HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD).

BACKGROUND: Cell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods. METHODS: We developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks. RESULTS: HASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients' overall survival. CONCLUSION: HASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD .

TSSUNet-MB - ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning.

MOTIVATION: Computational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters. RESULTS: TSSUNet-MB is a deep learning model developed to identify the TSSs of σ70 promoters. Mononucleotide and bendability were used to encode input sequences. TSSUNet-MB outperforms other CPP tools when assessed using the sequences obtained from the neighborhood of real promoters. TSSUNet-MB achieved a sensitivity of 0.839 and specificity of 0.768 on sliding sequences, while other CPP tool cannot maintain both sensitivities and specificities in a compatible range. Furthermore, TSSUNet-MB can precisely predict the TSS position of σ70 promoter-containing regions with a 10-base accuracy of 77.6%. By leveraging the sliding window scanning approach, we further computed the confidence score of each predicted TSS, which allows for more accurately identifying TSS locations. Our results suggest that TSSUNet-MB is a robust tool for finding σ70 promoters and identifying TSSs.

Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients.

BACKGROUND: Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression. RESULTS: A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes. CONCLUSIONS: HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.

Emergence of an XDR and carbapenemase-producing hypervirulent Klebsiella pneumoniae strain in Taiwan.

Background: Carbapenemase-producing Klebsiella pneumoniae causes high mortality owing to the limited therapeutic options available. Here, we investigated an emergent carbapenem-resistant K. pneumoniae strain with hypervirulence found among KPC-2-producing strains in Taiwan. Methods: KPC-producing K. pneumoniae strains were collected consecutively from clinical specimens at the Taipei Veterans General Hospital between January 2012 and December 2014. Capsular types and the presence of rmpA/rmpA2 were analysed, and PFGE and MLST performed using these strains. The strain positive for rmpA/rmpA2 was tested in an in vivo mouse lethality study to verify its virulence and subjected to WGS to delineate its genomic features. Results: A total of 62 KPC-2-producing K. pneumoniae strains were identified; all of these belonged to ST11 and capsular genotype K47. One strain isolated from a fatal case with intra-abdominal abscess (TVGHCRE225) harboured rmpA and rmpA2 genes. This strain was resistant to tigecycline and colistin, in addition to carbapenems, and did not belong to the major cluster in PFGE. TVGHCRE225 exhibited high in vivo virulence in the mouse lethality experiment. WGS showed that TVGHCRE225 acquired a novel hybrid virulence plasmid harbouring a set of virulence genes (iroBCDN, iucABCD, rmpA and rmpA2, and iutA) compared with the classic ST11 KPC-2-producing strain. Conclusions: We identified an XDR ST11 KPC-2-producing K. pneumoniae strain carrying a hybrid virulent plasmid in Taiwan. Active surveillance focusing on carbapenem-resistant hypervirulent K. pneumoniae strains is necessary, as the threat to human health is imminent.