Jiaying Zheng

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Despite recent progress in our understanding of the association between the gut microbiome and inflammatory bowel disease (IBD), the role of microbiome biomarkers in IBD diagnosis remains underexplored. Here we developed a microbiome-based diagnostic test for IBD. By utilization of metagenomic data from 5,979 fecal samples with and without IBD from different geographies and ethnicities, we identified microbiota alterations in IBD and selected ten and nine bacterial species for construction of diagnostic models for ulcerative colitis and Crohn’s disease, respectively. These diagnostic models achieved areas under the curve >0.90 for distinguishing IBD from controls in the discovery cohort, and maintained satisfactory performance in transethnic validation cohorts from eight populations. We further developed a multiplex droplet digital polymerase chain reaction test targeting selected IBD-associated bacterial species, and models based on this test showed numerically higher performance than fecal calprotectin in discriminating ulcerative colitis and Crohn’s disease from controls. Here we discovered universal IBD-associated bacteria and show the potential applicability of a multibacteria biomarker panel as a noninvasive tool for IBD diagnosis. Using ethnically and geographically diverse metagenomic data, the authors identify microbiota alterations associated with inflammatory bowel disease (IBD). They discover universal IBD-associated bacteria, which serve as the basis for a multibacteria biomarker panel that could support a noninvasive tool for IBD diagnosis.

Many chemotherapeutic agents have been successfully used to treat hepatocellular carcinoma (HCC); however, the development of chemoresistance in liver cancer cells usually results in a relapse and worsening of prognosis. It has been demonstrated that DNA methylation and histone modification play crucial roles in chemotherapy resistance. Currently, extensive research has shown that there is another potential mechanism of gene expression control, which is mediated through the function of short noncoding RNAs, especially for microRNAs (miRNAs), but little is known about their roles in cancer cell drug resistance. In present study, by taking advantage of miRNA effects on the resistance of human hepatocellular carcinoma cells line to cisplatin, it has been demonstrated that miR-340 were significantly downregulated whereas Nrf2 was upregulated in HepG2/ CDDP (cisplatin) cells, compared with parental HepG2 cells. Bioinformatics analysis and luciferase assays of Nrf2-3'-untranslated region-based reporter constructor indicated that Nrf2 was the direct target gene of miR- 340, miR-340 mimics suppressing Nrf2-dependent antioxidant pathway and enhancing the sensitivity of HepG2/ CDDP cells to cisplatin. Interestingly, transfection with miR-340 mimics combined with miR-340 inhibitors reactivated the Nrf2 related pathway and restored the resistance of HepG2/CDDP cells to CDDP. Collectively, the results first suggested that lower expression of miR-340 is involved in the development of CDDP resistance in hepatocellular carcinoma cell line, at least partly due to regulating Nrf2-dependent antioxidant pathway.

Inflammatory bowel disease (IBD) is a chronic immune-mediated intestinal disease consisting of ulcerative colitis and Crohn's disease. Inflammatory bowel disease is believed to be developed as a result of interactions between environmental, immune-mediated and microbial factors in a genetically susceptible host. Recent advances in high-throughput sequencing technologies have aided the identification of consistent alterations of the gut microbiome in patients with IBD. Preclinical and murine models have also shed light on the role of beneficial and pathogenic bacteria in IBD. These findings have stimulated interest in development of non-invasive microbial and metabolite biomarkers for predicting disease risk, disease progression, recurrence after surgery and responses to therapeutics. This review briefly summarizes the current evidence on the role of gut microbiome in IBD pathogenesis and mainly discusses the latest literature on the utilization of potential microbial biomarkers in disease diagnosis and prognosis.

Abstract BACKGROUND Infiltrating ductal carcinoma (IDCA) is the most common form of invasive breast cancer. Immunohistochemistry (IHC) is widely used to analyze estrogen receptor 1 (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) that can help classify the tumor to guide the medical treatment. IHC examinations require experienced pathologists to provide interpretations that are subjective, thereby lowering the reproducibility of IHC-based diagnosis. In this study, we developed a 4-plex droplet digital PCR (ddPCR) for the simultaneous and quantitative analyses of estrogen receptor 1 (ESR1), progesterone receptor (PGR), erb-b2 receptor tyrosine kinase 2 (ERBB2), and pumilio RNA binding family member 1 (PUM1) expression levels in formalin-fixed paraffin-embedded (FFPE) samples. METHODS We evaluated the sensitivity, reproducibility, and linear dynamic range of 4-plex ddPCR. We applied this method to analyze 95 FFPE samples from patients with breast IDCA and assessed the agreement rates between ddPCR and IHC to evaluate its potential in classifying breast cancer subtypes. RESULTS The limits of quantification (LOQ) were 25, 50, 50, and 50 copies per reaction for ERBB2, ESR1, PGR, and PUM1, respectively. The dynamic ranges of ESR1, PGR, and PUM1 extended over 50–1600 copies per reaction and those of ERBB2 from 25 to 1600 copies per reaction. The concordance correlation coefficients between 4-plex ddPCR and IHC were 96.8%, 91.5%, and 85.1% for ERBB2, ESR1, and PGR, respectively. Receiver operating characteristic curve area under the curve values of 0.991, 0.977, and 0.920 were generated for ERBB2, ESR1, and PGR, respectively. CONCLUSIONS Evaluation of breast cancer biomarker status by 4-plex ddPCR was highly concordant with IHC in this study.