=0.31, FDR = 0.01; waist circumference, r²=0.29, FDR = 0.02), glucose metabolism (fasting glucose, r²=0.46, FDR < 0.001; fasting insulin, r²=0.24, FDR = 0.05), and gut barrier integrity (zonulin, r²=0.25, FDR = 0.03). Although this was the first study to assess the link between the gut bacteriome and MDs in PCOS and included the largest PCOS dataset for the gut microbiome analysis, the number of subjects stratified by the presence of MDs was limited when contrasted with previous studies that focused on MDs in a non-selected population. The main finding is that gut bacteriome is associated with MDs irrespective of the PCOS status, but PCOS may also modulate further the connection between the gut bacteriome and MDs. This research was funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement (MATER, No. 813707), the Academy of Finland (project grants 315921, 321763, 336449), the Sigrid Jusélius Foundation, Novo Nordisk Foundation (NNF21OC0070372), grant numbers PID2021-12728OB-100 (Endo-Map) and CNS2022-135999 (ROSY) funded by MCIN/AEI/10.13039/501100011033 and ERFD A Way of Making Europe. The study was also supported by EU QLG1-CT-2000-01643 (EUROBLCS) (E51560), NorFA (731, 20056, 30167), USA/NIH 2000 G DF682 (50945), the Estonian Research Council (PRG1076, PRG1414), EMBO Installation (3573), and Horizon 2020 Innovation Grant (ERIN, No. EU952516). The funders did not participate in any process of the study. We have no conflicts of interest to declare. N/A.

STUDY QUESTION: Do polycystic ovary syndrome (PCOS), menstrual cycle phases, and ovulatory status affect reproductive tract (RT) microbiome profiles? SUMMARY ANSWER: We identified microbial features associated with menstrual cycle phases in the upper and lower RT microbiome, but only two specific differences in the upper RT according to PCOS status. WHAT IS KNOWN ALREADY: The vaginal and uterine microbiome profiles vary throughout the menstrual cycle. Studies have reported alterations in the vaginal microbiome among women diagnosed with PCOS. STUDY DESIGN, SIZE, DURATION: This prospective case-control study included a cohort of 37 healthy control women and 52 women diagnosed with PCOS. Microbiome samples were collected from the vagina as vaginal swabs (VS) and from the uterus as endometrial flushing (EF) aspirate samples, and compared according to PCOS diagnosis, the menstrual cycle phases, and ovulatory status, at Oulu University Hospital (Oulu, Finland) from January 2017 to March 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 83 VS samples and 80 EF samples were collected. Age and body mass index (BMI) were matched between women with and without PCOS. Clinical characteristics were assessed using blood samples collected between cycle days 2 and 8, and microbial DNA was sequenced on the Ion Torrent platform. Microbial alpha diversity (i.e. the observed number of unique genera and Shannon diversity index) was analysed across sample types, PCOS diagnosis and menstrual cycle phases. Linear mixed-effects models were utilised to identify microbial features in relation to PCOS and the menstrual cycle phases. Associations between the beta diversity of the RT microbiome and PCOS- and cycle-related clinical features were calculated using PERMANOVA. MAIN RESULTS AND THE ROLE OF CHANCE: Microbial alpha diversity showed no difference with PCOS (VS: Pobserved feature = 0.836, Pshannon = 0.998; EF: Pobserved feature = 0.366, Pshannon = 0.185), but varied with menstrual cycle phases (VS: Pobserved feature = 0.001, Pshannon = 0.882; EF: Pobserved feature = 0.026, Pshannon = 0.048). No difference was observed in beta diversity based on either PCOS or the menstrual cycle phases (VS: PPCOS = 0.280, Pcycle = 0.115; EF: PPCOS = 0.234, Pcycle = 0.088). In the endometrial flushing samples, we identified two novel microbial features, characterised by the ratio of differential abundance of two genera, associated with PCOS (FDR ≤ 0.1) and 13 novel features associated with the menstrual cycle phases (FDR ≤ 0.1). LIMITATIONS, REASONS FOR CAUTION: Although this was the first study to simultaneously analyse, the lower and upper RT microbiome in women with and without PCOS, the limited sample size of anovulatory cases may hinder the detection of differences related to PCOS and ovulatory status. WIDER IMPLICATIONS OF THE FINDINGS: The main finding suggests that PCOS and the menstrual cycle phases are associated with specific microbial features in the upper RT, indicating that the analysis of the upper RT microbiome can potentially identify biomarkers for both PCOS and menstrual cycle phases. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Research Council of Finland (grants no. 315921, 321763, 336449), the Sigrid Jusélius Foundation, Novo Nordisk Foundation (grant no. NNF21OC0070372), and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant (MATER, grant no. 813707). This research was also funded by the Estonian Research Council (grants no. PRG1076, PRG1414), the Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission, and EMBO Installation Grant (grant no. 3573). The funders did not participate in any processes of the study. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Immunohistochemical analysis of CD138+ plasma cells has been applied for detecting endometrial inflammation, especially chronic endometritis (CE). In this study, we developed for the first time an artificial intelligence (AI) algorithm, AITAH, to identify CD138+ plasma cells within endometrial tissue, focusing on two infertility-related conditions: polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF). We obtained 193 endometrial tissues from healthy controls (n=73), women with PCOS (n=91), and RIF patients (n=29) and compared CD138+ cell percentages across cycle phases, ovulation status, and endometrial receptivity. We trained AITAH with CD138 stained tissue images, and experienced pathologists validated the training and performance of AITAH. AITAH, with high accuracy in detecting CD138+ cells (88.57%), revealed higher CD138+ cell percentages in the proliferative phase than in the secretory phase or in the anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the proliferative phase (p=0.01). Different receptivity statuses had no impact on the cell percentages in RIF samples. In summary, the AI-enabled analysis is a rapid and accurate tool to examine endometrial tissues, potentially aiding clinical decision-making. Here, the AI analysis demonstrated cycle-phase differences in CD138+ aggregations pattern, but no major alterations in PCOS or RIF samples.

Chronic placental inflammation (CPI), an umbrella term encompassing chronic chorioamnionitis, chronic villitis, and chronic deciduitis, is associated with preterm birth (PTB), yet its cellular and molecular determinants across gestation remain poorly defined. Here, we constructed an integrated multiomic atlas of CPI by profiling placental and extraplacental tissues from preterm and term pregnancies using imaging mass cytometry, single-cell RNA sequencing, spatial transcriptomics, and T cell receptor (TCR) repertoire mining. CPI emerged as a gestational age–dependent immune continuum rather than a uniform inflammatory state. Preterm CPI was characterized by infiltration of cytotoxic CD8 ⁺ T cells, activated macrophages, and natural killer (NK) cells in the extraplacental membranes. In contrast, term CPI exhibited spatially confined immune niches enriched for homeostatic myeloid cells and regulatory T cells. Multiomic integration revealed active contributions from the developing fetal immune system in preterm CPI, including macrophages, T cells, and mast cells that reshaped maternal-fetal immune cross-talk. Cell-cell–communication analyses uncovered proinflammatory signaling states in preterm CPI and resolution-phase pathways in term CPI, whereas TCR repertoire profiling identified memory clonotypes with viral epitope similarity, consistent with antigen-agnostic activation. Overall, this atlas defines the cellular, spatial, and transcriptional architecture of CPI and provides a framework for translational biomarker discovery and therapeutic intervention development.