COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. METHODS: We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. RESULTS: The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. CONCLUSIONS: The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

Transcriptome Profiling of Cardiac Glycoside Treatment Reveals EGR1 and Downstream Proteins of MAPK/ERK Signaling Pathway in Human Breast Cancer Cells.

Cardiac glycosides (CGs) constitute a group of steroid-like compounds renowned for their effectiveness in treating cardiovascular ailments. In recent times, there has been growing recognition of their potential use as drug leads in cancer treatment. In our prior research, we identified three highly promising CG compounds, namely lanatoside C (LC), peruvoside (PS), and strophanthidin (STR), which exhibited significant antitumor effects in lung, liver, and breast cancer cell lines. In this study, we investigated the therapeutic response of these CGs, with a particular focus on the MCF-7 breast cancer cell line. We conducted transcriptomic profiling and further validated the gene and protein expression changes induced by treatment through qRT-PCR, immunoblotting, and immunocytochemical analysis. Additionally, we demonstrated the interactions between the ligands and target proteins using the molecular docking approach. The transcriptome analysis revealed a cluster of genes with potential therapeutic targets involved in cytotoxicity, immunomodulation, and tumor-suppressor pathways. Subsequently, we focused on cross-validating the ten most significantly expressed genes, EGR1, MAPK1, p53, CCNK, CASP9, BCL2L1, CDK7, CDK2, CDK2AP1, and CDKN1A, through qRT-PCR, and their by confirming the consistent expression pattern with RNA-Seq data. Notably, among the most variable genes, we identified EGR1, the downstream effector of the MAPK signaling pathway, which performs the regulatory function in cell proliferation, tumor invasion, and immune regulation. Furthermore, we substantiated the influence of CG compounds on translational processes, resulting in an alteration in protein expression upon treatment. An additional analysis of ligand-protein interactions provided further evidence of the robust binding affinity between LC, PS, and STR and their respective protein targets. These findings underscore the intense anticancer activity of the investigated CGs, shedding light on potential target genes and elucidating the probable mechanism of action of CGs in breast cancer.

Ganglioside-Enriched Phospholipid Vesicles Induce Cooperative Aß Oligomerization and Membrane Disruption.

A major hallmark of Alzheimer's disease (AD) is the accumulation of extracellular aggregates of amyloid-ß (Aß). Structural polymorphism observed among Aß fibrils in AD brains seem to correlate with the clinical subtypes suggesting a link between fibril polymorphism and pathology. Since fibrils emerge from a templated growth of low-molecular-weight oligomers, understanding the factors affecting oligomer generation is important. Membrane lipids are key factors to influence early stages of Aß aggregation and oligomer generation, which cause membrane disruption. We have previously demonstrated that conformationally discrete Aß oligomers can be generated by modulating the charge, composition, and chain length of lipids and surfactants. Here, we extend our studies into liposomal models by investigating Aß oligomerization on large unilamellar vesicles (LUVs) of total brain extracts (TBE), reconstituted lipid rafts (LRs), or 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Varying the vesicle composition by specifically increasing the amount of GM1 gangliosides as a constituent, we found that only GM1-enriched liposomes induce the formation of toxic, low-molecular-weight oligomers. Furthermore, we found that the aggregation on liposome surface and membrane disruption are highly cooperative and sensitive to membrane surface characteristics. Numerical simulations confirm such a cooperativity and reveal that GM1-enriched liposomes form twice as many pores as those formed in the absence GM1. Overall, this study uncovers mechanisms of cooperativity between oligomerization and membrane disruption under controlled lipid compositional bias, and refocuses the significance of the early stages of Aß aggregation in polymorphism, propagation, and toxicity in AD.

Long-term unsustainable patterns of development rather than recent deforestation caused the emergence of Orthocoronavirinae species.

We investigated whether a set of phylogeographical tracked emergent events of Orthocoronavirinae were related to developed, urban and polluted environments worldwide. We explored coronavirus records in response to climate (rainfall parameters), population density, CO2 emission, Human Developmental Index (HDI) and deforestation. We contrasted environmental characteristics from regions with spillovers or encounters of wild Orthocoronavirinae against adjacent areas having best-preserved conditions. We used all complete sequenced CoVs genomes deposited in NCBI and GISAID databases until January 2021. Except for Deltacoronavirus, concentrated in Hong Kong and in birds, the other three genera were scattered all over the planet, beyond the original distribution of the subfamily, and found in humans, mammals, fishes and birds, wild or domestic. Spillovers and presence in wild animals were only reported in developed/densely populated places. We found significantly more occurrences reported in places with higher HDI, CO2 emission, or population density, along with more rainfall and more accentuated seasonality. Orthocoronavirinae occurred in areas with significantly higher human populations, CO2 emissions and deforestation rates than in adjacent locations. Intermediately disturbed ecosystems seemed more vulnerable for Orthocoronavirinae emergence than forested regions in frontiers of deforestation. Sadly, people experiencing poverty in an intensely consumerist society are the most vulnerable.

Computational identification of putative common genomic drug and vaccine targets in Mycoplasma genitalium.

Mycoplasma genitalium is an obligate intracellular bacterium that is responsible for several sexually transmitted infections, including non-gonococcal urethritis in men and several inflammatory reproductive tract syndromes in women. Here, we applied subtractive genomics and reverse vaccinology approaches for in silico prediction of potential vaccine and drug targets against five strains of M. genitalium. We identified 403 genes shared by all five strains, from which 104 non-host homologous proteins were selected, comprising of 44 exposed/secreted/membrane proteins and 60 cytoplasmic proteins. Based on the essentiality, functionality, and structure-based binding affinity, we finally predicted 19 (14 novel) putative vaccine and 7 (2 novel) candidate drug targets. The docking analysis showed six molecules from the ZINC database as promising drug candidates against the identified targets. Altogether, both vaccine candidates and drug targets identified here may contribute to the future development of therapeutic strategies to control the spread of M. genitalium worldwide.

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology.

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM-RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site-specific "Smart" templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well-defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.

The pan-genome of Treponema pallidum reveals differences in genome plasticity between subspecies related to venereal and non-venereal syphilis.

BACKGROUND: Spirochetal organisms of the Treponema genus are responsible for causing Treponematoses. Pathogenic treponemes is a Gram-negative, motile, spirochete pathogen that causes syphilis in human. Treponema pallidum subsp. endemicum (TEN) causes endemic syphilis (bejel); T. pallidum subsp. pallidum (TPA) causes venereal syphilis; T. pallidum subsp. pertenue (TPE) causes yaws; and T. pallidum subsp. Ccarateum causes pinta. Out of these four high morbidity diseases, venereal syphilis is mediated by sexual contact; the other three diseases are transmitted by close personal contact. The global distribution of syphilis is alarming and there is an increasing need of proper treatment and preventive measures. Unfortunately, effective measures are limited. RESULTS: Here, the genome sequences of 53 T. pallidum strains isolated from different parts of the world and a diverse range of hosts were comparatively analysed using pan-genomic strategy. Phylogenomic, pan-genomic, core genomic and singleton analysis disclosed the close connection among all strains of the pathogen T. pallidum, its clonal behaviour and showed increases in the sizes of the pan-genome. Based on the genome plasticity analysis of the subsets containing the subspecies T pallidum subsp. pallidum, T. pallidum subsp. endemicum and T. pallidum subsp. pertenue, we found differences in the presence/absence of pathogenicity islands (PAIs) and genomic islands (GIs) on subsp.-based study. CONCLUSIONS: In summary, we identified four pathogenicity islands (PAIs), eight genomic islands (GIs) in subsp. pallidum, whereas subsp. endemicum has three PAIs and seven GIs and subsp. pertenue harbours three PAIs and eight GIs. Concerning the presence of genes in PAIs and GIs, we found some genes related to lipid and amino acid biosynthesis that were only present in the subsp. of T. pallidum, compared to T. pallidum subsp. endemicum and T. pallidum subsp. pertenue.

A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels.

PURPOSE: To present a Machine Learning pipeline for automatically relabeling anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) format to a standard nomenclature that will enable data abstraction for research and quality improvement. METHODS: DICOM structure sets from approximately 1200 lung and prostate cancer patients across 40 treatment centers were used to build predictive models to automate the relabeling of clinically specified structure labels to standardized labels as defined by the American Association of Physics in Medicine's (AAPM) Task Group 263 (TG-263). Volumetric bitmaps were created based on the delineated volumes and were combined with associated bony anatomy data to build feature vectors. Feature reduction was performed with singular value decomposition and the resulting vectors were used for predicting the label of each structure using five different classifier algorithms on the Apache Spark platform with 5-fold cross-validation. Undersampling methods were used to deal with underlying class imbalance that hindered the performance of classifiers. Experiments were performed on both a curated version of the data, which included only annotated structures, and the non-curated data that included all structures from the original treatment plans. RESULTS: Random Forest provided the highest accuracies with F1 scores of 98.77 for lung and 95.06 for prostate on the curated data sets. Scores were lower with 95.67 for lung and 90.22 for prostate on the non-curated data sets, highlighting some of the challenges of classifying real clinical data. Including bony anatomy data and pooling information from all structures for the same patient both increased accuracies. In some cases, undersampling with k-Means clustering for class balancing improved classifier accuracy but in all experiments it significantly reduced run time compared to random undersampling. CONCLUSION: This work shows that structure sets can be relabeled using our approach with accuracies over 95% for many structure types when presented with curated data. Although accuracies dropped when using the full non-curated data sets, some structure types were still correctly labeled over 90% of the time. With similar results obtained on an external test data set, we can infer that the proposed models are likely to work on other clinical data sets.

Bacteriocins as an alternative in the treatment of infections by Staphylococcus aureus.

Staphylococcus aureus (S. aureus) is a highly versatile Gram-positive bacterium that is carried asymptomatically by up to 30% of healthy people, while being a major cause of healthcare-associated infections, making it a worldwide problem in clinical medicine. The adaptive evolution of S. aureus strains is demonstrated by its remarkable capacity to promptly develop high resistance to multiple antibiotics, thus limiting treatment choice. Nowadays, there is a continuous demand for an alternative to the use of antibiotics for S. aureus infections and a strategy to control the spread or to kill phylogenetically related strains. In this scenario, bacteriocins fit as with a promising and interesting alternative. These molecules are produced by a range of bacteria, defined as ribosomally synthesized peptides with bacteriostatic or bactericidal activity against a wide range of pathogens. This work reviews ascertained the main antibiotic-resistance mechanisms of S. aureus strains and the current, informative content concerning the applicability of the use of bacteriocins overlapping the use of conventional antibiotics in the context of S. aureus infections. Besides, we highlight the possible application of these biomolecules on an industrial scale in future work.

Anticancer and Antiviral Properties of Cardiac Glycosides: A Review to Explore the Mechanism of Actions.

Cardiac glycosides (CGs) have a long history of treating cardiac diseases. However, recent reports have suggested that CGs also possess anticancer and antiviral activities. The primary mechanism of action of these anticancer agents is by suppressing the Na+/k+-ATPase by decreasing the intracellular K+ and increasing the Na+ and Ca2+. Additionally, CGs were known to act as inhibitors of IL8 production, DNA topoisomerase I and II, anoikis prevention and suppression of several target genes responsible for the inhibition of cancer cell proliferation. Moreover, CGs were reported to be effective against several DNA and RNA viral species such as influenza, human cytomegalovirus, herpes simplex virus, coronavirus, tick-borne encephalitis (TBE) virus and Ebola virus. CGs were reported to suppress the HIV-1 gene expression, viral protein translation and alters viral pre-mRNA splicing to inhibit the viral replication. To date, four CGs (Anvirzel, UNBS1450, PBI05204 and digoxin) were in clinical trials for their anticancer activity. This review encapsulates the current knowledge about CGs as anticancer and antiviral drugs in isolation and in combination with some other drugs to enhance their efficiency. Further studies of this class of biomolecules are necessary to determine their possible inhibitory role in cancer and viral diseases.

Transcriptome profile of Corynebacterium pseudotuberculosis in response to iron limitation.

BACKGROUND: Iron is an essential micronutrient for the growth and development of virtually all living organisms, playing a pivotal role in the proliferative capability of many bacterial pathogens. The impact that the bioavailability of iron has on the transcriptional response of bacterial species in the CMNR group has been widely reported for some members of the group, but it hasn't yet been as deeply explored in Corynebacterium pseudotuberculosis. Here we describe for the first time a comprehensive RNA-seq whole transcriptome analysis of the T1 wild-type and the Cp13 mutant strains of C. pseudotuberculosis under iron restriction. The Cp13 mutant strain was generated by transposition mutagenesis of the ciuA gene, which encodes a surface siderophore-binding protein involved in the acquisition of iron. Iron-regulated acquisition systems are crucial for the pathogenesis of bacteria and are relevant targets to the design of new effective therapeutic approaches. RESULTS: Transcriptome analyses showed differential expression in 77 genes within the wild-type parental T1 strain and 59 genes in Cp13 mutant under iron restriction. Twenty-five of these genes had similar expression patterns in both strains, including up-regulated genes homologous to the hemin uptake hmu locus and two distinct operons encoding proteins structurally like hemin and Hb-binding surface proteins of C. diphtheriae, which were remarkably expressed at higher levels in the Cp13 mutant than in the T1 wild-type strain. These hemin transport protein genes were found to be located within genomic islands associated with known virulent factors. Down-regulated genes encoding iron and heme-containing components of the respiratory chain (including ctaCEF and qcrCAB genes) and up-regulated known iron/DtxR-regulated transcription factors, namely ripA and hrrA, were also identified differentially expressed in both strains under iron restriction. CONCLUSION: Based on our results, it can be deduced that the transcriptional response of C. pseudotuberculosis under iron restriction involves the control of intracellular utilization of iron and the up-regulation of hemin acquisition systems. These findings provide a comprehensive analysis of the transcriptional response of C. pseudotuberculosis, adding important understanding of the gene regulatory adaptation of this pathogen and revealing target genes that can aid the development of effective therapeutic strategies against this important pathogen.

Evaluation of the Common Molecular Basis in Alzheimer's and Parkinson's Diseases.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.

Propagation of an Aß Dodecamer Strain Involves a Three-Step Mechanism and a Key Intermediate.

Proteinaceous deposits composed of fibrillar amyloid-ß (Aß) are the primary neuropathological hallmarks in Alzheimer disease (AD) brains. The nucleation-dependent aggregation of Aß is a stochastic process with frequently observed heterogeneity in aggregate size, structure, and conformation that manifests in fibril polymorphism. Emerging evidence indicates that polymorphic variations in Aß fibrils contribute to phenotypic diversity and the rate of disease progression in AD. We recently demonstrated that a dodecamer strain derived from synthetic Aß42 propagates to morphologically distinct fibrils and selectively induces cerebral amyloid angiopathy phenotype in transgenic mice. This report supports the growing contention that stable oligomer strains can influence phenotypic outcomes by faithful propagation of their structures. Although we determined the mechanism of dodecamer propagation on a mesoscopic scale, the molecular details of the microscopic reactions remained unknown. Here, we have dissected and evaluated individually the kinetics of macroscopic phases in aggregation to gain insight into the process of strain propagation. The bulk rates determined experimentally in each phase were used to build an ensemble kinetic simulation model, which confirmed our observation that dodecamer seeds initially grow by monomer addition toward the formation of a key intermediate. This is followed by conversion of the intermediate to fibrils by oligomer elongation and association mechanisms. Overall, this report reveals important insights into the molecular details of oligomer strain propagation involved in AD pathology.

Multiple environmental stressors induce complex transcriptomic responses indicative of phenotypic outcomes in Western fence lizard.

BACKGROUND: The health and resilience of species in natural environments is increasingly challenged by complex anthropogenic stressor combinations including climate change, habitat encroachment, and chemical contamination. To better understand impacts of these stressors we examined the individual- and combined-stressor impacts of malaria infection, food limitation, and 2,4,6-trinitrotoluene (TNT) exposures on gene expression in livers of Western fence lizards (WFL, Sceloporus occidentalis) using custom WFL transcriptome-based microarrays. RESULTS: Computational analysis including annotation enrichment and correlation analysis identified putative functional mechanisms linking transcript expression and toxicological phenotypes. TNT exposure increased transcript expression for genes involved in erythropoiesis, potentially in response to TNT-induced anemia and/or methemoglobinemia and caused dose-specific effects on genes involved in lipid and overall energy metabolism consistent with a hormesis response of growth stimulation at low doses and adverse decreases in lizard growth at high doses. Functional enrichment results were indicative of inhibited potential for lipid mobilization and catabolism in TNT exposures which corresponded with increased inguinal fat weights and was suggestive of a decreased overall energy budget. Malaria infection elicited enriched expression of multiple immune-related functions likely corresponding to increased white blood cell (WBC) counts. Food limitation alone enriched functions related to cellular energy production and decreased expression of immune responses consistent with a decrease in WBC levels. CONCLUSIONS: Despite these findings, the lizards demonstrated immune resilience to malaria infection under food limitation with transcriptional results indicating a fully competent immune response to malaria, even under bio-energetic constraints. Interestingly, both TNT and malaria individually increased transcriptional expression of immune-related genes and increased overall WBC concentrations in blood; responses that were retained in the TNT x malaria combined exposure. The results demonstrate complex and sometimes unexpected responses to multiple stressors where the lizards displayed remarkable resiliency to the stressor combinations investigated.

Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks.

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (≈20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise.

SIMBA: a web tool for managing bacterial genome assembly generated by Ion PGM sequencing technology.

BACKGROUND: The evolution of Next-Generation Sequencing (NGS) has considerably reduced the cost per sequenced-base, allowing a significant rise of sequencing projects, mainly in prokaryotes. However, the range of available NGS platforms requires different strategies and software to correctly assemble genomes. Different strategies are necessary to properly complete an assembly project, in addition to the installation or modification of various software. This requires users to have significant expertise in these software and command line scripting experience on Unix platforms, besides possessing the basic expertise on methodologies and techniques for genome assembly. These difficulties often delay the complete genome assembly projects. RESULTS: In order to overcome this, we developed SIMBA (SImple Manager for Bacterial Assemblies), a freely available web tool that integrates several component tools for assembling and finishing bacterial genomes. SIMBA provides a friendly and intuitive user interface so bioinformaticians, even with low computational expertise, can work under a centralized administrative control system of assemblies managed by the assembly center head. SIMBA guides the users to execute assembly process through simple and interactive pages. SIMBA workflow was divided in three modules: (i) projects: allows a general vision of genome sequencing projects, in addition to data quality analysis and data format conversions; (ii) assemblies: allows de novo assemblies with the software Mira, Minia, Newbler and SPAdes, also assembly quality validations using QUAST software; and (iii) curation: presents methods to finishing assemblies through tools for scaffolding contigs and close gaps. We also presented a case study that validated the efficacy of SIMBA to manage bacterial assemblies projects sequenced using Ion Torrent PGM. CONCLUSION: Besides to be a web tool for genome assembly, SIMBA is a complete genome assemblies project management system, which can be useful for managing of several projects in laboratories. SIMBA source code is available to download and install in local webservers at http://ufmg-simba.sourceforge.net .

Corynebacterium pseudotuberculosis may be under anagenesis and biovar Equi forms biovar Ovis: a phylogenic inference from sequence and structural analysis.

BACKGROUND: Corynebacterium pseudotuberculosis can be classified into two biovars or biovars based on their nitrate-reducing ability. Strains isolated from sheep and goats show negative nitrate reduction and are termed biovar Ovis, while strains from horse and cattle exhibit positive nitrate reduction and are called biovar Equi. However, molecular evidence has not been established so far to understand this difference, specifically if these C. pseudotuberculosis strains are under an evolutionary process. RESULTS: The ERIC 1 + 2 Minimum-spanning tree from 367 strains of C. pseudotuberculosis showed that the great majority of biovar Ovis strains clustered together, but separately from biovar Equi strains that also clustered amongst themselves. Using evolutionarily conserved genes (rpoB, gapA, fusA, and rsmE) and their corresponding amino acid sequences, we analyzed the phylogenetic relationship among eighteen strains of C. pseudotuberculosis belonging to both biovars Ovis and Equi. Additionally, conserved point mutation based on structural variation analysis was also carried out to elucidate the genotype-phenotype correlations and speciation. We observed that the biovars are different at the molecular phylogenetic level and a probable anagenesis is occurring slowly within the species C. pseudotuberculosis. CONCLUSIONS: Taken together the results suggest that biovar Equi is forming the biovar Ovis. However, additional analyses using other genes and other bacterial strains are required to further support our anagenesis hypothesis in C. pseudotuberculosis.

DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis.

BACKGROUND: MicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive. METHODS: In this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network. RESULTS AND CONCLUSION: Our tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.