Clinical identification and microbiota analysis of Chlamydia psittaci- and Chlamydia abortus- pneumonia by metagenomic next-generation sequencing.

Introduction: Recently, the incidence of chlamydial pneumonia caused by rare pathogens such as C. psittaci or C. abortus has shown a significant upward trend. The non-specific clinical manifestations and the limitations of traditional pathogen identification methods determine that chlamydial pneumonia is likely to be poorly diagnosed or even misdiagnosed, and may further result in delayed treatment or unnecessary antibiotic use. mNGS's non-preference and high sensitivity give us the opportunity to obtain more sensitive detection results than traditional methods for rare pathogens such as C. psittaci or C. abortus. Methods: In the present study, we investigated both the pathogenic profile characteristics and the lower respiratory tract microbiota of pneumonia patients with different chlamydial infection patterns using mNGS. Results: More co-infecting pathogens were found to be detectable in clinical samples from patients infected with C. psittaci compared to C. abortus, suggesting that patients infected with C. psittaci may have a higher risk of mixed infection, which in turn leads to more severe clinical symptoms and a longer disease course cycle. Further, we also used mNGS data to analyze for the first time the characteristic differences in the lower respiratory tract microbiota of patients with and without chlamydial pneumonia, the impact of the pattern of Chlamydia infection on the lower respiratory tract microbiota, and the clinical relevance of these characteristics. Significantly different profiles of lower respiratory tract microbiota and microecological diversity were found among different clinical subgroups, and in particular, mixed infections with C. psittaci and C. abortus resulted in lower lung microbiota diversity, suggesting that chlamydial infections shape the unique lung microbiota pathology, while mixed infections with different Chlamydia may have important effects on the composition and diversity of the lung microbiota. Discussion: The present study provides possible evidences supporting the close correlation between chlamydial infection, altered microbial diversity in patients' lungs and clinical parameters associated with infection or inflammation in patients, which also provides a new research direction to better understand the pathogenic mechanisms of pulmonary infections caused by Chlamydia.

lncRNA pair as candidate diagnostic signature for colorectal cancer based on the within-sample relative expression levels.

Background: Early diagnosis of colorectal cancer could significantly improve the prognosis and reduce mortality. However, indeterminate diagnosis is often met in pathology diagnosis in biopsy samples. Abnormal expression of long non-coding RNA (lncRNA) is associated with the initiation and progression of colorectal cancer. It is of great value and clinical significance to explore lncRNAs as candidate diagnostic biomarkers in colorectal cancer. Methods: Based on the within-sample relative expression levels of lncRNA pairs, we identified a group of candidate diagnostic biomarkers for colorectal cancer. In addition, we validated it in independent datasets produced by different laboratories and different platforms. We also tested it in colorectal cancer tissue samples using quantitative real-time polymerase chain reaction (RT-qPCR). Results: A biomarker consisting of six lncRNA pairs including nine lncRNAs was identified for the diagnosis of colorectal cancer. For a total of 950 cancer samples and 247 non-cancer samples, both of the sensitivity and specificity could achieve approximately 90%. For adenoma samples, the accuracy could achieve 73%. For normal tissues from inflammatory bowel disease patients, 93% (14/15) were correctly classified as non-cancer. Furthermore, the lncRNA pair biomarker showed excellent performance in all clinical stages; even in the early stage, the accuracy could achieve 87% and 82% in stage I and II. Meanwhile, the biomarker was also robust to the microsatellite instability status. More importantly, we measured the biomarker in 35 colorectal cancer and 30 cancer-adjacent tissue samples using quantitative real-time polymerase chain reaction (RT-qPCR). The accuracy could achieve 93.3% (70/75). Specially, even in early-stage tumors (I and II), the accuracy could also achieve 90.9% (30/33). The enrichment analysis revealed that these lncRNAs were involved in highly associated cancer pathways and immune-related pathways. Immune analysis showed that these marker lncRNAs were associated with multiple immune cells, implying that they might be involved in the regulation of immune cell functions in colorectal cancer. Most of the biomarker lncRNAs were also differentially expressed between the mutant group and wild-type group of colorectal cancer driver genes. Conclusion: We identified and validated six lncRNA pairs including nine lncRNAs as a biomarker for assisting in the diagnosis of colorectal cancer.

Identification of the origin of brain metastases based on the relative methylation orderings of CpG sites.

Accurate diagnosis of the origin of brain metastases (BMs) is crucial for tailoring an effective therapy to improve patients' prognosis. BMs of unknown origin account for approximately 2-14% of patients with BMs. Hence, the aim of this study was to identify the original cancer type of BMs based on their DNA methylation profiles. The DNA methylation profiles of glioma (GM), BM, and seven other types of primary cancers were collected. In comparison with GM, the reversal CpG site pairs were identified for each of the seven other types of primary cancers based on the within-sample relative methylation orderings (RMOs) of the CpG sites. Then, using the reversal CpG site pairs, GMs were distinguished from BMs and the seven other types of primary cancers. All 61 of the GM samples were correctly identified as GM. The cancer type was also identified for the non-GM samples. For the seven other types of primary cancers, greater than 93% of samples of each cancer type were correctly identified as their corresponding cancer type, except for breast cancer, which had an 88% accuracy. For 133 BM samples, 132 BM samples were identified as non-GM, and 95% of the 133 BM samples were correctly classified into their corresponding original cancer types. The RMO-based method can accurately identify the origin of BMs, which is important for precision treatment.