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Coxsackievirus B3 (CVB3), a member of Picornaviridae family, is an important human pathogen that causes a wide range of diseases, including myocarditis, pancreatitis, and meningitis. Although CVB3 has been well demonstrated to target murine neural progenitor cells (NPCs), gene expression profiles of CVB3-infected human NPCs (hNPCs) has not been fully explored. To characterize the molecular signatures and complexity of CVB3-mediated host cellular responses in hNPCs, we performed QuantSeq 3′ mRNA sequencing. Increased expression levels of viral RNA sensors (RIG-I, MDA5) and interferon-stimulated genes, such as IFN-β, IP-10, ISG15, OAS1, OAS2, Mx2, were detected in response to CVB3 infection, while IFN-γ expression level was significantly downregulated in hNPCs. Consistent with the gene expression profile, CVB3 infection led to enhanced secretion of inflammatory cytokines and chemokines, such as interleukin-6 (IL-6), interleukin-8 (IL-8), and monocyte chemoattractant protein-1 (MCP-1). Furthermore, we show that type I interferon (IFN) treatment in hNPCs leads to significant attenuation of CVB3 RNA copy numbers, whereas, type II IFN (IFN-γ) treatment enhances CVB3 replication and upregulates suppressor of cytokine signaling 1/3 (SOCS) expression levels. Taken together, our results demonstrate the distinct molecular patterns of cellular responses to CVB3 infection in hNPCs and the pro-viral function of IFN-γ via the modulation of SOCS expression.

Homologous recombination deficiency (HRD) leads to DNA double‐strand breaks and can be exploited by the use of poly (ADP‐ribose) polymerase (PARP) inhibitors to induce synthetic lethality. Extending the original therapeutic concept, the role of HRD is currently being investigated in clinical trials testing immune checkpoint blockers alone or in combination with PARP inhibitors, but the relationship between HRD and immune cell context in cancer is incompletely understood. We analyzed the association between immune cell composition, gene expression, and HRD in 9,041 tumors of 32 solid cancer types from The Cancer Genome Atlas (TCGA). The numbers of genomic scars were quantified by the HRD sum score (HRDsum) including loss of heterozygosity, large‐scale state transitions, and telomeric allelic imbalance. The T‐cell inflamed gene expression profile correlated weakly, but significantly positively, with HRDsum across cancer types (ρ = 0.17). Within individual cancer types, a significantly positive correlation was observed only in breast cancer, ovarian cancer, and four other cancer types, but not in the remaining 26 cancer types. HRDsum and tumor mutational burden (TMB) correlated significantly positively across cancer types (ρ = 0.42) and within 18 cancer types. HRDsum and a proliferation metagene correlated significantly positively across cancer types (ρ = 0.52) and within 20 cancer types. Mismatch repair deficiency and HRD as well as proofreading deficiency showed a high level of exclusivity. High HRD scores were associated with an immunologically activated tumor microenvironment only in a minority of cancer types. Our data favor the combination of genetic markers, complex genomic markers (including HRDsum and TMB), and other molecular markers (including proliferation scores) for a precise and comprehensive read‐out of the tumor biology and an individually tailored treatment.

Methylprednisolone is a commonly used drug for the treatment of spinal cord injury, but high doses of methylprednisolone can increase the incidence of infectious diseases. Methotrexate has anti-inflammatory activity and immunosuppressive effects, and can reduce in- flammation after spinal cord injury. To analyze gene expression changes and the molecular mechanism of methotrexate combined with methylprednisolone in the treatment of spinal cord injury, a rat model of spinal cord contusion was prepared using the PinPointTM preci- sion cortical impactor technique. Rats were injected with methylprednisolone 30 mg/kg 30 minutes after injury, and then subcutaneously injected with 0.3 mg/kg methotrexate 1 day after injury, once a day, for 2 weeks. TreadScan gait analysis found that at 4 and 8 weeks after injury, methotrexate combined with methylprednisolone significantly improved hind limb swing time, stride time, minimum longitudinal deviation, instant speed, footprint area and regularity index. Solexa high-throughput sequencing was used to analyze differential gene ex- pression. Compared with methylprednisolone alone, differential expression of 316 genes was detected in injured spinal cord treated with methotrexate and methylprednisolone. The 275 up-regulated genes were mainly related to nerve recovery, anti-oxidative, anti-inflammatory and anti-apoptotic functions, while 41 down-regulated genes were mainly related to proinflammatory and pro-apoptotic functions. These results indicate that methotrexate combined with methylprednisolone exhibited better effects on inhibiting the activity of inflammatory cytokines and enhancing antioxidant and anti-apoptotic effects and thereby produced stronger neuroprotective effects than methotrexate alone. The 316 differentially expressed genes play an important role in the above processes.

Cerebral gliomas of World Health Organization (WHO) grade II and III represent a major challenge in terms of histological classification and clinical management. Here, we asked whether large-scale genomic and transcriptomic profiling improves the definition of prognostically distinct entities. We performed microarray-based genome- and transcriptome-wide analyses of primary tumor samples from a prospective German Glioma Network cohort of 137 patients with cerebral gliomas, including 61 WHO grade II and 76 WHO grade III tumors. Integrative bioinformatic analyses were employed to define molecular subgroups, which were then related to histology, molecular biomarkers, including isocitrate dehydrogenase 1 or 2 ( IDH1/2 ) mutation, 1p/19q co-deletion and telomerase reverse transcriptase ( TERT ) promoter mutations, and patient outcome. Genomic profiling identified five distinct glioma groups, including three IDH1/2 mutant and two IDH1/2 wild-type groups. Expression profiling revealed evidence for eight transcriptionally different groups (five IDH1/2 mutant, three IDH1/2 wild type), which were only partially linked to the genomic groups. Correlation of DNA-based molecular stratification with clinical outcome allowed to define three major prognostic groups with characteristic genomic aberrations. The best prognosis was found in patients with IDH1/2 mutant and 1p/19q co-deleted tumors. Patients with IDH1/2 wild-type gliomas and glioblastoma-like genomic alterations, including gain on chromosome arm 7q (+7q), loss on chromosome arm 10q (−10q), TERT promoter mutation and oncogene amplification, displayed the worst outcome. Intermediate survival was seen in patients with IDH1/2 mutant, but 1p/19q intact, mostly astrocytic gliomas, and in patients with IDH1/2 wild-type gliomas lacking the +7q/−10q genotype and TERT promoter mutation. This molecular subgrouping stratified patients into prognostically distinct groups better than histological classification. Addition of gene expression data to this genomic classifier did not further improve prognostic stratification. In summary, DNA-based molecular profiling of WHO grade II and III gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological classification as well as IDH1/2 mutation and 1p/19q co-deletion status.

Currently, there is no strong evidence of the well-established biomarkers for immune checkpoint inhibitors (ICIs) in nasopharyngeal carcinoma (NPC). Here, we aimed to reveal the heterogeneity of tumour microenvironment (TME) through virtual microdissection of gene expression profiles. An immune-enriched subtype was identified in 38% (43/113) of patients, which was characterized by significant enrichment of immune cells or immune responses. The remaining patients were therefore classified as a non-Immune Subtype (non-IS), which exhibited highly proliferative features. Then we identified a tumour immune evasion state within the immune-enriched subtype (18/43, 42%), in which high expression of exclusion- and dysfunction-related signatures was observed. These subgroups were designated the Evaded and Active Immune Subtype (E-IS and A-IS), respectively. We further demonstrated that A-IS predicted favourable survival and improved ICI response as compared to E-IS and non-IS. In summary, this study introduces the novel immune subtypes and demonstrates their feasibility in tailoring immunotherapeutic strategies.

Metastasis remains the most common cause of death in most cancers, with limited therapies for combating disseminated disease. While the primary tumour microenvironment is an important regulator of cancer progression, it is less well understood how different tissue environments influence metastasis. We analysed tumour–stroma interactions that modulate organ tropism of brain, bone and lung metastasis in xenograft models. We identified a number of potential modulators of site-specific metastasis, including cathepsin S as a regulator of breast-to-brain metastasis. High cathepsin S expression at the primary site correlated with decreased brain metastasis-free survival in breast cancer patients. Both macrophages and tumour cells produce cathepsin S, and only the combined depletion significantly reduced brain metastasis in vivo . Cathepsin S specifically mediates blood–brain barrier transmigration through proteolytic processing of the junctional adhesion molecule, JAM-B. Pharmacological inhibition of cathepsin S significantly reduced experimental brain metastasis, supporting its consideration as a therapeutic target for this disease. Joyce and colleagues analyse tumour–stroma interactions in distinct metastatic microenvironments, and show that cathepsin S promotes brain metastasis by cleaving the JAM-B junctional protein, allowing cancer cells to traverse the blood–brain barrier.

Microarray technology facilitates the simultaneous measurement of expression of tens of thousands of genes and enables us to study cancers and tumors at the molecular level. Because microarray data are typically characterized by small sample size and high dimensionality, accurate and stable feature selection is thus of fundamental importance to the diagnostic accuracy and deep understanding of disease mechanism. Hence, we in this study present an ensemble feature selection framework to improve the discrimination and stability of finally selected features. Specifically, we utilize sampling techniques to obtain multiple sampled datasets, from each of which we use a base feature selector to select a subset of features. Afterwards, we develop two aggregation strategies to combine multiple feature subsets into one set. Finally, comparative experiments are conducted on four publicly available microarray datasets covering both binary and multi-class cases in terms of classification accuracy and three stability metrics. Results show that the proposed method obtains better stability scores and achieves comparable to and even better classification performance than its competitors. •An ensemble feature selection framework towards stable gene selection is proposed.•We present two aggregation strategies to combine multiple feature subsets into one.•Experimental results show its effectiveness in terms of stability and accuracy.•We conducted time complexity analysis of the proposed model.

Background RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation. Results In order to enable fast and user-friendly data analysis, we developed an intuitive and scalable transcriptome pipeline that executes the full process, starting from cDNA sequences derived by RNA-Seq [Nat Rev Genet 10:57-63, 2009] and bulk MARS-Seq [Science 343:776-779, 2014] and ending with sets of differentially expressed genes. Output files are placed in structured folders, and results summaries are provided in rich and comprehensive reports, containing dozens of plots, tables and links. Conclusion Our User -friendly T ranscriptome A nalysis P ipeline (UTAP) is an open source, web-based intuitive platform available to the biomedical research community, enabling researchers to efficiently and accurately analyse transcriptome sequence data.

Background Expansins ( EXPs ), a group of proteins that loosen plant cell walls and cellulosic materials, are involved in regulating cell growth and diverse developmental processes in plants. However, the biological functions of this gene family in cotton are still unknown. Results In this paper, we identified a total of 93 expansin genes in Gossypium hirsutum . These genes were classified into four subfamilies, including 67 GhEXPAs , 8 GhEXPBs , 6 GhEXLAs , and 12 GhEXLBs , and divided into 15 subgroups. The 93 expansin genes are distributed over 24 chromosomes, excluding Ghir_A02 and Ghir_D06. All GhEXP genes contain multiple exons, and each GhEXP protein has multiple conserved motifs. Transcript profiling and qPCR analysis revealed that the expansin genes have distinct expression patterns among different stages of cotton fibre development. Among them, 3 genes ( GhEXPA4o , GhEXPA1A , and GhEXPA8h ) were highly expressed in the initiation stage, 9 genes ( GhEXPA4a , GhEXPA13a , GhEXPA4f , GhEXPA4q , GhEXPA8f , GhEXPA2 , GhEXPA8g , GhEXPA8a , and GhEXPA4n ) had high expression during the fast elongation stage, and GhEXLA1c and GhEXLA1f were preferentially expressed in the transition stage of fibre development. Conclusions Our results provide a solid basis for further elucidation of the biological functions of expansin genes in relation to cotton fibre development and valuable genetic resources for future crop improvement.

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.