Genome-wide prediction of cauliflower miRNAs and lncRNAs and their roles in post-transcriptional gene regulation.

MAIN CONCLUSION: We have predicted miRNAs, their targets and lncRNAs from the genome of Brassica oleracea along with their functional annotation. Selected miRNAs and their targets are experimentally validated. Roles of these non-coding RNAs in post-transcriptional gene regulation are also deciphered. Cauliflower (Brassica oleracea var. Botrytis) is an important vegetable crop for its dietary and medicinal values with rich source of vitamins, dietary fibers, flavonoids and antioxidants. MicroRNAs (miRNAs) are small non-coding RNAs (ncRNAs), which regulate gene expression by inhibiting translation or by degrading messenger RNAs (mRNAs). On the other hand, long non-coding RNAs (lncRNAs) are responsible for the up regulation and the down regulation of transcription. Although the genome of cauliflower is reported, yet the roles of these ncRNAs in post-transcriptional gene regulation (PTGR) remain elusive. In this study, we have computationally predicted 355 miRNAs, of which 280 miRNAs are novel compared to miRBase 22.1. All the predicted miRNAs belong to 121 different families. We have also identified 934 targets of 125 miRNAs along with their functional annotation. These targets are further classified into biological processes, molecular functions and cellular components. Moreover, we have predicted 634 lncRNAs, of which 61 are targeted by 30 novel miRNAs. Randomly chosen 10 miRNAs and 10 lncRNAs are experimentally validated. Five miRNA targets including squamosa promoter-binding-like protein 9, homeobox-leucine zipper protein HDG12-like, NAC domain-containing protein 100, CUP-SHAPED COTYLEDON 1 and kinesin-like protein NACK2 of four miRNAs including bol-miR156a, bol-miR162a, bol-miR164d and bol-miR2673 are also experimentally validated. We have built network models of interactions between miRNAs and their target mRNAs, as well as between miRNAs and lncRNAs. Our findings enhance the knowledge of non-coding genome of cauliflower and their roles in PTGR, and might play important roles in improving agronomic traits of this economically important crop.

Optimizing Fecal Occult Blood Test (FOBT) Colorectal Cancer Screening Using Gut Bacteriome as a Biomarker.

BACKGROUND: Colorectal cancer (CRC) is a major cause of cancer mortality in the world. One of the most widely used screening tests for CRC is the immunochemical fecal occult blood test (iFOBT), which detects human hemoglobin from patient's stool sample. Although it is highly efficient in detecting blood from patients with gastro-intestinal lesions, such as polyps and cancers, the iFOBT has a high rate of false positive discovery. Recent studies suggested gut bacteria as a promising noninvasive biomarker for improving the diagnosis of CRC. In this study, we examined the composition of gut bacteria using iFOBT leftover from patients undergoing screening test along with a colonoscopy. METHODS: After collecting data from more than 800 patients, we considered 4 groups for this study. The first and second groups were respectively "healthy" in which the patients had either no blood in their stool or had blood but no lesions. The third and fourth groups of patients had both blood in their stools with precancerous and cancerous lesions and considered either as low-grade and high-grade lesion groups, respectively. An amplification of 16S rRNA (V4 region) gene was performed, followed by sequencing along with various statistical and bioinformatic analysis. RESULTS: We analyzed the composition of the gut bacteriome at phylum, class, genus, and species levels. Although members of the Firmicute phylum increased in the 3 groups compared to healthy patients, the phylum Actinobacteriota was found to decrease. Moreover, Blautia obeum and Anaerostipes hadrus from the phylum Firmicutes were increased and Collinsella aerofaciens from phylum Actinobacteriota was found decreased when healthy group is compared to the patients with high-grade lesions. Finally, among the 5 machine learning algorithms used to perform our analysis, both elastic net (AUC > 0.7) and random forest (AUC > 0.8) performs well in differentiating healthy patients from 3 other patient groups having blood in their stool. CONCLUSION: Our study integrates the iFOBT screening tool with gut bacterial composition to improve the prediction of CRC lesions.