Background
Reconstruction of amino acid sequences from assembled transcriptome is of interest in personalized medicine, for example, to predict drug-target (or protein-protein) interaction ...considering individual’s genomic variations. Most of the existing transcriptome assemblers, however, seems not well suited for this purpose.
Methods
In this work, we present StringFix, an annotation guided transcriptome assembly and protein sequence reconstruction software tool that takes genome-aligned reads and the annotations associated to the reference genome as input. The tool ‘fixes’ the pre-annotated transcript sequence by taking small variations into account, finally to produce possible amino acid sequences that are likely to exist in the test tissue.
Results
The results show that, using outputs from existing reference-based assemblers as the input GTF-guide, StringFix could reconstruct amino acid sequences more precisely with higher sensitivity than direct generation using the recovered transcripts from all the assemblers we tested.
Conclusion
By using StringFix with the existing reference-based assemblers, one can recover not only a novel transcripts and isoforms but also the possible amino acid sequence stemming from them.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background
Alarmins S100A8 and S100A9 are recognized as hallmarks of severe COVID-19 and are primarily produced in myeloid cells, such as monocytes and neutrophils. As single-cell RNA-sequencing ...(scRNA-seq) data from patients with COVID-19 revealed the expression of
S100A8/A9
in lymphoid cells in patients with severe COVID-19.
Objective
We investigated the characteristics of lymphoid cells expressing S100A8/A9 in COVID-19 patients.
Methods
Publicly available scRNA-seq data from patients with mild (N = 12) or severe (N = 7) COVID-19 were reanalyzed. The data were further divided into the following two groups based on the time of sample collection (from infection-onset): within 6 days (early phase) and after 6 days (late phase). Differential expression and gene set enrichment analyses were performed between S100A8/A9
High
and S100A8/A9
Low
lymphoid cells. Finally, cell-cell interaction analysis was performed to investigate the role of lymphoid cells expressing high levels of
S100A8/A9
in COVID-19.
Results
S100A8/A9
overexpression was observed in lymphoid cells, including B cells, T cells, and NK cells, in patients with severe COVID-19 (compared to patients with mild COVID-19). Cells exhibiting strong interferon/cytokine responses were found to be associated with the severity of COVID-19. Furthermore, differences in S100A8/A9-TLR4/RAGE interactions were confirmed between patients with severe and mild disease.
Conclusions
Lymphoid cells overexpressing
S100A8/A9
contribute to the dysregulation of the innate immune response in patients with severe COVID-19, specifically during the early phase of infection. This study fosters a better understanding of the hyper-induction of pro-inflammatory cytokine expression and the generation of a cytokine storm in response to COVID-19 infection.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Cell type identification is a key step toward downstream analysis of single cell RNA-seq experiments. Although the primary objective is to identify known cell populations, good identifiers should ...also recognize unknown clusters which may represent a previously unidentified subpopulation of a known cell type or tumor cells of an unknown phenotype. Herein, we present MarkerCount, which utilizes the number of expressed markers, regardless of their expression level. MarkerCount works in both reference- and marker-based mode, where the latter utilizes existing lists of markers, while the former uses a pre-annotated dataset to find markers to be used for cell type identification. In both modes, MarkerCount first utilizes the “marker count” to identify cell populations and, after rejecting uncertain cells, reassigns cell type and/or makes corrections in cluster-basis. The performance of MarkerCount was evaluated and compared with existing identifiers, both marker- and reference-based, that can be customized using publicly available datasets and marker databases. The results show that MarkerCount performs better in the identification of known populations as well as of unknown ones, when compared to other reference- and marker-based cell type identifiers for most of the datasets analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Nicotinamide phosphoribosyl transferase (NAMPT) is required to maintain the NAD
pool, among which extracellular (e) NAMPT is associated with inflammation, mainly mediated by macrophages. However, the ...role of (e) NAMPT in inflammatory macrophages in ulcerative colitis is insufficiently understood. Here our analyses of single-cell RNA-seq data revealed that the levels of NAMPT and CYBB/NOX2 in macrophages were elevated in patients with colitis and in mouse models of acute and chronic colitis. These findings indicate the clinical significance of NAMPT and CYBB in colitis. Further, we found that eNAMPT directly binds the extracellular domains of CYBB and TLR4 in activated NLRP3 inflammasomes. Moreover, we developed a recombinant 12-residue TK peptide designated colon-targeted (CT)-conjugated multifunctional NAMPT (rCT-NAMPT), comprising CT as the colon-targeting moiety, which harbors the minimal essential residues required for CYBB/TLR4 binding. rCT-NAMPT effectively suppressed the severity of disease in DSS-induced acute and chronic colitis models through targeting the colon and inhibiting the interaction of NAMPT with CYBB or TLR4. Together, our data show that rCT-NAMPT may serve as an effective novel candidate therapeutic for colitis by modulating the NLRP3 inflammasome-mediated immune signaling system.
This study was conducted to evaluate a visual surveillance system. The advancement of recording technology and network service make it easy to record and transfer the videos. Moreover, progressed ...recognition technology help to make a distinction each other. Cows show distinguishing behaviors during their estrus period. The mounting is one of the behaviors. The result was different depending on the breed of the cows and the size of the farm. In the case of Korean native cattle, the estrus detection rate was 71.15%, however, dairy cows, the estrus detection rate was 39.38%. At the farms having below 6 modules, the estrus detection rate was 87.41%. On the other hand, at the farms having over 6 modules, the estrus detection rate was 77.78%. With the proper progress, the visual surveillance system can be used to detect heat detection.
Abstract
Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, ...whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
CD82 is a transmembrane protein that is involved in cancer suppression and activates immune cells; however, information on the NLRP3 inflammasome is limited. Herein, we show that although CD82 ...suppressed the activation of the NLRP3 inflammasome in vivo and in vitro, CD82 deficiency decreased the severity of colitis in mice. Furthermore, two binding partners of CD82, NLRP3 and BRCC3, were identified. CD82 binding to these partners increased the degradation of NLRP3 by blocking BRCC3-dependent K63-specific deubiquitination. Previous studies have shown that CD82-specific bacteria in the colon microbiota called Bacteroides vulgatus (B. vulgatus) regulated the expression of CD82 and promoted the activation of the NLRP3 inflammasome. Accordingly, we observed that B. vulgatus administration increased mouse survival by mediating CD82 expression and activating NLRP3 in mice with colitis. Overall, this study showed that CD82 suppression reduced the pathogenesis of colitis by elevating the activation of the NLRP3 inflammasome through BRCC3-dependent K63 deubiquitination. Based on our findings, we propose that B. vulgatus is a novel therapeutic candidate for colitis.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing ...oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.
