An Oral Microbial Biomarker for Early Detection of Recurrence of Oral Squamous Cell Carcinoma.

Changes in the oral microbiome are associated with oral squamous cell carcinoma (OSCC). Oral microbe-derived signatures have been utilized as markers of OSCC. However, the structure of the oral microbiome during OSCC recurrence and biomarkers for the prediction of OSCC recurrence remains unknown. To identify OSCC recurrence-associated microbial biomarkers for the prediction of OSCC recurrence, we performed 16S rRNA amplicon sequencing on 54 oral swab samples from OSCC patients. Differences in bacterial compositions were observed in patients with vs without recurrence. We found that Granulicatella, Peptostreptococcus, Campylobacter, Porphyromonas, Oribacterium, Actinomyces, Corynebacterium, Capnocytophaga, and Dialister were enriched in OSCC recurrence. Functional analysis of the oral microbiome showed altered functions associated with OSCC recurrence compared with nonrecurrence. A random forest prediction model was constructed with five microbial signatures including Leptotrichia trevisanii, Capnocytophaga sputigena, Capnocytophaga, Cardiobacterium, and Olsenella to discriminate OSCC recurrence from original OSCC (accuracy = 0.963). Moreover, we validated the prediction model in another independent cohort (46 OSCC patients), achieving an accuracy of 0.761. We compared the accuracy of the prediction of OSCC recurrence between the five microbial signatures and two clinicopathological parameters, including resection margin and lymph node counts. The results predicted by the model with five microbial signatures showed a higher accuracy than those based on the clinical outcomes from the two clinicopathological parameters. This study demonstrated the validity of using recurrence-related microbial biomarkers, a noninvasive and effective method for the prediction of OSCC recurrence. Our findings may contribute to the prognosis and treatment of OSCC recurrence.

Generation of induced pluripotent stem cells from Bornean orangutans.

Introduction: Orangutans, classified under the Pongo genus, are an endangered non-human primate (NHP) species. Derivation of induced pluripotent stem cells (iPSCs) represents a promising avenue for conserving the genetic resources of these animals. Earlier studies focused on deriving orangutan iPSCs (o-iPSCs) from Sumatran orangutans (Pongo abelii). To date, no reports specifically target the other Critically Endangered species in the Pongo genus, the Bornean orangutans (Pongo pygmaeus). Methods: Using Sendai virus-mediated Yamanaka factor-based reprogramming of peripheral blood mononuclear cells to generate iPSCs (bo-iPSCs) from a female captive Bornean orangutan. In this study, we evaluate the colony morphology, pluripotent markers, X chromosome activation status, and transcriptomic profile of the bo-iPSCs to demonstrate the pluripotency of iPSCs from Bornean orangutans. Results: The bo-iPSCs were successfully derived from Bornean orangutans, using Sendai virus-mediated Yamanaka factor-based reprogramming of peripheral blood mononuclear cells. When a modified 4i/L/A (m4i/L/A) culture system was applied to activate the WNT signaling pathway in these bo-iPSCs, the derived cells (m-bo-iPSCs) manifested characteristics akin to human naive pluripotent stem cells, including high expression levels of KLF17, DNMT3L, and DPPA3/5, as well as the X chromosome reactivation. Comparative RNA-seq analysis positioned the m-bo-iPSCs between human naive and formative pluripotent states. Furthermore, the m-bo-iPSCs express differentiation capacity into all three germlines, evidenced by controlled in vitro embryoid body formation assay. Discussion: Our work establishes a novel approach to preserve the genetic diversity of endangered Bornean orangutans while offering insights into primate stem cell pluripotency. In the future, derivation of the primordial germ cell-like cells (PGCLCs) from m-bo-iPSCs is needed to demonstrate the further specific application in species preservation and broaden the knowledge of primordial germ cell specification across species.

To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences.

BACKGROUND: Taxonomic assignment is a vital step in the analytic pipeline of bacterial 16S ribosomal RNA (rRNA) sequencing. Over the past decade, most research in this field used next-generation sequencing technology to target V3∼V4 regions to analyze bacterial composition. However, focusing on only one or two hypervariable regions limited the taxonomic resolution to the species level. In recent years, third-generation sequencing technology has allowed researchers to easily access full-length prokaryotic 16S sequences and presented an opportunity to attain greater taxonomic depth. However, the accuracy of current taxonomic classifiers in analyzing 16S full-length sequence analysis remains unclear. OBJECTIVE: The purpose of this study is to compare the accuracy of several widely-used 16S sequence classifiers and to indicate the most suitable 16S training dataset for each classifier. METHODS: Both curated 16S full-length sequences and cross-validation datasets were used to validate the performance of seven classifiers, including QIIME2, mothur, SINTAX, SPINGO, Ribosomal Database Project (RDP), IDTAXA, and Kraken2. Different sequence training datasets, such as SILVA, Greengenes, and RDP, were used to train the classification models. RESULTS: The accuracy of each classifier to the species levels were illustrated. According to the experimental results, using RDP sequences as the training data, SINTAX and SPINGO provided the highest accuracy, and were recommended for the task of classifying prokaryotic 16S full-length rRNA sequences. CONCLUSION: The performance of the classifiers was affected by sequence training datasets. Therefore, different classifiers should use the most suitable 16S training data to improve the accuracy and taxonomy resolution in the taxonomic assignment.