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In geminiviruses belonging to the genus Begomovirus, coat protein (CP) expression depends on viral AL2 protein, which derepresses and activates the CP promoter through sequence elements that lie within the viral intergenic region (IR). However, AL2 does not exhibit sequence-specific DNA binding activity but is instead directed to responsive promoters through interactions with host factors, most likely transcriptional activators and/or repressors. In this study, we describe a repressive plant-specific transcription factor, Arabidopsis thaliana TCP24 (AtTCP24), that interacts with AL2 and recognizes a class II TCP binding site in the CP promoter (GTGGTCCC). This motif corresponds to the previously identified conserved late element (CLE). We also report that histone 3 lysine 27 trimethylation (H3K27me3), an epigenetic mark associated with facultative repression, is enriched over the viral IR. H3K27me3 is deposited by Polycomb Repressive Complex 2 (PRC2), a critical regulator of gene expression and development in plants and animals. Remarkably, mutation of the TCP24 binding site (the CLE) in tomato golden mosaic virus (TGMV) and cabbage leaf curl virus (CaLCuV) CP promoters greatly diminishes H3K27me3 levels on viral chromatin and causes a dramatic delay and attenuation of disease symptoms in infected Arabidopsis and Nicotiana benthamiana plants. Symptom remission is accompanied by decreased viral DNA levels in systemically infected tissue. Nevertheless, in transient replication assays CLE mutation delays but does not limit the accumulation of viral double-stranded DNA, although single-stranded DNA and CP mRNA levels are decreased. These findings suggest that TCP24 binding to the CLE leads to CP promoter repression and H3K27me3 deposition, while TCP24-AL2 interaction may recruit AL2 to derepress and activate the promoter. Thus, a repressive host transcription factor may be repurposed to target a viral factor essential for promoter activity. The presence of the CLE in many begomoviruses suggests a common scheme for late promoter regulation.

Osteosarcoma (OS) is a lethal disease with few known targeted therapies. Here, we show that decreased ATRX expression is associated with more aggressive tumor cell phenotypes, including increased growth, migration, invasion, and metastasis. These phenotypic changes correspond with activation of NF-κB signaling, extracellular matrix remodeling, increased integrin αvβ3 expression, and ETS family transcription factor binding. Here, we characterize these changes in vitro, in vivo, and in a data set of human OS patients. This increased aggression substantially sensitizes ATRX-deficient OS cells to integrin signaling inhibition. Thus, ATRX plays an important tumor-suppression role in OS, and loss of function of this gene may underlie new therapeutic vulnerabilities. The relationship between ATRX expression and integrin binding, NF-κB activation, and ETS family transcription factor binding has not been described in previous studies and may impact the pathophysiology of other diseases with ATRX loss, including other cancers and the ATR-X α thalassemia intellectual disability syndrome.

Abstract Background The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution. Results Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis. Conclusions Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.

Abstract Background Discovering a highly accurate and robust gene signature for the prediction of breast cancer metastasis from gene expression profiling of primary tumors is one of the most challenging tasks to reduce the number of deaths in women. Due to the limited success of gene-based features in achieving satisfactory prediction accuracy, many methodologies have been proposed in recent years to develop network-based features by integrating network information with gene expression. However, evaluation results are inconsistent to confirm the effectiveness of network-based features, because of many confounding factors involved in classification model learning process, such as data normalization, dimension reduction, and feature selection. An unbiased comparative evaluation is essential for uncovering the strength of network-based features. Methods In this study, we compared several types of network-based features obtained using different mathematical operators (Mean, Maximum, Minimum, Median, Variance) on geneset (i.e., a gene and its’ neighbors in the network) in protein-protein interaction network and gene co-expression network for their ability in predicting breast cancer metastasis using gene expression data from more than 10 patient cohorts. Results While network-based features are usually statistically more significant than gene-based feature, a consistent improvement of prediction performance using network-based features requires a substantial number of patients in the dataset. In contrary to many previous reports, no evidence was found to support the robustness of network-based features and we argue some of the robustness may be due to the inherent bias associated with node degree in the network. In addition, different types of network features seem to cover different pathways and are complementary to each other. Consequently, an ensemble classifier combining different network features was proposed and was found to significantly outperform classifiers based on gene-based feature or any single type of network-based features. Conclusions Network-based features and their combination show promise for improving the prediction of breast cancer metastasis but may require a large amount of training data. Robustness claim of network-based features needs to be re-examined with network node degree and other confounding factors in consideration.

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

Abstract The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10–12, 2018. This conference featured eleven regular scientific sessions, four tutorials, one poster session, four keynote talks, and four eminent scholar talks. The scientific program covered a wide range of topics from bench to bedside, including 3D Genome Organization, reconstruction of large scale evolution of genomes and gene functions, artificial intelligence in biological and biomedical fields, and precision medicine. Both method development and application in genomic research continued to be a main component in the conference, including studies on genetic variants, regulation of transcription, genetic-epigenetic interaction at both single cell and tissue level and artificial intelligence. Here, we write a summary of the conference and also briefly introduce the four high quality papers selected to be published in BMC Genomics that cover novel methodology development or innovative data analysis.

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.

Abstract In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) that was held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. ICIBM 2016 included four workshops or tutorials, four keynote lectures, four conference invited talks, eight concurrent scientific sessions and a poster session for 53 accepted abstracts, covering current topics in bioinformatics, systems biology, intelligent computing, and biomedical informatics. Through our call for papers, a total of 77 original manuscripts were submitted to ICIBM 2016. After peer review, 11 articles were selected in this special issue, covering topics such as single cell RNA-seq analysis method, genome sequence and variation analysis, bioinformatics method for vaccine development, and cancer genomics.

Abstract Background position weight matrix (PWM) and sequence logo are the most widely used representations of transcription factor binding site (TFBS) in biological sequences. Sequence logo - a graphical representation of PWM, has been widely used in scientific publications and reports, due to its easiness of human perception, rich information, and simple format. Different from sequence logo, PWM works great as a precise and compact digitalized form, which can be easily used by a variety of motif analysis software. There are a few available tools to generate sequence logos from PWM; however, no tool does the reverse. Such tool to convert sequence logo back to PWM is needed to scan a TFBS represented in logo format in a publication where the PWM is not provided or hard to be acquired. A major difficulty in developing such tool to convert sequence logo to PWM is to deal with the diversity of sequence logo images. Results We propose logo2PWM for reconstructing PWM from a large variety of sequence logo images. Evaluation results on over one thousand logos from three sources of different logo format show that the correlation between the reconstructed PWMs and the original PWMs are constantly high, where median correlation is greater than 0.97. Conclusion Because of the high recognition accuracy, the easiness of usage, and, the availability of both web-based service and stand-alone application, we believe that logo2PWM can readily benefit the study of transcription by filling the gap between sequence logo and PWM.

Abstract The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

Abstract Background A 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. Methods A 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. Results Over 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. Conclusions The 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.

COBRA1 is a BRCA1-binding protein and, as part of the negative elongation factor, regulates RNA polymerase II pausing and transcription elongation. Here, the authors show that tissue-specific deletion of mouse Cobra1 inhibits postnatal mammary gland development and that the mammary defects can be rescued by additional deletion of Brca1in a DNA repair-independent manner.

Abstract The 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) was held on December 8–10, 2016 in Houston, Texas, USA. ICIBM included eight scientific sessions, four tutorials, one poster session, four highlighted talks and four keynotes that covered topics on 3D genomics structural analysis, next generation sequencing (NGS) analysis, computational drug discovery, medical informatics, cancer genomics, and systems biology. Here, we present a summary of the nine research articles selected from ICIBM 2016 program for publishing in BMC Bioinformatics.

The inflammatory and metabolic derangements of obesity in pregnant women generate an adverse intrauterine environment, increase pregnancy complications and adverse fetal outcomes and program the fetus for obesity and metabolic syndrome in later life. We hypothesized that epigenetic modifications in placenta including altered DNA methylation/hydroxymethylation may mediate these effects. Term placental villous tissue was collected following cesarean section from lean (prepregnancy BMI30) women. Genomic DNA was isolated, methylated and hydroxymethylated DNA immunoprecipitated and hybridized to the NimbleGen 2.1M human DNA methylation array. Intermediate metabolites in placental tissues were measured by HPLC-ESI-MS, ascorbate levels by reverse phase HPLC and gene expression by RT-PCR. Differentially methylated and hydroxymethylated regions occurred across the genome, with a 21% increase in methylated but a 31% decrease in hydroxymethylated regions in obese vs lean groups. Whereas increased methylation and decreased methylation was evident around transcription start sites of multiple genes in the GH/CSH and PSG gene clusters on chromosomes 17 and 19 in other areas there was no relationship. Increased methylation was associated with decreased expression only for some genes in these clusters. Biological pathway analysis revealed the 262 genes which showed reciprocal differential methylation/ hydroxymethylation were enriched for pregnancy, immune response and cell adhesion-linked processes. We found a negative relationship for maternal BMI but a positive relationship for ascorbate with α-ketoglutarate a metabolite that regulates ten eleven translocase (TET) which mediates DNA methylation. We provide evidence for the obese maternal metabolic milieu being linked to an altered DNA methylome that may affect placental gene expression in relation to adverse outcomes.

A wealth of computational methods has been developed to address problems in systems biology, such as modeling gene expression. However, to objectively evaluate and compare such methods is notoriously difficult. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) project is a community-wide effort to assess the relative strengths and weaknesses of different computational methods for a set of core problems in systems biology. This article presents a top-performing algorithm for one of the challenge problems in the third annual DREAM (DREAM3), namely the gene expression prediction challenge. In this challenge, participants are asked to predict the expression levels of a small set of genes in a yeast deletion strain, given the expression levels of all other genes in the same strain and complete gene expression data for several other yeast strains. I propose a simple -nearest-neighbor (KNN) method to solve this problem. Despite its simplicity, this method works well for this challenge, sharing the "top performer" honor with a much more sophisticated method. I also describe several alternative, simple strategies, including a modified KNN algorithm that further improves the performance of the standard KNN method. The success of these methods suggests that complex methods attempting to integrate multiple data sets do not necessarily lead to better performance than simple yet robust methods. Furthermore, none of these top-performing methods, including the one by a different team, are based on gene regulatory networks, which seems to suggest that accurately modeling gene expression using gene regulatory networks is unfortunately still a difficult task.

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS-induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into "pathway nodes" qualitatively improved the interactome organization, revealing a highly organized "MMS survival network." We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis.

Diagnosis of acute infection in the critically ill remains a challenge. We hypothesized that circulating leukocyte transcriptional profiles can be used to monitor the host response to and recovery from infection complicating critical illness.A translational research approach was employed. Fifteen mice underwent intratracheal injections of live P. aeruginosa, P. aeruginosa endotoxin, live S. pneumoniae, or normal saline. At 24 hours after injury, GeneChip microarray analysis of circulating buffy coat RNA identified 219 genes that distinguished between the pulmonary insults and differences in 7-day mortality. Similarly, buffy coat microarray expression profiles were generated from 27 mechanically ventilated patients every two days for up to three weeks. Significant heterogeneity of VAP microarray profiles was observed secondary to patient ethnicity, age, and gender, yet 85 genes were identified with consistent changes in abundance during the seven days bracketing the diagnosis of VAP. Principal components analysis of these 85 genes appeared to differentiate between the responses of subjects who did versus those who did not develop VAP, as defined by a general trajectory (riboleukogram) for the onset and resolution of VAP. As patients recovered from critical illness complicated by acute infection, the riboleukograms converged, consistent with an immune attractor.Here we present the culmination of a mouse pneumonia study, demonstrating for the first time that disease trajectories derived from microarray expression profiles can be used to quantitatively track the clinical course of acute disease and identify a state of immune recovery. These data suggest that the onset of an infection-specific transcriptional program may precede the clinical diagnosis of pneumonia in patients. Moreover, riboleukograms may help explain variance in the host response due to differences in ethnic background, gender, and pathogen. Prospective clinical trials are indicated to validate our results and test the clinical utility of riboleukograms.

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species.

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.