UNAGI: Yeast Transcriptome Reconstruction and Gene Discovery Using Nanopore Sequencing.

Computational approaches are the main approaches used in genome annotation. However, accuracy is low. Untranslated regions are not identified, complex isoforms are not predicted correctly and discovery rate of noncoding RNA is low. RNA-seq has revolutionized transcriptome reconstruction over the last decade. However, fragmentation included in cDNA sequencing leads to information loss, requiring transcripts to be assembled and reconstructed, thus affecting the accuracy of reconstructed transcriptome. Recently, long-read sequencing has been introduced with technologies such as Oxford Nanopore sequencing. cDNA is sequenced directly without fragmentation producing long reads that don't need to be assembled keeping the transcript structure intact and increasing the accuracy of transcriptome reconstruction.Here we present a protocol and a pipeline to reconstruct the transcriptome of compact genomes including yeasts. It involves generating full-length cDNA and using Oxford Nanopore ligation-based sequencing kit to sequence multiple samples in the same run. The pipeline (1) strands the generated long reads, (2) corrects the reads by mapping them to the reference genome, (3) identifies transcripts including 5'UTR and 3'UTR, (4) profiles the isoforms, filtering out artifacts resulting from low accuracy in sequencing, and (5) improves accuracy of provided annotations. Using long reads improves the accuracy of transcriptome reconstruction and helps in discovering a significant number of novel RNAs.

Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1-V2 and V3-V4 primer sets.

BACKGROUND: 16S rRNA gene amplicon sequencing (16S analysis) is widely used to analyze microbiota with next-generation sequencing technologies. Here, we compared fecal 16S analysis data from 192 Japanese volunteers using the modified V1-V2 (V12) and the standard V3-V4 primer (V34) sets to optimize the gut microbiota analysis protocol. RESULTS: QIIME1 and QIIME2 analysis revealed a higher number of unclassified representative sequences in the V34 data than in the V12 data. The comparison of bacterial composition demonstrated that at the phylum level, Actinobacteria and Verrucomicrobia were detected at higher levels with V34 than with V12. Among these phyla, we observed higher relative compositions of Bifidobacterium and Akkermansia with V34. To estimate the actual abundance, we performed quantitative real-time polymerase chain reaction (qPCR) assays for Akkermansia and Bifidobacterium. We found that the abundance of Akkermansia as detected by qPCR was close to that in V12 data, but was markedly lower than that in V34 data. The abundance of Bifidobacterium detected by qPCR was higher than that in V12 and V34 data. CONCLUSIONS: These results indicate that the bacterial composition derived from the V34 region might differ from the actual abundance for specific gut bacteria. We conclude that the use of the modified V12 primer set is more desirable in the 16S analysis of the Japanese gut microbiota.

selectBoost: a general algorithm to enhance the performance of variable selection methods.

MOTIVATION: With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting. RESULTS: In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering. AVAILABILITY AND IMPLEMENTATION: Code is available as the SelectBoost package on the CRAN, https://cran.r-project.org/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https://cran.r-project.org/package=Patterns. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNAGI: an automated pipeline for nanopore full-length cDNA sequencing uncovers novel transcripts and isoforms in yeast.

Sequencing the entire RNA molecule leads to a better understanding of the transcriptome architecture. SMARTer (Switching Mechanism at 5'-End of RNA Template) is a technology aimed at generating full-length cDNA from low amounts of mRNA for sequencing by short-read sequencers such as those from Illumina. However, short read sequencing such as Illumina technology includes fragmentation that results in bias and information loss. Here, we built a pipeline, UNAGI or UNAnnotated Gene Identifier, to process long reads obtained with nanopore sequencing and compared this pipeline with the standard Illumina pipeline by studying the Saccharomyces cerevisiae transcriptome in full-length cDNA samples generated from two different biological samples: haploid and diploid cells. Additionally, we processed the long reads with another long read tool, FLAIR. Our strand-aware method revealed significant differential gene expression that was masked in Illumina data by antisense transcripts. Our pipeline, UNAGI, outperformed the Illumina pipeline and FLAIR in transcript reconstruction (sensitivity and specificity of 80% and 40% vs. 18% and 34% and 79% and 32%, respectively). Moreover, UNAGI discovered 3877 unannotated transcripts including 1282 intergenic transcripts while the Illumina pipeline discovered only 238 unannotated transcripts. For isoforms profiling, UNAGI also outperformed the Illumina pipeline and FLAIR in terms of sensitivity (91% vs. 82% and 63%, respectively). But the low accuracy of nanopore sequencing led to a closer gap in terms of specificity with Illumina pipeline (70% vs. 63%) and to a huge gap with FLAIR (70% vs 0.02%).

miRNAs Regulate Cytokine Secretion Induced by Phosphorylated S100A8/A9 in Neutrophils.

The release of cytokines by neutrophils constitutes an essential process in the development of inflammation by recruiting and activating additional cells. Neutrophils are also able to secrete a complex of S100A8 and S100A9 proteins (S100A8/A9), which can amplify the general inflammatory state of the host and is involved in the pathogenesis of several chronic inflammatory diseases, such as rheumatoid arthritis (RA). S100A8/A9 have received renewed attention due to their susceptibility to several function-altering post-translational modifications. In that context, it has been recently demonstrated that only the phosphorylated form of S100A8/A9 (S100A8/A9-P) is able to induce the secretion of several cytokines in neutrophils. Here, we investigate the mechanism by which this post-translational modification of S100A8/A9 can regulate the extracellular activity of the protein complex and its impact on the inflammatory functions of neutrophils. We found that S100A8/A9-P are present in large amounts in the synovial fluids from RA patients, highlighting the importance of this form of S100A8/A9 complex in the inflammation process. Using miRNA-sequencing on S100A8/A9-P-stimulated differentiated HL-60 cells, we identified a dysregulation of miR-146a-5p and miR-155-5p expression through TRL4 signaling pathways. Our data reveal that overexpression of these miRNAs in neutrophil-like cells reduces S100A8/A9-P-mediated secretion of pro-inflammatory cytokines.

Comprehensive subspecies identification of 175 nontuberculous mycobacteria species based on 7547 genomic profiles.

The prevalence of nontuberculous mycobacteria (NTM) pulmonary diseases has been increasing worldwide. NTM consist of approximately 200 species and distinguishing between them at the subspecies level is critical to treatment. In this study, we sequenced 63 NTM genomes, 27 of which were newly determined, by hybrid assembly using sequencers from Illumina and Oxford Nanopore Technologies (ONT). This analysis expanded the available genomic data to 175 NTM species and redefined their subgenus classification. We also developed a novel multi-locus sequence typing (MLST) database based on 184 genes from 7547 assemblies and an identification software, mlstverse, which can also be used for detecting other bacteria given a suitable MLST database. This method showed the highest sensitivity and specificity amongst conventional methods and demonstrated the capacity for rapid detection of NTM, 10 min of sequencing of the ONT MinION being sufficient. Application of this methodology could improve disease epidemiology and increase the cure rates of NTM diseases.

Secretion of the Phosphorylated Form of S100A9 from Neutrophils Is Essential for the Proinflammatory Functions of Extracellular S100A8/A9.

S100A8 and S100A9 are members of the S100 family of cytoplasmic EF-hand Ca2+-binding proteins and are abundantly expressed in the cytosol of neutrophils. In addition to their intracellular roles, S100A8/A9 can be secreted in the extracellular environment and are considered as alarmins able to amplify the inflammatory response. The intracellular activity of S100A8/A9 was shown to be regulated by S100A9 phosphorylation, but the importance of this phosphorylation on the extracellular activity of S100A8/A9 has not yet been extensively studied. Our work focuses on the impact of the phosphorylation state of secreted S100A9 on the proinflammatory function of neutrophils. In a first step, we characterized the secretion of S100A8/A9 in different stimulatory conditions and investigated the phosphorylation state of secreted S100A9. Our results on neutrophil-like differentiated HL-60 (dHL-60) cells and purified human neutrophils showed a time-dependent secretion of S100A8/A9 when induced by phorbol 12-myristoyl 13-acetate and this secreted S100A9 was found in a phosphorylated form. Second, we evaluated the impact of this phosphorylation on proinflammatory cytokine expression and secretion in dHL-60 cells. Time course experiments with purified unphosphorylated or phosphorylated S100A8/A9 were performed and the expression and secretion levels of interleukin (IL)-1α, IL-1ß, IL-6, tumor necrosis factor alpha, CCL2, CCL3, CCL4, and CXCL8 were measured by real-time PCR and cytometry bead array, respectively. Our results demonstrate that only the phosphorylated form of the complex induces proinflammatory cytokine expression and secretion. For the first time, we provide evidence that S100A8/PhosphoS100A9 is inducing cytokine secretion through toll-like receptor 4 signaling.

Matching for the nonconventional MHC-I MICA gene significantly reduces the incidence of acute and chronic GVHD.

Graft-versus-host disease (GVHD) is among the most challenging complications in unrelated donor hematopoietic cell transplantation (HCT). The highly polymorphic MHC class I chain-related gene A, MICA, encodes a stress-induced glycoprotein expressed primarily on epithelia. MICA interacts with the invariant activating receptor NKG2D, expressed by cytotoxic lymphocytes, and is located in the MHC, next to HLA-B Hence, MICA has the requisite attributes of a bona fide transplantation antigen. Using high-resolution sequence-based genotyping of MICA, we retrospectively analyzed the clinical effect of MICA mismatches in a multicenter cohort of 922 unrelated donor HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 10/10 allele-matched HCT pairs. Among the 922 pairs, 113 (12.3%) were mismatched in MICA MICA mismatches were significantly associated with an increased incidence of grade III-IV acute GVHD (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.50-2.23; P < .001), chronic GVHD (HR, 1.50; 95% CI, 1.45-1.55; P < .001), and nonelapse mortality (HR, 1.35; 95% CI, 1.24-1.46; P < .001). The increased risk for GVHD was mirrored by a lower risk for relapse (HR, 0.50; 95% CI, 0.43-0.59; P < .001), indicating a possible graft-versus-leukemia effect. In conclusion, when possible, selecting a MICA-matched donor significantly influences key clinical outcomes of HCT in which a marked reduction of GVHD is paramount. The tight linkage disequilibrium between MICA and HLA-B renders identifying a MICA-matched donor readily feasible in clinical practice.

Circulating Human Eosinophils Share a Similar Transcriptional Profile in Asthma and Other Hypereosinophilic Disorders.

Eosinophils are leukocytes that are released into the peripheral blood in a phenotypically mature state and are capable of being recruited into tissues in response to appropriate stimuli. Eosinophils, traditionally considered cytotoxic effector cells, are leukocytes recruited into the airways of asthma patients where they are believed to contribute to the development of many features of the disease. This perception, however, has been challenged by recent findings suggesting that eosinophils have also immunomodulatory functions and may be involved in tissue homeostasis and wound healing. Here we describe a transcriptome-based approach-in a limited number of patients and controls-to investigate the activation state of circulating human eosinophils isolated by flow cytometry. We provide an overview of the global expression pattern in eosinophils in various relevant conditions, e.g., eosinophilic asthma, hypereosinophilic dermatological diseases, parasitosis and pulmonary aspergillosis. Compared to healthy subjects, circulating eosinophils isolated from asthma patients differed in their gene expression profile which is marked by downregulation of transcripts involved in antigen presentation, pathogen recognition and mucosal innate immunity, whereas up-regulated genes were involved in response to non-specific stimulation, wounding and maintenance of homeostasis. Eosinophils from other hypereosinophilic disorders displayed a very similar transcriptional profile. Taken together, these observations seem to indicate that eosinophils exhibit non-specific immunomodulatory functions important for tissue repair and homeostasis and suggest new roles for these cells in asthma immunobiology.

Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network.

SUMMARY: Temporal gene interactions, in response to environmental stress, form a complex system that can be efficiently described using gene regulatory networks. They allow highlighting the more influential genes and spotting some targets for biological intervention experiments. Despite that many reverse engineering tools have been designed, the Cascade package is an integrated solution adding several new and original key features such as the ability to predict changes in gene expressions after a biological perturbation in the network and graphical outputs that allow monitoring the spread of a signal through the network. AVAILABILITY AND IMPLEMENTATION: The R package Cascade is available online at http://www-math.u-strasbg.fr/genpred/spip.php?rubrique4.

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.