The human gastrointestinal tract is inhabited by the largest microbial community within the human body consisting of trillions of microbes called gut microbiota. The normal flora is the site of many physiological functions such as enhancing the host immunity, participating in the nutrient absorption and protecting the body against pathogenic microorganisms. Numerous investigations showed a bidirectional interplay between gut microbiota and many organs within the human body such as the intestines, the lungs, the brain, and the skin. Large body of evidence demonstrated, more than a decade ago, that the gut microbial alteration is a key factor in the pathogenesis of many local and systemic disorders. In this regard, a deep understanding of the mechanisms involved in the gut microbial symbiosis/dysbiosis is crucial for the clinical and health field. We review the most recent studies on the involvement of gut microbiota in the pathogenesis of many diseases. We also elaborate the different strategies used to manipulate the gut microbiota in the prevention and treatment of disorders. The future of medicine is strongly related to the quality of our microbiota. Targeting microbiota dysbiosis will be a huge challenge.

Calcium-sensing receptor (CaSR) is a plasma membrane protein that regulates tubular reabsorption of Ca. To establish its role in idiopathic hypercalciuria, the association of urinary Ca excretion with the polymorphisms of CASR gene has been studied in healthy subjects and in hypercalciuric and normocalciuric Ca stone formers. CASR exon 7 single nucleotide polymorphisms (SNP), G/T at codon 986, G/A at codon 990, and C/G at codon 1011, were evaluated by PCR amplification and direct sequencing in 97 normocalciuric stone formers, 134 hypercalciuric stone formers, and 101 normocalciuric healthy controls. Four haplotypes were defined on the basis of CASR gene SNP: haplotype 1 was characterized by the most frequent sequence; haplotypes 2, 3, or 4 by the presence of a single polymorphic variant at codon 986, 990, or 1011, respectively. The relative risk of hypercalciuria was calculated with multinomial logistic regression and was significantly increased only in individuals carrying haplotype 3 (Odds ratio, 13.0 [95% confidence interval, 1.7 to 99.4]). Accordingly, Ca excretion was higher in subjects bearing haplotype 3, whereas those bearing haplotype 2 showed a slight increase of plasma Ca concentration. Multiple regression analysis showed that haplotype 3 explained 4.1% of the total variance of Ca excretion and 12.6% of the variance explained by the variables considered in the study. In conclusion, CASR gene could be a component of the complex genetic background regulating Ca excretion. Arg990Gly polymorphism could facilitate activation of CaSR and increase Ca excretion and susceptibility to idiopathic hypercalciuria.

Preterm birth (PTB) is the most common cause of neonatal morbidity and mortality worldwide. Approximately half of PTBs is linked with microbial etiologies, including pathologic changes to the vaginal microbiota, which vary according to ethnicity. Globally more than 50% of PTBs occur in Asia, but studies of the vaginal microbiome and its association with pregnancy outcomes in Asian women are lacking. This study aimed to longitudinally analyzed the vaginal microbiome and cytokine environment of 18 Karen and Burman pregnant women who delivered preterm and 36 matched controls delivering at full term. Using 16S ribosomal RNA gene sequencing we identified a predictive vaginal microbiota signature for PTB that was detectable as early as the first trimester of pregnancy, characterized by higher levels of Prevotella buccalis, and lower levels of Lactobacillus crispatus and Finegoldia, accompanied by decreased levels of cytokines including IFNγ, IL-4, and TNFα. Differences in the vaginal microbial diversity and local vaginal immune environment were associated with greater risk of preterm birth. Our findings highlight new opportunities to predict PTB in Asian women in low-resource settings who are at highest risk of adverse outcomes from unexpected PTB, as well as in Burman/Karen ethnic minority groups in high-resource regions.

Chemotherapy resistance resulting in incomplete pathologic response is associated with high risk of metastasis and early relapse in breast cancer. The aim of this study was to identify and evaluate biomarkers of treatment-resistant tumor cells. We performed a cell surface marker screen in triple-negative breast cancer patient-derived xenograft models treated with standard care genotoxic chemotherapy. Global expression profiling was used to further characterize the identified treatment-resistant subpopulations. High expression of sialyl-glycolipid stage-specific embryonic antigen 4 (SSEA4) was found in residual tumor cells surviving chemotherapy and in samples from metastatic patients who relapsed after neoadjuvant chemotherapy. Gene and microRNA (miRNA) expression profiling linked SSEA4 positivity with a mesenchymal phenotype and a deregulation of drug resistance pathways. Functional assays demonstrated a direct link between epithelial–mesenchymal transition (EMT) and SSEA4 expression. Interestingly, SSEA4 expression, EMT, and drug resistance seemed to be regulated posttranscriptionally. Finally, high expression of CMP-N-acetylneuraminate-β-galactosamide-α-2,3-sialyltransferase 2 (ST3GAL2), the rate-limiting enzyme of SSEA4 synthesis, was found to be associated with poor clinical outcome in breast and ovarian cancer patients treated with chemotherapy. In this study, we identified SSEA4 as highly expressed in a subpopulation of tumor cells resistant to multiple commonly used chemotherapy drugs, as well as ST3GAL2, the rate-limiting enzyme of SSEA4 synthesis, as a predictive marker of poor outcome for breast and ovarian cancer patients undergoing chemotherapy. Both biomarkers and additionally identified regulatory miRNAs may be used to further understand chemoresistance, to stratify patient groups in order to avoid ineffective and painful therapies, and to develop alternative treatment regimens for breast cancer patients.

Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.