scholarly journals Metagenomic Noncoding RNA Profiling and Biomarker Discovery

2020 ◽  
Author(s):  
Ben Liu ◽  
Sirisha Thippabhotla ◽  
Jun Zhang ◽  
Cuncong Zhong

AbstractNoncoding RNA plays important regulatory and functional roles in microorganisms, such as gene expression regulation, signaling, protein synthesis, and RNA processing. Given its essential role in microbial physiology, it is natural to question whether we can use noncoding RNAs as biomarkers to distinguish among environments under different biological conditions, such as those between healthy versus disease status. The current metagenomic sequencing technology primarily generates short reads, which contain incomplete structural information that may complicate noncoding RNA homology detection. On the other hand, de novo assembly of the metagenomics sequencing data remains fragmentary and has a risk of missing low-abundant noncoding RNAs. To tackle these challenges, we have developed DRAGoM (Detection of RNA using Assembly Graph from Metagenomics data), a novel noncoding RNA homology search algorithm. DRAGoM operates on a metagenome assembly graph, rather than on unassembled reads or assembled contigs. Our benchmark experiments show DRAGoM’s improved performance and robustness over the traditional approaches. We have further demonstrated DRAGoM’s real-world applications in disease characterization via analyzing a real case-control gut microbiome dataset for Type-2 diabetes (T2D). DRAGoM revealed potential ncRNA biomarkers that can clearly separate the T2D gut microbiome from those of healthy controls. DRAGoM is freely available from https://github.com/benliu5085/DRAGoM.

2018 ◽  
Vol 92 (7) ◽  
Author(s):  
Dongcheng Liu ◽  
Yan Wang ◽  
Yan Yuan

ABSTRACTKaposi's sarcoma-associated herpesvirus (KSHV) lytic replication and constant primary infection of fresh cells are crucial for viral tumorigenicity. The virus-encoded bZIP family protein K8 plays an important role in viral DNA replication in both viral reactivation andde novoinfection. The mechanism underlying the functional role of K8 in the viral life cycle is elusive. Here, we report that K8 is an RNA binding protein that also associates with many other proteins, including other RNA binding proteins. Many protein-protein interactions involving K8 are mediated by RNA. Using a UVcross-linking andimmunoprecipitation (CLIP) procedure combined with high-throughput sequencing, RNAs that are associated with K8 in BCBL-1 cells were identified, including both viral (PAN, T1.4, T0.7, etc.) and cellular (MALAT-1, MRP, 7SK, etc.) RNAs. An RNA binding motif in K8 was defined, and mutation of the motif abolished the ability of K8 to bind to many noncoding RNAs, as well as viral DNA replication duringde novoinfection, suggesting that the K8 functions in viral replication are carried out through RNA association. The functions of K8 and associated T1.4 RNA were investigated in detail, and the results showed that T1.4 mediates the binding of K8 to ori-Lyt DNA. The T1.4-K8 complex physically bound to KSHV ori-Lyt DNA and recruited other proteins and cofactors to assemble a replication complex. Depletion of T1.4 abolished DNA replication in primary infection. These findings provide mechanistic insights into the role of K8 in coordination with T1.4 RNA in regulating KSHV DNA replication duringde novoinfection.IMPORTANCEGenomewide analyses of the mammalian transcriptome revealed that a large proportion of sequence previously annotated as noncoding regions is actually transcribed and gives rise to stable RNAs. The emergence of a large number of noncoding RNAs suggests that functional RNA-protein complexes, e.g., ribosomes or spliceosomes, are not ancient relics of the last ribo-organism but would be well adapted to a regulatory role in biology. K8 has been puzzling because of its unique characteristics, such as multiple regulatory roles in gene expression and DNA replication without DNA binding capability. This study reveals the mechanism underlying its regulatory role by demonstrating that K8 is an RNA binding protein that binds to DNA and initiates DNA replication in coordination with a noncoding RNA. It is suggested that many K8 functions, if not all, are carried out through its associated RNAs.


2021 ◽  
Vol 118 (30) ◽  
pp. e2100709118
Author(s):  
Kezhi Zheng ◽  
Lili Wang ◽  
Longjun Zeng ◽  
Dachao Xu ◽  
Zhongxin Guo ◽  
...  

RNA-directed DNA methylation (RdDM) functions in de novo methylation in CG, CHG, and CHH contexts. Here, we performed map-based cloning of OsNRPE1, which encodes the largest subunit of RNA polymerase V (Pol V), a key regulator of gene silencing and reproductive development in rice. We found that rice Pol V is required for CHH methylation on RdDM loci by transcribing long noncoding RNAs. Pol V influences the accumulation of 24-nucleotide small interfering RNAs (24-nt siRNAs) in a locus-specific manner. Biosynthesis of 24-nt siRNAs on loci with high CHH methylation levels and low CG and CHG methylation levels tends to depend on Pol V. In contrast, low methylation levels in the CHH context and high methylation levels in CG and CHG contexts predisposes 24-nt siRNA accumulation to be independent of Pol V. H3K9me1 and H3K9me2 tend to be enriched on Pol V–independent 24-nt siRNA loci, whereas various active histone modifications are enriched on Pol V–dependent 24-nt siRNA loci. DNA methylation is required for 24-nt siRNAs biosynthesis on Pol V–dependent loci but not on Pol V–independent loci. Our results reveal the function of rice Pol V for long noncoding RNA production, DNA methylation, 24-nt siRNA accumulation, and reproductive development.


Author(s):  
Kazuaki Matoba ◽  
Nobuo N Noda

Summary Autophagy, which is an evolutionarily conserved intracellular degradation system, involves de novo generation of autophagosomes that sequester and deliver diverse cytoplasmic materials to the lysosome for degradation. Autophagosome formation is mediated by approximately 20 core autophagy-related (Atg) proteins, which collaborate to mediate complicated membrane dynamics during autophagy. To elucidate the molecular functions of these Atg proteins in autophagosome formation, many researchers have tried to determine the structures of Atg proteins by using various structural biological methods. Although not sufficient, the basic structural catalog of all core Atg proteins was established. In this review article, we summarize structural biological studies of core Atg proteins, with an emphasis on recently unveiled structures, and describe the mechanistic breakthroughs in autophagy research that have derived from new structural information.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Feifei Zhang ◽  
Hui Wang ◽  
Jiang Yu ◽  
Xueqing Yao ◽  
Shibin Yang ◽  
...  

AbstractDe novo and acquired resistance, which are mainly mediated by genetic alterations, are barriers to effective routine chemotherapy. However, the mechanisms underlying gastric cancer (GC) resistance to chemotherapy are still unclear. We showed that the long noncoding RNA CRNDE was related to the chemosensitivity of GC in clinical samples and a PDX model. CRNDE was decreased and inhibited autophagy flux in chemoresistant GC cells. CRNDE directly bound to splicing protein SRSF6 to reduce its protein stability and thus regulate alternative splicing (AS) events. We determined that SRSF6 regulated the PICALM exon 14 skip splice variant and triggered a significant S-to-L isoform switch, which contributed to the expression of the long isoform of PICALM (encoding PICALML). Collectively, our findings reveal the key role of CRNDE in autophagy regulation, highlighting the significance of CRNDE as a potential prognostic marker and therapeutic target against chemoresistance in GC.


Cells ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 109
Author(s):  
Álvaro M. Martins ◽  
Cátia C. Ramos ◽  
Daniela Freitas ◽  
Celso A. Reis

Glycans are major constituents of extracellular vesicles (EVs). Alterations in the glycosylation pathway are a common feature of cancer cells, which gives rise to de novo or increased synthesis of particular glycans. Therefore, glycans and glycoproteins have been widely used in the clinic as both stratification and prognosis cancer biomarkers. Interestingly, several of the known tumor-associated glycans have already been identified in cancer EVs, highlighting EV glycosylation as a potential source of circulating cancer biomarkers. These particles are crucial vehicles of cell–cell communication, being able to transfer molecular information and to modulate the recipient cell behavior. The presence of particular glycoconjugates has been described to be important for EV protein sorting, uptake and organ-tropism. Furthermore, specific EV glycans or glycoproteins have been described to be able to distinguish tumor EVs from benign EVs. In this review, the application of EV glycosylation in the development of novel EV detection and capture methodologies is discussed. In addition, we highlight the potential of EV glycosylation in the clinical setting for both cancer biomarker discovery and EV therapeutic delivery strategies.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.


2021 ◽  
Vol 22 (S10) ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.


Oncogene ◽  
2021 ◽  
Vol 40 (17) ◽  
pp. 3164-3179
Author(s):  
Yang Liu ◽  
Tianchi Tang ◽  
Xiaosheng Yang ◽  
Peng Qin ◽  
Pusen Wang ◽  
...  

AbstractPancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies and rapidly progressive diseases. Exosomes and long noncoding RNAs (lncRNAs) are emerging as vital mediators in tumor cells and their microenvironment. However, the detailed roles and mechanisms of exosomal lncRNAs in PDAC progression remain unknown. Here, we aimed to clarify the clinical significance and mechanisms of exosomal lncRNA 01133 (LINC01133) in PDAC. We analyzed the expression of LINC01133 in PDAC and found that exosomal LINC01133 expression was high and positively correlated with higher TNM stage and poor overall survival rate of PDAC patients. Further research demonstrated that Periostin could increase exosome secretion and then enhance LINC01133 expression. In addition, Periostin increased p-EGFR, p-Erk, and c-myc expression, and c-myc could bind to the LINC01133 promoter region. These findings suggested that LINC01133 can be regulated by Periostin via EGFR pathway activity. We also observed that LINC01133 promoted the proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) of pancreatic cancer cells. We subsequently evaluated the effect of LINC01133 on the Wnt/β-catenin pathway and confirmed that LINC01133 can interact with Enhancer Of Zeste Homolog 2 (EZH2) and then promote H3K27 trimethylation. This can further silence AXIN2 and suppress GSK3 activity, ultimately activating β-catenin. Collectively, these data indicate that exosomal LINC01133 plays an important role in pancreatic tumor progression, and targeting LINC01133 may provide a potential treatment strategy for PDAC.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moein Dehbashi ◽  
Zohreh Hojati ◽  
Majid Motovali-bashi ◽  
Mazdak Ganjalikhani-Hakemi ◽  
Akihiro Shimosaka ◽  
...  

AbstractCancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Zicheng Zhang ◽  
Congcong Yan ◽  
Ke Li ◽  
Siqi Bao ◽  
Lei Li ◽  
...  

AbstractThe emerging field of long noncoding RNA (lncRNA)-immunity has provided a new perspective on cancer immunity and immunotherapies. The lncRNA modifiers of infiltrating immune cells in the tumor immune microenvironment (TIME) and their impact on tumor behavior and disease prognosis remain largely uncharacterized. In the present study, a systems immunology framework integrating the noncoding transcriptome and immunogenomics profiles of 9549 tumor samples across 30 solid cancer types was used, and 36 lncRNAs were identified as modifier candidates underlying immune cell infiltration in the TIME at the pan-cancer level. These TIME lncRNA modifiers (TIL-lncRNAs) were able to subclassify various tumors into three de novo pan-cancer subtypes characterized by distinct immunological features, biological behaviors, and disease prognoses. Finally, a TIL-lncRNA-derived immune state index (TISI) that was reflective of immunological and oncogenic states but also predictive of patients’ prognosis was proposed. Furthermore, the TISI provided additional prognostic value for existing tumor immunological and molecular subtypes. By applying the TISI to tumors from different clinical immunotherapy cohorts, the TISI was found to be significantly negatively correlated with immune-checkpoint genes and to have the ability to predict the effectiveness of immunotherapy. In conclusion, the present study provided comprehensive resources and insights for future functional and mechanistic studies on lncRNA-mediated cancer immunity and highlighted the potential of the clinical application of lncRNA-based immunotherapeutic strategies in precision immunotherapy.


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