One of the most striking examples of small RNA regulation of gene expression is the process of RNA editing in the mitochondria of trypanosomes. In these parasites, RNA editing involves extensive uridylate insertions and deletions within most of the mitochondrial messenger RNAs (mRNAs). Over 1200 small guide RNAs (gRNAs) are predicted to be responsible for directing the sequence changes that create start and stop codons, correct frameshifts and for many of the mRNAs generate most of the open reading frame. In addition, alternative editing creates the opportunity for unprecedented protein diversity. In Trypanosoma brucei, the vast majority of gRNAs are transcribed from minicircles, which are approximately one kilobase in size, and encode between three and four gRNAs. The large number (5000-10,000) and their concatenated structure make them difficult to sequence. To identify the complete set of gRNAs necessary for mRNA editing in T. brucei, we used Illumina deep sequencing of purified gRNAs from the procyclic stage. We report a near complete set of gRNAs needed to direct the editing of the mRNAs.

Gene assembly, which recovers gene segments from short reads, is an important step in functional analysis of next-generation sequencing data. Lacking quality reference genomes, de novo assembly is commonly used for RNA-Seq data of non-model organisms and metagenomic data. However, heterogeneous sequence coverage caused by heterogeneous expression or species abundance, similarity between isoforms or homologous genes, and large data size all pose challenges to de novo assembly. As a result, existing assembly tools tend to output fragmented contigs or chimeric contigs, or have high memory footprint. In this work, we introduce a targeted gene assembly program SAT-Assembler, which aims to recover gene families of particular interest to biologists. It addresses the above challenges by conducting family-specific homology search, homology-guided overlap graph construction, and careful graph traversal. It can be applied to both RNA-Seq and metagenomic data. Our experimental results on an Arabidopsis RNA-Seq data set and two metagenomic data sets show that SAT-Assembler has smaller memory usage, comparable or better gene coverage, and lower chimera rate for assembling a set of genes from one or multiple pathways compared with other assembly tools. Moreover, the family-specific design and rapid homology search allow SAT-Assembler to be naturally compatible with parallel computing platforms. The source code of SAT-Assembler is available at https://sourceforge.net/projects/sat-assembler/. The data sets and experimental settings can be found in supplementary material.

We advocate for a tRNA- rather than an mRNA-centric model for evolution of the genetic code. The mechanism for evolution of cloverleaf tRNA provides a root sequence for radiation of tRNAs and suggests a simplified understanding of code evolution. To analyze code sectoring, rooted tRNAomes were compared for several archaeal and one bacterial species. Rooting of tRNAome trees reveals conserved structures, indicating how the code was shaped during evolution and suggesting a model for evolution of a LUCA tRNAome tree. We propose the polyglycine hypothesis that the initial product of the genetic code may have been short chain polyglycine to stabilize protocells. In order to describe how anticodons were allotted in evolution, the sectoring-degeneracy hypothesis is proposed. Based on sectoring, a simple stepwise model is developed, in which the code sectors from a 1→4→8→∼16 letter code. At initial stages of code evolution, we posit strong positive selection for wobble base ambiguity, supporting convergence to 4-codon sectors and ∼16 letters. In a later stage, ∼5-6 letters, including stops, were added through innovating at the anticodon wobble position. In archaea and bacteria, tRNA wobble adenine is negatively selected, shrinking the maximum size of the primordial genetic code to 48 anticodons. Because 64 codons are recognized in mRNA, tRNA-mRNA coevolution requires tRNA wobble position ambiguity leading to degeneracy of the code.

During an evolving outbreak or pandemic, non-pharmaceutical interventions (NPIs) including physical distancing, isolation, and mask use may flatten the peak in communities. However, these strategies rely on community understanding and motivation to engage to ensure appropriate compliance and impact. To support current activities for COVID-19, the objectives of this narrative review was to identify the key determinants impacting on engagement.

Adipocyte-derived adiponectin (APN) is involved in the protection against cardiovascular disease, but the endogenous APN and its receptor expression in the perivascular adipocytes and their role in hypertensive vascular injury remain unclear. The present study aimed to detect endogenous APN and its receptor expression and their protective effects against hypertensive vascular injury. APN was mainly expressed in the perivascular adipocytes, while its receptors AdipoR1 and AdipoR2 were ubiquitously expressed in the blood vessels. Angiotensin II (Ang II)‑induced hypertension resulted in a significant decrease of APN and AdipoR1 and AdipoR2 in the perivascular adipocytes and vascular cells. The migration assay used demonstrated that APN attenuated Ang II‑induced vascular smooth muscle cells migration and p38 phosphorylation Furthermore, the in vivo study demonstrated that APN receptor agonist AdipoRon attenuated Ang II‑induced hypertensive vascular hypertrophy and fibrosis. Taken together, the present study indicated that perivascular adipocytes‑derived APN attenuated hypertensive vascular injury possibly via its receptor‑mediated inhibition of p38 signaling pathway.

Mobilization failure may occur when the conventional hematopoietic stem cells (HSCs) mobilization agent granulocyte colony-stimulating factor (G-CSF) is used alone, new regimens were developed to improve mobilization efficacy. Multiple studies have been performed to investigate the efficacy of these regimens via animal models, but the results are inconsistent. We aim to compare the efficacy of different HSC mobilization regimens and identify new promising regimens with a network meta-analysis of preclinical studies.

Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction.

Viruses are the most abundant form of life on earth and play important roles in a broad range of ecosystems. Currently, two methods, whole genome shotgun metagenome (WGSM) and viral-like particle enriched metagenome (VLPM) sequencing, are widely applied to compare viruses in various environments. However, there is no critical assessment of their performance in recovering viruses and biological interpretation in comparative viral metagenomic studies. To fill this gap, we applied the two methods to investigate the stool virome in hepatocellular carcinoma (HCC) patients and healthy controls. Both WGSM and VLPM methods can capture the major diversity patterns of alpha and beta diversities and identify the altered viral profiles in the HCC stool samples compared with healthy controls. Viral signatures identified by both methods showed reductions of Faecalibacterium virus Taranis in HCC patients' stool. Ultra-deep sequencing recovered more viruses in both methods, however, generally, 3 or 5 Gb were sufficient to capture the non-fragmented long viral contigs. More lytic viruses were detected than lysogenetic viruses in both methods, and the VLPM can detect the RNA viruses. Using both methods would identify shared and specific viral signatures and would capture different parts of the total virome.

As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs.

Metagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult. Generic de novo assembly tools are not optimized for assembling 16S rRNA genes. In this work, we introduce a targeted rRNA assembly tool, REAGO (REconstruct 16S ribosomal RNA Genes from metagenOmic data). It addresses the above challenges by combining secondary structure-aware homology search, zproperties of rRNA genes and de novo assembly. Our experimental results show that our tool can correctly recover more rRNA genes than several popular generic metagenomic assembly tools and specially designed rRNA construction tools.

The number of transcriptomic sequencing projects of various non-model organisms is still accumulating rapidly. As non-coding RNAs (ncRNAs) are highly abundant in living organism and play important roles in many biological processes, identifying fragmentary members of ncRNAs in small RNA-seq data is an important step in post-NGS analysis. However, the state-of-the-art ncRNA search tools are not optimized for next-generation sequencing (NGS) data, especially for very short reads. In this work, we propose and implement a comprehensive ncRNA classification tool (RNA-CODE) for very short reads. RNA-CODE is specifically designed for ncRNA identification in NGS data that lack quality reference genomes. Given a set of short reads, our tool classifies the reads into different types of ncRNA families. The classification results can be used to quantify the expression levels of different types of ncRNAs in RNA-seq data and ncRNA composition profiles in metagenomic data, respectively. The experimental results of applying RNA-CODE to RNA-seq of Arabidopsis and a metagenomic data set sampled from human guts demonstrate that RNA-CODE competes favorably in both sensitivity and specificity with other tools. The source codes of RNA-CODE can be downloaded at http://www.cse.msu.edu/~chengy/RNA_CODE.

Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common malignancies around the world. It has been verified that the expression of SOX18 is correlated to poor clinical prognosis in patients with ovarian cancer, non-small cell lung cancer, or breast invasive ductal carcinoma. However, the expression pattern and biological function of SOX18 in HCC tissues remains unclear. In this study, we set out to investigate the associated biological function and potential molecular mechanism of the SOX18 gene in HCC cells. MATERIAL AND METHODS The mRNA and protein expression levels of experimental related genes were detected by real-time polymerase chain reaction and western blotting assay, respectively. The MTT method was used to assess cell viability, and cell apoptosis analysis was performed by means of FACScan flow cytometry. Wound-healing assay and Transwell analysis were performed to evaluate the ability of cell migration and invasiveness, respectively. RESULTS SOX18 was highly expressed in various HCC cell lines. In addition, SOX18 promoted cell viability, migration, and invasion and simultaneously induce cell apoptosis. SOX18 promoted epithelial-to-mesenchymal transition (EMT) progression, and SOX18 downregulation activated the autophagy signaling pathway AMPK/mTOR in HCC cells. CONCLUSIONS SOX18 downregulation in HCC cells suppressed cell viability and metastasis, induced cell apoptosis and hindered the occurrence and progression of tumor cells by participating in the EMT process and regulating the autophagy signaling pathway AMPK/mTOR.

Background. Liver fibrosis is a chronic progressive liver disease, but no established effective treatment exists except for liver transplantation. The present study was designed to investigate the effect of human placenta mesenchymal stem cells (hPMSCs) expressing green fluorescent protein (GFP) on carbon tetrachloride- (CCl4-) induced liver fibrosis in rats. Methods. Liver fibrosis was induced by subcutaneous injection with CCl4; hPMSCs were directly transplanted into rats through the caudal vein. The therapeutic efficacy of hPMSCs on liver fibrosis was measured by liver function tests, liver elastography, histopathology, Masson's trichrome and Sirius red staining, and immunohistochemical studies. The expression levels of fibrotic markers, transforming growth factor β1 (TGF-β1) and α-smooth muscle actin (α-SMA), were assessed using real-time polymerase chain reaction. Results. We demonstrated that liver fibrosis was significantly dampened in the hPMSC transplantation group according to the Laennec fibrosis scoring system and histological data. The Sirius red-stained collagen area and the elastography score were significantly reduced in the hPMSC-treated group. Meanwhile, hPMSC administration significantly decreased TGF-β1 and α-SMA expression and enhanced liver functions in CCl4-induced fibrotic rats. Conclusion. This study indicates that transplantation of hPMSCs could repair liver fibrosis induced by CCl4 in rats, which may serve as a valuable therapeutic approach to treat liver diseases.

Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various species. However, existing tools are not optimized for identifying genes with highly variable GC content. In addition, some genes in grass genomes exhibit a sharp 5 '- 3' decreasing GC content gradient, which is not carefully modeled by available gene prediction tools. Thus, there is still room to improve the sensitivity and accuracy for predicting genes with GC gradients.

With advances in library construction protocols and next-generation sequencing technologies, viral metagenomic sequencing has become the major source for novel virus discovery. Conducting taxonomic classification for metagenomic data is an important means to characterize the viral composition in the underlying samples. However, RNA viruses are abundant and highly diverse, jeopardizing the sensitivity of comparison-based classification methods. To improve the sensitivity of read-level taxonomic classification, we developed an RNA-dependent RNA polymerase (RdRp) gene-based read classification tool RdRpBin. It combines alignment-based strategy with machine learning models in order to fully exploit the sequence properties of RdRp. We tested our method and compared its performance with the state-of-the-art tools on the simulated and real sequencing data. RdRpBin competes favorably with all. In particular, when the query RNA viruses share low sequence similarity with the known viruses ($\sim 0.4$), our tool can still maintain a higher F-score than the state-of-the-art tools. The experimental results on real data also showed that RdRpBin can classify more RNA viral reads with a relatively low false-positive rate. Thus, RdRpBin can be utilized to classify novel and diverged RNA viruses.

Rituximab combined with chemotherapy is the first-line induction therapy of CD20 positive B-cell non-Hodgkin lymphomas (CD20+ B-NHL). Recently new anti-CD20 monoclonal antibodies (mAbs) have been developed, but their efficacy and safety compared with rituximab are still controversial. We searched MEDLINE, Embase, and Cochrane Library for eligible randomized controlled trials (RCTs) that compared new anti-CD20 mAbs with rituximab in induction therapy of B-NHL. The primary outcomes are progression-free survival (PFS) and overall survival (OS), additional outcomes include event-free survival (EFS), disease-free survival (DFS), overall response rate (ORR), complete response rate (CRR) and incidences of adverse events (AEs). Time-to-event data were pooled as hazard ratios (HRs) using the generic inverse-variance method and dichotomous outcomes were pooled as odds ratios (ORs) using the Mantel-Haenszel method with their respective 95% confidence interval (CI). Eleven RCTs comprising 5261 patients with CD20+ B-NHL were included. Compared with rituximab, obinutuzumab significantly prolonged PFS (HR 0.84, 95% CI 0.73-0.96, P = 0.01), had no improvement on OS, ORR, and CRR, but increased the incidences of serious AEs (OR 1.29, 95% CI 1.13-1.48, P < 0.001). Ofatumumab was inferior to rituximab in consideration of ORR (OR 0.73, 95% CI 0.55-0.96, P = 0.02), and had no significant differences with rituximab in regard to PFS, OS and CRR. 131I-tositumomab yielded similar PFS, OS, ORR and CRR with rituximab. 90Y-ibritumomab tiuxetan increased ORR (OR 3.07, 95% CI 1.47-6.43, P = 0.003), but did not improve PFS, DFS, OS and CRR compared with rituximab. In conclusion, compared with rituximab in induction therapy of CD20+ B-NHL, obinutuzumab significantly improves PFS but with higher incidence of AEs, ofatumumab decreases ORR, 90Y-ibritumomab tiuxetan increases ORR.

Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus-prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY's performance on short contigs is more stable than other tools.

Without relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses.

Sacred lotus (Nelumbo nucifera Gaertn.) is a basal eudicot plant with a unique lifestyle, physiological features, and evolutionary characteristics. Here we report the unique profile of transposable elements (TEs) in the genome, using a manually curated repeat library. TEs account for 59% of the genome, and hAT (Ac/Ds) elements alone represent 8%, more than in any other known plant genome. About 18% of the lotus genome is comprised of Copia LTR retrotransposons, and over 25% of them are associated with non-canonical termini (non-TGCA). Such high abundance of non-canonical LTR retrotransposons has not been reported for any other organism. TEs are very abundant in genic regions, with retrotransposons enriched in introns and DNA transposons primarily in flanking regions of genes. The recent insertion of TEs in introns has led to significant intron size expansion, with a total of 200 Mb in the 28 455 genes. This is accompanied by declining TE activity in intergenic regions, suggesting distinct control efficacy of TE amplification in different genomic compartments. Despite the prevalence of TEs in genic regions, some genes are associated with fewer TEs, such as those involved in fruit ripening and stress responses. Other genes are enriched with TEs, and genes in epigenetic pathways are the most associated with TEs in introns, indicating a dynamic interaction between TEs and the host surveillance machinery. The dramatic differential abundance of TEs with genes involved in different biological processes as well as the variation of target preference of different TEs suggests the composition and activity of TEs influence the path of evolution.