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BackgroundNatural killer (NK) cells are effective at killing tumors in a non-MHC restricted manner and are emerging targets for cancer therapy but their importance in bladder cancer (BC) is poorly defined. NK cells are commonly subdivided into populations based on relative surface expression of CD56. Two major subsets are CD56bright and CD56dim NK cells.MethodsThe prevalence of intratumoral lymphocytes was examined via flow cytometric analysis of bladder tissue from a local cohort of patients with non-invasive and invasive BC (n=28). The association of NK cell subsets with cancer-specific survival (CSS) and overall survival (OS) was examined in 50 patients with BC using Cox regression. Fluorescence-activated cell sorting (FACS) of intratumoral lymphocytes isolated CD56 NK cell subsets were used for examination of function, including cytokine production and in vitro cytotoxicity.ResultsNK cells predominated among bladder intratumoral lymphocytes. Intratumoral CD56bright NK cells showed increased cytokine production and cytotoxicity compared to their CD56dim counterparts and were associated with improved CSS and OS independent of pathologic tumor stage. On the other hand, CD56dim NK cells were not associated with improved outcomes but were associated with higher pathologic stage.ConclusionsNK cells are frequent among intratumoral lymphocytes in BC. Bladder intratumoral CD56bright NK cells are functional and prognostically relevant whereas CD56dim NK cells are dysfunctional and prevalent in higher stage tumors. Thus, CD56bright NK cells are promising targets in BC.

BackgroundA negative attitude toward disability is one of the potential barriers for people with disability (PWD) to achieve social equality. Although numerous studies have investigated attitudes toward disability, few have evaluated personal attitudes toward disability among PWD, and made comparisons with attitudes of healthy respondents. This study was to investigate and compare the attitudes of PWD, caregivers, and the public toward disability and PWD in China, to identify discrepancies in attitude among the three groupsand to examine potential influencing factors of attitude within each group.MethodsA cross-sectional study was conducted among 2912 PWD, 507 caregivers, and 354 members of the public in Guangzhou, China. Data were collected on participants’ socio-demographic information and personal attitudes toward disability using the Attitude to Disability Scale (ADS). ANOVA and ANCOVA were applied to compare the level of attitude among the three groups. Simple and multiple linear regression analyses were used to investigate the relationship between each background factor and attitude within each group.ResultsOver 90 % of caregivers were PWD’s family members. After controlling the socio-demographic characteristics, caregivers had the lowest total scores of ADS (caregivers: 47.7; PWD: 52.3; the public: 50.5). Caregivers who had taken care of PWD for longer durations of time had a more negative attitude toward disability. In contrast, PWD who had been disabled for longer times had a more positive attitude toward disability.ConclusionsThe current national social security system of China does not adequately support PWD’s family-member caregivers who may need assistance coping with their life with PWDs. More research is needed, and the development of a new health-care model for PWD is warranted.

BackgroundPeople with physical disability (PWPD) is the largest subgroup of people with disability (PWD) in China, but few studies have been conducted among this vulnerable population. The objective of this study was to investigate the level of quality of life (QoL), self-perceived quality of care and support (QOCS), severity of disability and personal attitude towards disability among people with physical disability in China, as well as to identify how QoL can be affected by severity of disability through QOCS and personal attitude towards disability among PWPD.MethodsA cross-sectional study was conducted among 1,853 PWPD in Guangzhou, China. Data were collected on participants’ QoL, QOCS, personal attitude towards disability and severity of disability. Structural equation modeling was used to examine the effects of the other variables on QoL.ResultsEven with a mild disability (mean score:1.72), relatively low levels of QoL (mean score: 2.65- 3.22) and QOCS (mean score: 2.95 to 3.28), as well as unfavorable personal attitude towards disability (mean score: 2.75 to 3.36) were identified among PWPD. According to SEM, we found that the influence of severity of physical disability on QoL is not only exerted directly, but is also indirectly through QOCS and their personal attitudes towards disability, with QOCS playing a more important mediating role than PWPD’s attitudes towards their own disability.ConclusionsUnfavorable health status was identified among PWPD in China. Focusing on improvement of assistance and care services has the potential to substantially improve PWPD’s QoL. Further research should focus on understanding the needs and their current state of health care of PWPD in China thus being able to develop better interventions for them.

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

Abstract. Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. In this paper, we introduce a simple method to construct gene co-expression networks from microarray data, and then propose an efficient spectral clustering algorithm to identify natural communities, which are relatively densely connected sub-graphs, in the network. To assess the effectiveness of our approach and its advantage over existing methods, we develop a novel method to measure the agreement between the gene communities and the modular structures in other reference networks, including protein-protein interaction networks, transcriptional regulatory networks, and gene networks derived from gene annotations. We evaluate the proposed methods on two large-scale gene expression data in budding yeast and Arabidopsis thaliana. The results show that the clusters identified by our method are functionally more coherent than the clusters from several standard clustering algorithms, such as k-means, self-organizing maps, and spectral clustering, and have high agreement to the modular structures in the reference networks.

Geminiviruses are a significant group of emergent plant DNA viruses causing devastating diseases in food crops worldwide, including the Southern United States, Central America and the Caribbean. Crop failure due to geminivirus-related disease can be as high as 100%. Improved global transportation has enhanced the spread of geminiviruses and their vectors, supporting the emergence of new, more virulent recombinant strains. With limited coding capacity, geminiviruses encode multifunctional proteins, including the AC2/C2 gene that plays a central role in the viral replication-cycle through suppression of host defenses and transcriptional regulation of the late viral genes. The AC2/C2 proteins encoded by mono-and bipartite geminiviruses and the curtovirus C2 can be considered virulence factors, and are known to interact with both basal and inducible systems. This review highlights the role of AC2/C2 in affecting the jasmonic acid and salicylic acid (JA and SA) pathways, the ubiquitin/proteasome system (UPS), and RNA silencing pathways. In addition to suppressing host defenses, AC2/C2 play a critical role in regulating expression of the coat protein during the viral life cycle. It is important that the timing of CP expression is regulated to ensure that ssDNA is converted to dsDNA early during an infection and is sequestered late in the infection. How AC2 interacts with host transcription factors to regulate CP expression is discussed along with how computational approaches can help identify critical host networks targeted by geminivirus AC2 proteins. Thus, the role of AC2/C2 in the viral life-cycle is to prevent the host from mounting an efficient defense response to geminivirus infection and to ensure maximal amplification and encapsidation of the viral genome.

Background Metastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer. Methods In this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers. Results We applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis. Conclusions The randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.