The human microbiome is increasingly recognized as a key determinant of health and disease, yet methodological variability continues to limit reproducibility and clinical translation of findings. This review synthesizes current approaches in microbiome research, critically evaluating each step from sampling to sequencing and downstream bioinformatics. Pre-analytical factors such as sample type, collection method, preservation, and storage conditions profoundly affect microbial community profiles and remain a major source of bias. Nucleic acid extraction protocols and quality assessment strategies are discussed with emphasis on optimized lysis techniques, contamination controls, and DNA yield evaluation. Advances in sequencing technologies are highlighted, including 16S rRNA amplicon sequencing, shotgun metagenomics, third-generation long-read platforms, and emerging single-cell and minimal-input methods, each with specific advantages and limitations in taxonomic and functional resolution. Bioinformatics pipelines for taxonomic profiling, variant detection, phylogenetic inference, and functional annotation are compared, with attention to widely used reference databases such as RefSeq, GTDB, and SILVA. Integrative multi-omics approaches, including metatranscriptomics, metabolomics, and genome-scale metabolic modeling, are presented as powerful tools for linking microbial community structure to host physiology and disease mechanisms. Despite these advances, the lack of standardized workflows across pre-analytical, sequencing, and computational steps continues to hinder inter-study comparability and biomarker validation. This review aims to provide a methodological framework that highlights both strengths and limitations of current technologies while underlining the need for harmonized protocols to ensure reproducibility and accelerate the translation of microbiome research into clinical practice.

The human microbiome, which encompasses microbial communities and their genetic material, significantly influences health and disease, including cancer. The urogenital microbiota, naturally present in the urinary and genital tracts, interact with factors such as age, lifestyle, and health conditions to affect homeostasis and carcinogenesis. Studies suggest that alterations in this microbiota contribute to the development and progression of genitourinary cancers, emphasizing the concept of oncobiome, which refers to microbial genetic contributions to cancer. Similarly, gut microbiota can influence hormone levels and systemic inflammation, impacting cancers such as cervical and prostate cancer. Advanced studies indicate that microbial communities in genitourinary cancers have distinct profiles that may serve as diagnostic biomarkers or therapeutic targets. Dysbiosis of the urinary microbiota correlates with bladder and kidney cancer. Additionally, gut microbiota influence the effectiveness of cancer treatments. However, further research is necessary to clarify causality, the role of microbial metabolites, and hormonal regulation. The aim of this review is to understand that these dynamics present opportunities for innovative cancer diagnostics and therapies, highlighting the need for integration of microbiology, oncology, and genomics to explore the role of microbiota in genitourinary cancers. For this, a comprehensive search of relevant databases was conducted, applying specific inclusion and exclusion criteria to identify studies examining the association between microbiota and urogenital cancers. Research into the mechanisms by which microbiota influence urogenital cancers may pave the way for new diagnostic and therapeutic approaches, ultimately improving patient outcomes.

Background: The human gut microbiota is an extensive population of microorganisms, and it shows significant variations between periods of optimal health and periods of illness. Vancomycin-resistant Enterococcus (VRE) and carbapenem-resistant Klebsiella pneumoniae (CRKP) are both pathogenic agents (BPAs) that can colonize in the gut after dysbiosis of microbiotal composition following antibiotic treatment. Methods: This study aimed to investigate the impact of antibiotics on the microbiotal composition of the gut. For this purpose, the first pass meconiums of 20 patients and the first rectal swabs containing BPAs of the same patients after antibiotic treatment were studied using next-generation sequencing-based 16S rRNA gene analysis. The V1–V9 region of 16S rRNA was sequenced with Oxford Nanopore. Results: Twenty-five phyla were detected in the meconiums, and 12 of them were absent after antibiotic treatment. The four most prevalent phyla in meconiums were Bacillota, Pseudomonadota, Bacteroidota, and Actinomycetota. Only the relative abundance of Pseudomonadota was increased, while a significant decrease was observed in the other three phyla (p < 0.05). A significant decrease was observed in alpha-diversity in rectal swabs containing BPAs versus meconiums (p = 0.00408), whereas an increased variance was observed in beta-diversity in all samples (p < 0.05). As a result of a LEfSe analysis, Pseudomonadota was found to have a higher relative abundance in rectal swabs, and Bacillota was significantly higher in the meconiums of the twins. Conclusions: Our study strongly verified the relationship between the administration of antibiotics, dysbiosis, and colonization of BPAs in the infants’ gut microbiota. Further research would be beneficial and needed, comprising the natural development process of the infants’ gut microbiota.

Introduction: Escherichia coli is among the most common causes of urinary tract infections (UTIs), and the rise in extended-spectrum beta-lactamase (ESBL)-producing isolates is a growing concern because of increasingly limited treatment options. This study aimed to investigate the resistance profiles of ESBL-positive and -negative uropathogenic E. coli isolates to antibiotics used in treatment and colistin. Methodology: Urine samples sent to the Central Laboratory of Istanbul University, Istanbul Faculty of Medicine, for routine examination between September 2023 and January 2024 were included in the study. The presence of ESBL and susceptibility to antibiotics other than colistin were determined by the Vitek2 (bioMérieux, France) automated system, and colistin susceptibility was determined by the reference broth microdilution method. Results: Of the 80 patients with E. coli, 80% were female, and 20% were male; 32.5% were children, and 67.5% were adults. All E. coli isolates were susceptible to nitrofurantoin, ertapenem, and meropenem. 40% of the strains were ESBL-positive. In respect of multidrug-resistant bacteria, among ESBL-positive and-negative isolates, 12.5% and 2.1% were resistant to six, 28.1% and 8.3% to five, 34.4% and 6.25% to four, and 18.75% and 16.7% to three different antibiotic groups, respectively. Of 80 E. coli strains, 92.5% of which were sensitive to colistin, the MIC50 value was 0.5 mg/L, and the MIC90 value was 2 mg/L. Conclusions: Although the colistin resistance rate and minimum inhibitory concentration (MIC) values are not high, it is important to monitor resistance when treating problematic infections with multiple resistance.

Introduction: Carbapenem-resistant Klebsiella pneumoniae (CRKP) and vancomycin-resistant Enterococcus (VRE) pose significant threats in neonatal intensive care units (NICUs) due to their high transmission potential and limited treatment options. Identifying the source of colonization and detection of predominant antibiotic-resistant genes are crucial for effective infection control measures. Methodology: In this study, we analyzed 20 carbapenem-susceptible Klebsiella pneumoniae (CSKP) and 20 Vancomycin-susceptible Enterococcus (VSE) of meconium versus 20 CRKP and 20 VRE of rectal swabs isolates, respectively, from 20 preterm infants hospitalized in the NICU. Pulsed-field gel electrophoresis (PFGE) and arbitrarily primed PCR (AP-PCR) were used for molecular epidemiological analysis. The presence of carbapenemase (blaOXA-48, blaNDM, blaKPC) and Vancomycin-resistance (vanA, vanB, vanC) genes was investigated by multiplex PCR. Results: No predominant outbreak strain was detected, and isolates exhibited high genetic diversity, indicating an exogenous source of colonization of both CRKP and VRE. PFGE analysis revealed 24 distinct genotypes among CRKP and 25 among VRE isolates, with a clustering rate of 57.5%. The most commonly detected resistance gene in CRKP isolates was blaOXA-48 (50%), followed by blaNDM (35%) and blaKPC (10%). Among VRE isolates, only the vanA gene was present (85%). Conclusions: The absence of a clonal outbreak and the detection of resistance genes primarily on plasmids indicate healthcare-associated transmission rather than endogenous selection. This highlights the critical role of hand hygiene and strict infection control measures in preventing multidrug-resistant pathogen colonization in vulnerable preterm infants.