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The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA–DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein–chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein–chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood.MethodsWe developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology.ResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC.ConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

Funder: W. D. Armstrong Trust Fund, University of Cambridge, UK, Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.

BackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunesenescence.

Significance Immune activation and inflammation are predictors of serious non-AIDS events even in virally suppressed HIV-1−infected individuals. This does not apply to HIV-2−infected patients, who experience a form of attenuated HIV-1 disease. Here, we show that the HIV-1 matrix protein 17 (p17) binds to and activates the common beta chain receptor (βCR). The βCR-activating epitope on p17 is expressed on the matrix protein of HIV-1 ancestors but not on that of HIV-2 and its ancestors. Our finding highlights this epitope as a signature tracing the HIV-1 evolutionary trajectory that may have represented a critical step to enhance the HIV-1 ancestors aggressiveness and early human-to-human transmission. Whether this functional epitope actually marks the pathogenic difference between HIV-1 and HIV-2 needs further investigation., The HIV-1 matrix protein p17 (p17) is a pleiotropic molecule impacting on different cell types. Its interaction with many cellular proteins underlines the importance of the viral protein as a major determinant of human specific adaptation. We previously showed the proangiogenic capability of p17. Here, by integrating functional analysis and receptor binding, we identify a functional epitope that displays molecular mimicry with human erythropoietin (EPO) and promotes angiogenesis through common beta chain receptor (βCR) activation. The functional EPO-like epitope was found to be present in the matrix protein of HIV-1 ancestors SIV originated in chimpanzees (SIVcpz) and gorillas (SIVgor) but not in that of HIV-2 and its ancestor SIVsmm from sooty mangabeys. According to biological data, evolution of the EPO-like epitope showed a clear differentiation between HIV-1/SIVcpz-gor and HIV-2/SIVsmm branches, thus highlighting this epitope on p17 as a divergent signature discriminating HIV-1 and HIV-2 ancestors. P17 is known to enhance HIV-1 replication. Similarly to other βCR ligands, p17 is capable of attracting and activating HIV-1 target cells and promoting a proinflammatory microenvironment. Thus, it is tempting to speculate that acquisition of an epitope on the matrix proteins of HIV-1 ancestors capable of triggering βCR may have represented a critical step to enhance viral aggressiveness and early human-to-human SIVcpz/gor dissemination. The hypothesis that the p17/βCR interaction and βCR abnormal stimulation may also play a role in sustaining chronic activation and inflammation, thus marking the difference between HIV-1 and HIV-2 in term of pathogenicity, needs further investigation.

We consider sex differences in human facial morphology in the context of developmental change. We show that at puberty, the height of the upper face, between the lip and the brow, develops differently in males and females, and that these differences are not explicable in terms of sex differences in body size. We find the same dimorphism in the faces of human ancestors. We propose that the relative shortening in men and lengthening in women of the anterior upper face at puberty is the mechanistic consequence of extreme maxillary rotation during ontogeny. A link between this developmental model and sexual dimorphism is made for the first time, and provides a new set of morphological criteria to sex human crania. This finding has important implications for the role of sexual selection in the evolution of anthropoid faces and for theories of human facial attractiveness.

Funder: Fondi di Ateneo per la Ricerca (FAR-UNICAM) 2014-2015, Funder: European Commission Marie Sklodowska-Curie Actions H2020 RISE Metable– 645693, We isolated a novel bacterial strain from a prokaryotic consortium associated to the psychrophilic marine ciliate Euplotes focardii, endemic of the Antarctic coastal seawater. The 16S rDNA sequencing and the phylogenetic analysis revealed the close evolutionary relationship to the Antarctic marine bacterium Marinomonas sp. BSw10506 and the sub antarctic Marinomonas polaris. We named this new strain Marinomonas sp. ef1. The optimal growth temperature in LB medium was 22 °C. Whole genome sequencing and analysis showed a reduced gene loss limited to regions encoding for transposases. Additionally, five genomic islands, e.g. DNA fragments that facilitate horizontal gene transfer phenomena, were identified. Two open reading frames predicted from the genomic islands coded for enzymes belonging to the Nitro-FMN-reductase superfamily. One of these, the putative NAD(P)H nitroreductase YfkO, has been reported to be involved in the bioreduction of silver (Ag) ions and the production of silver nanoparticles (AgNPs). After the Marinomonas sp. ef1 biomass incubation with 1 mM of AgNO3 at 22 °C, we obtained AgNPs within 24 h. The AgNPs were relatively small in size (50 nm) and had a strong antimicrobial activity against twelve common nosocomial pathogenic microorganisms including Staphylococcus aureus and two Candida strains. To our knowledge, this is the first report of AgNPs biosynthesis by a Marinomonas strain. This biosynthesis may play a dual role in detoxification from silver nitrate and protection from pathogens for the bacterium and potentially for the associated ciliate. Biosynthetic AgNPs also represent a promising alternative to conventional antibiotics against common pathogens.

In the present work, we performed a comprehensive intra-tissue and inter-tissue network analysis of the human transcriptome. We generated an atlas of communities in co-expression networks in each of 49 tissues and evaluated their tissue specificity. UMAP embeddings of gene expression from the identified communities (representing nearly 18% of all genes) recovered biologically meaningful tissue clusters, based on tissue organ membership or known shared function. We developed an approach to quantify the conservation of global structure and estimate the sampling distribution of the distance between tissue clusters via bootstrapped manifolds. We found not only preserved local structure among clearly related tissues (e.g., the 13 brain regions) but also a strong correlation between the clustering of these related tissues relative to the remaining ones. Interestingly, brain tissues showed significantly higher variability in community size than non-brain (p = 1.55 × 10-4). We identified communities that capture some of our current knowledge about biological processes, but most are likely to encode novel and previously inaccessible functional information. For example, we found a 17-member community present across all of the brain regions, which shows significant enrichment for the nonsense-mediated decay pathway (adjusted p = 1.01 × 10-37). We also constructed multiplex architectures to gain insights into tissue-to-tissue mechanisms for regulation of communities in the transcriptome, including communities that are likely to play a functional role throughout the central nervous system (CNS) and communities that may participate in the interaction between the CNS and the enteric nervous system. Notably, new gene expression data can be embedded into our models to accelerate discoveries in high-dimensional molecular datasets. Our study provides a rich resource of co-expression networks, communities, multiplex architectures, and enriched pathways in a broad collection of tissues, to catalyse research into inter-tissue regulatory mechanisms and enable insights into their downstream phenotypic consequences.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

Motivation Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability and implementation The Parapred method is freely available as a webserver at http://www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred. Supplementary information Supplementary information is available at Bioinformatics online.

Background The difficulty in distinguishing infection by Zika virus (ZIKV) from other flaviviruses is a global health concern, particularly given the high risk of neurologic complications (including Guillain-Barre syndrome [GBS]) with ZIKV infection. Methods We developed quantitative frameworks to compare and explore infectome, diseasome, and comorbidity of ZIKV infections. We analyzed gene expression microarray and RNA-Seq data from ZIKV, West Nile fever (WNF), chikungunya, dengue, yellow fever, Japanese encephalitis virus, GBS, and control datasets. Using neighborhood-based benchmarking and multilayer network topology, we constructed relationship networks based on the Online Mendelian Inheritance in Man database and our identified significant genes. Results ZIKV infections showed dysregulation in expression of 929 genes. Forty-seven genes were highly expressed in both ZIKV and dengue infections. However, ZIKV shared

Background Human embryonic stem cells (hESCs) potentially offer new routes to study, on the basis of the Developmental Origins of Health and Disease (DOHaD) concept, how the maternal environment during pregnancy influences the offspring’s health and can predispose to chronic disease in later life. Reactive oxygen species (ROS), antioxidant defences and cellular redox status play a key function in gene expression regulation and are involved in diabetes and metabolic syndromes as in ageing. Methods We have, therefore, designed an in vitro cell model of oxidative stress by exposing hESCs to hydrogen peroxide (H2O2) during 72 h, in order to resemble the period of preimplantation embryonic development. Results We have analysed the global gene expression profiles of hESCs (HUES3) exposed to non-cytotoxic H2O2 concentrations, using Illumina microarray HT-12 v4, and we found the differential expression of 569 upregulated and 485 downregulated genes. The most affected gene ontology categories were those related with RNA processing and splicing, oxidation reduction and sterol metabolic processes. We compared our findings with a published RNA-seq profiling dataset of human embryos developed in vitro, thereupon exposed to oxidative stress, and we observed that one of the common downregulated genes between this publication and our data, NEDD1, is involved in centrosome structure and function. Conclusions Therefore, we assessed the presence of supernumerary centrosomes and showed that the percentage of cells with more than two centrosomes increased acutely with H2O2 treatment in hESCs (HUES3 and 7) and in a control somatic cell line (Hs27), inducing a premature entry into senescence. Electronic supplementary material The online version of this article (doi:10.1186/s13287-017-0602-6) contains supplementary material, which is available to authorized users.

Background Not all the mutations are equally important for the development of metastasis. What about their order? The survival of cancer cells from the primary tumour site to the secondary seeding sites depends on the occurrence of very few driver mutations promoting oncogenic cell behaviours. Usually these driver mutations are among the most effective clinically actionable target markers. The quantitative evaluation of the effects of a mutation across primary and secondary sites is an important challenging problem that can lead to better predictability of cancer progression trajectory. Results We introduce a quantitative model in the framework of Cellular Automata to investigate the effects of metabolic mutations and mutation order on cancer stemness and tumour cell migration from breast, blood to bone metastasised sites. Our approach models three types of mutations: driver, the order of which is relevant for the dynamics, metabolic which support cancer growth and are estimated from existing databases, and non–driver mutations. We integrate the model with bioinformatics analysis on a cancer mutation database that shows metabolism-modifying alterations constitute an important class of key cancer mutations. Conclusions Our work provides a quantitative basis of how the order of driver mutations and the number of mutations altering metabolic processis matter for different cancer clones through their progression in breast, blood and bone compartments. This work is innovative because of multi compartment analysis and could impact proliferation of therapy-resistant clonal populations and patient survival. Mathematical modelling of the order of mutations is presented in terms of operators in an accessible way to the broad community of researchers in cancer models so to inspire further developments of this useful (and underused in biomedical models) methodology. We believe our results and the theoretical framework could also suggest experiments to measure the overall personalised cancer mutational signature. Electronic supplementary material The online version of this article (10.1186/s12920-019-0541-4) contains supplementary material, which is available to authorized users.

Background Little is understood of the molecular mechanisms involved in the earliest cell fate decision in human development, leading to the establishment of the trophectoderm (TE) and inner cell mass (ICM) stem cell population. Notably, there is a lack of understanding of how transcriptional networks arise during reorganisation of the embryonic genome post-fertilisation. Results We identified a hierarchical structure of preimplantation gene network modules around the time of embryonic genome activation (EGA). Using network models along with eukaryotic initiation factor (EIF) and epigenetic-associated gene expression we defined two sets of blastomeres that exhibited diverging tendencies towards ICM or TE. Analysis of the developmental networks demonstrated stage specific EIF expression and revealed that histone modifications may be an important epigenetic regulatory mechanism in preimplantation human embryos. Comparison to published RNAseq data confirmed that during EGA the individual 8-cell blastomeres are transcriptionally primed for the first lineage decision in development towards ICM or TE. Conclusions Using multiple systems biology approaches to compare developmental stages in the early human embryo with single cell transcript data from blastomeres, we have shown that blastomeres considered to be totipotent are not transcriptionally equivalent. Furthermore we have linked the developmental interactome to individual blastomeres and to later cell lineage. This has clinical implications for understanding the impact of fertility treatments and developmental programming of long term health. Electronic supplementary material The online version of this article (10.1186/s12864-019-5558-8) contains supplementary material, which is available to authorized users.

BackgroundWelding exposes different types of fumes, gases and radiant energy that can be potentially dangerous for unsafe welder’s health. Welding fumes (WFs) are a significant problem among all those exposed. WFs are a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. If a welder inhales such fumes in large quantities over a long period of time, there is a risk of various neurodegenerative diseases (NDGDs) development.MethodsWe developed quantitative frameworks to identify the genetic relationship of WFs and NDGDs. We analyzed Gene Expression microarray data from WFs exposed tissues and NDGDs including Parkinson’s disease (PD), Alzheimer’s disease (AD), Lou Gehrig’s disease (LGD), Epilepsy disease (ED), Multiple Sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological path- ways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking.ResultsWe observed that WFs shares 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis showed that WFs are responsible for the progression of PD, AD, LGD, ED and MSD neurodegenerative diseases.ConclusionOur developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue–residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences., Author Summary Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base differences between individual genomes that lead to amino acid changes in protein sequences. They may influence an individual's susceptibility to disease or response to drugs through their impacts on a protein's structure and hence cause functional changes. In this paper, we present a new methodology to estimate the impact of nsSNPs on disease susceptibility. This is made possible by characterising the protein structure and the change of structural stability due to nsSNPs. We show that our computer program Bongo, which describes protein structures as interlinked amino acids, can identify conformational changes resulting from nsSNPs that are closely associated with pathological consequences. Bongo requires only structural information to analyze nsSNPs and thus is complementary to methods that use evolutionary information. Bongo helps us investigate the suggestion that most disease-causing mutations disturb structural features of proteins, thus affecting their stability. We anticipate that making Bongo available to the community will facilitate a better understanding of disease-associated nsSNPs and thus benefit personal medicine in the future.

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organisation and ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi –omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications. To reconstruct the two haplotypes, we addressed the weighted Minimum Error Correction (wMEC) problem, which is a successful approach for haplotype assembly. This NP-hard problem consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets, with the least number of corrections to the SNP values. To this aim, we propose here GenHap, a novel computational method for haplotype assembly based on Genetic Algorithms, yielding optimal solutions by means of a global search process. In order to evaluate the effectiveness of our approach, we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454 and PacBio RS II sequencing technologies. We compared the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype phasing. Our results show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 4x faster than HapCol in the case of Roche/454 instances and up to 20x faster when compared on the PacBio RS II dataset. Finally, we assessed the performance of GenHap on two different real datasets. Future-generation sequencing technologies, producing longer reads with higher coverage, can highly benefit from GenHap, thanks to its capability of efficiently solving large instances of the haplotype assembly problem., Comment: Accepted for publication in BMC Bioinformatics

Antibodies play an essential role in the immune system of vertebrates and are vital tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method outperforms the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. The Parapred method is freely available at https://github.com/eliberis/parapred for download.

The success of marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of well-defined cell surface markers specific for diverse progenitor populations. An inherent problem with this approach is that the presence of specific cell surface markers does not directly reflect the transcriptional state of a cell. Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in zebrafish and order cells along their differentiation trajectory, based on their global transcriptional differences. Within the population of transcriptionally similar stem and progenitor cells, our analysis reveals considerable cell-to-cell differences in their probability to transition to another committed state. Once fate decision is executed, the suppression of transcription of ribosomal genes and upregulation of lineage-specific factors coordinately controls lineage differentiation. Evolutionary analysis further demonstrates that this haematopoietic programme is highly conserved between zebrafish and higher vertebrates., Traditionally marker-based approaches are used to define haematopoietic cell type or state. Here, the authors use single-cell RNA-seq to establish a cellular hierarchy of lineage development in zebrafish haematopoiesis, and propose a refined model of developmental progression of haematopoietic cells.

Background Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship. Results We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium’s metabolism, such as changes in the bacterium’s growth in response to different environmental conditions. Conclusions After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0395-3) contains supplementary material, which is available to authorized users.

Summary The Antarctic strain Pseudoalteromonas haloplanktis TAC125 is one of the model organisms of cold-adapted bacteria and is currently exploited as a new alternative expression host for numerous biotechnological applications. Here, we investigated several metabolic features of this strain through in silico modelling and functional integration of –omics data. A genome-scale metabolic model of P. haloplanktis TAC125 was reconstructed, encompassing information on 721 genes, 1133 metabolites and 1322 reactions. The predictive potential of this model was validated against a set of experimentally determined growth rates and a large dataset of growth phenotypic data. Furthermore, evidence synthesis from proteomics, phenomics, physiology and metabolic modelling data revealed possible drawbacks of cold-dependent changes in gene expression on the overall metabolic network of P. haloplanktis TAC125. These included, for example, variations in its central metabolism, amino acid degradation and fatty acid biosynthesis. The genome-scale metabolic model described here is the first one reconstructed so far for an Antarctic microbial strain. It allowed a system-level investigation of variations in cellular metabolic fluxes following a temperature downshift. It represents a valuable platform for further investigations on P. haloplanktis TAC125 cellular functional states and for the design of more focused strategies for its possible biotechnological exploitation.

Bacteria has been proposed in recent years as one approach to achieve molecular communication. Bacterial cells can harbour DNA encoded information and can deliver this information from one nanomachine to another by swimming (motility). One aspect of bacterial communication that could further enhance the performance of information delivery in bacterial nanonetworks is conjugation . Conjugation involves forming a physical connection between the bacteria in order to transfer DNA molecules (i.e., plasmids or chromosomes). However, the fragile physical connection between the bacteria is prone to breakage, in particular under mechanical stress. In this paper, a simple Forward and Reverse coding process is proposed to enhance the performance of information delivery in bacterial nanonetworks. The coding process involves segmenting messages into blocks and integrating this into the bacterial chromosome. Simulation work have been conducted to validate the efficiency of the coding process, where the results have shown positive performance compared to approaches that do not utilize coding or pure conjugation.

Drug treatments often perturb the activities of certain pathways, sets of functionally related genes. Examining pathways/gene sets that are responsive to drug treatments instead of a simple list of regulated genes can advance our understanding about such cellular processes after perturbations. In general, pathways do not work in isolation and their connections can cause secondary effects. To address this, we present a new method to better identify pathway responsiveness to drug treatments and simultaneously to determine between-pathway interactions. Firstly, we developed a Bayesian matrix factorisation of gene expression data together with known gene-pathway memberships to identify pathways perturbed by drugs. Secondly, in order to determine the interactions between pathways, we implemented a Gaussian Markov Random Field (GMRF) under the matrix factorization framework. Assuming a Gaussian distribution of pathway responsiveness, we calculated the correlations between pathways. We applied the combination of the Bayesian factor model and the GMRF to analyse gene expression data of 1169 drugs with 236 known pathways, 66 of which were disease-related pathways. Our model yielded a significantly higher average precision than the existing methods for identifying pathway responsiveness to drugs that affected multiple pathways. This implies the advantage of the between-pathway interactions and confirms our assumption that pathways are not independent, an aspect that has been commonly overlooked in the existing methods. Additionally, we demonstrate four case studies illustrating that the between-pathway network enhances the performance of pathway identification and provides insights into disease comorbidity, drug repositioning, and tissue-specific comparative analysis of drug treatments.

Molecular communication is a new paradigm for nanomachines to exchange information, by utilizing biological mechanism and/or components to transfer information (e.g., molecular diffusion, neuronal networks, molecular motors). One possible approach for molecular communication is through the use of bacteria, which can act as carriers for DNA-based information, i.e., plasmids. This paper analyzes multi-hop molecular nanonetworks that utilize bacteria as a carrier. The proposed approach combines different properties of bacteria to enable multi-hop transmission, such as conjugation and chemotaxis-based motility. Various analyses have been performed, including the correlation between the success rate of plasmid delivery to the destination node, and the role of conjugation in enabling this; as well as analyses on the impact of large topology shapes (e.g., Grid, Random, and Scale-free) on the success rate of plasmid delivery for multiple source-destination nanonetworks. A further solution proposed in this paper is the application of antibiotics to act as filters on illegitimate messages that could be delivered by the bacteria. Our evaluation, which has been conducted through a series of simulations, has shown that numerous bacteria properties fit to properties required for communication networking (e.g., packet filtering, routing, addressing).

The human immunodeficiency virus type-1 (HIV-1) is the etiological agent of the acquired immunodeficiency syndrome (AIDS), a disease highly lethal in the absence of combination antiretroviral therapy. HIV infects CD4 + cells of the immune system (T cells, mono- cyte-macrophages and dendritic cells) via interaction with a universal primary receptor, the CD4 molecule, followed by a mandatory interaction with a second receptor (co-receptor) belonging to the chemokine receptor family. Apart from some rare cases, two chemokine receptors have been evolutionarily selected to accomplish this need for HIV-1: CCR5 and CXCR4. Yet, usage of these two receptors appears to be neither casual nor simply explained by their levels of cell surface expression. While CCR5 use is the universal rule at the start of every infection regardless of the transmission route (blood-related, sexual or mother to child), CXCR4 utilization emerges later in disease coinciding with the immunological deficient phase of infection. Moreover, in most instances CXCR4 use as viral entry co-receptor is associated with maintenance of CCR5 use. Since antiviral agents preventing CCR5 utilization by the virus are already in use, while others targeting either CCR5 or CXCR4 (or both) are under investigation, understanding the biological correlates of this "asymmetrical" utilization of HIV entry co-receptors bears relevance for the clinical choice of which therapeutics should be administered to infected individuals. We will here summarize the basic knowledge and the hypotheses underlying the puzzling and yet unequivocal role of CXCR4 in HIV-1 infection.

Publicly available multi-omic databases, in particular if associated with medical annotations, are rich resources with the potential to lead a rapid transition from high-throughput molecular biology experiments to better clinical outcomes for patients. In this work, we propose a model for multi-omic data integration (i.e., genetic variations, gene expression, genome conformation, and epigenetic patterns), which exploits a multi-layer network approach to analyse, visualize, and obtain insights from such biological information, in order to use achieved results at a macroscopic level. Using this representation, we can describe how driver and passenger mutations accumulate during the development of diseases providing, for example, a tool able to characterize the evolution of cancer. Indeed, our test case concerns the MCF-7 breast cancer cell line, before and after the stimulation with estrogen, since many datasets are available for this case study. In particular, the integration of data about cancer mutations, gene functional annotations, genome conformation, epigenetic patterns, gene expression, and metabolic pathways in our multi-layer representation will allow a better interpretation of the mechanisms behind a complex disease such as cancer. Thanks to this multi-layer approach, we focus on the interplay of chromatin conformation and cancer mutations in different pathways, such as metabolic processes, that are very important for tumor development. Working on this model, a variance analysis can be implemented to identify normal variations within each omics and to characterize, by contrast, variations that can be accounted to pathological samples compared to normal ones. This integrative model can be used to identify novel biomarkers and to provide innovative omic-based guidelines for treating many diseases, improving the efficacy of decision trees currently used in clinic.

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.

Schizophrenia is a severe and highly heritable psychiatric disorder affecting approximately 1% of the population. Genome-wide association studies have identified 108 independent genetic loci with genome-wide significance but their functional importance has yet to be elucidated. Here, we develop a novel strategy based on network analysis of protein-protein interactions (PPI) to infer biological function associated with variants most strongly linked to illness risk. We show that the schizophrenia loci are strongly linked to synaptic transmission (P FWE < .001) and ion transmembrane transport (P FWE = .03), but not to ontological categories previously found to be shared across psychiatric illnesses. We demonstrate that brain expression of risk-linked genes within the identified processes is strongly modulated during birth and identify a set of synaptic genes consistently changed across multiple brain regions of adult schizophrenia patients. These results suggest synaptic function as a developmentally determined schizophrenia process supported by the illness's most associated genetic variants and their PPI networks. The implicated genes may be valuable targets for mechanistic experiments and future drug development approaches.

Background Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured. Results To this end, we model a large compendium of growth conditions as a multiplex network consisting of transcriptomic and fluxomic layers, and we propose a multi-omic network approach to infer similarity of growth conditions by integrating layers of the multiplex network. Each node of the network represents a single condition, while edges are similarities between conditions, as measured by phenotypic and transcriptomic properties on different layers of the network. We then fuse these layers into one network, therefore capturing a global network of conditions and the associated similarities across two omic levels. We apply this multi-omic fusion to an updated genome-scale reconstruction of Escherichia coli that includes underground metabolism and new gene-protein-reaction associations. Conclusions Our method can be readily used to evaluate and cross-compare different collections of conditions among different species. Acquiring multi-omic information on the topology of the space of experimental conditions makes it possible to infer the position and to build condition-specific models of untested or incomplete profiles for which experimental data is not available. Our weighted network fusion method for genome-scale models is freely available at https://github.com/maxconway/SNFtool. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0912-1) contains supplementary material, which is available to authorized users.

As the field of molecular communication continues to grow, numerous solutions have been proposed to enable communication between nanomachines. Amongst these solutions, bacteria communication nanonetworks has been proposed as a promising approach for molecular communication. This is driven by a number of attractive properties found in bacteria, which includes biased motility toward the destination through chemotaxis process, as well as the ability of bacteria to transfer genetic information between each other using conjugation. Bacterial conjugation is a major mechanism for Lateral Gene Transfer (LGT) that enables information transfer among bacteria. In this paper, we propose an opportunistic routing process in bacteria communication network using these two properties. The paper presents the simulation work to analyze the performance of message delivery for three different topology shapes, which includes grid, hexagon, and T-shape topologies. The aim of simulating on different shape topologies is to determine the impact that conjugation will have to improve message delivery. In all topologies, the use of conjugation helped improve the reliability of message delivery to the destination point. The paper will analyze various commonly used metrics used in communication networks, such as the average delay, the number of messages, as well as the distribution of messages and their originating node. The conjugation process is most beneficial in complexed shaped topologies, where the directionality from the source to the destination is a number of hops apart, as represented in the T-shape topology.

Several pathological mutations in the human mitochondrial genome have been characterized based on medical, genetic and biochemical evidence. The observation that the structure and core functions of the mitochondrial genome are conserved from animals to man suggests that the analysis of animal variation may be informative to further characterize, and possibly predict, human pathological variants. We studied the distribution of sequence site-wise diversity and structural heterogeneity (based on several scales of hydrophobicity and supercomplex classification of mitochondrial genes) at different taxonomic levels in ∼15,000 human and animal genomes. We found that human pathological mutations tend to lay in regions of low diversity and that states that are pathological in humans appear to be extremely rare in animals, with two noticeable exceptions (T10663C and C14568T). Focusing on hydrophobicity, as possibly the most general site-wise functional parameter of a protein, we deploy the observed range of hydrophobicity in mammals as a proxy for the range of permissible states compatible with an efficient functioning of the mitochondrial machinery. We show that, while non pathological human variants tend to fall within the hypothesized range, pathological mutations generally fall outside this range. We further analyzed this distribution quantitatively to show that the estimated probability of observed states can indeed be used to predict the pathogenicity of a mutation in humans. This study provides a proof of principle that animal data can indeed be informative to predict the pathogenicity of a human mutation alongside, or in the absence of, additional evidence.

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.

Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct) through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET). In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics., Author Summary Breast cancer is caused by genetic mutations leading to uncontrollable cell reproduction. During successive proliferations, the progenies of tumour cells acquire further mutations increasing their heterogeneity. Among the tumoural mutated cells, there are some which present specific markers of increased aggressiveness and resistance. Sufficiently skilled cancer cells detach from the mammary epithelial cells, enter the blood vessels becoming circulating tumour cells, and reach the bone tissue where they seed. Breast cancer survival probability is the statistical representation of clinical data describing the times patients will survive after the diagnosis of the disease. Breast cancer survival is strongly correlated to genetic markers which increase the resistance and the invading skills of cancer cells but, it is poorly correlated to the amount of circulating tumour cells. To improve the understanding of the dynamic progression of the disease and assisting biologists in the interpretation of results and in experimental design, we developed a mathematical model encompassing the evolution of cancer cells originated in the breast, passing through the circulatory system, and invading the bone tissue based on survival probabilities of patients with different genetic expressions. The model allows us to strongly correlate the gene expression data of cancer cells with the survival probability by identifying the circulating tumour cells responsible for the formation of metastasis. Survival probabilities generated with the model are a useful tool to identify the presence of hidden markers not yet taken into consideration and study the effects of drugs’ administration.

The representation, integration and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture (3C) allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighbourhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

The field of forensic science is increasingly based on biomolecular data and many European countries are establishing forensic databases to store DNA profiles of crime scenes of known offenders and apply DNA testing. The field is boosted by statistical and technological advances such as DNA microarray sequencing, TFT biosensors, machine learning algorithms, in particular Bayesian networks, which provide an effective way of evidence organization and inference. The aim of this article is to discuss the state of art potentialities of bioinformatics in forensic DNA science. We also discuss how bioinformatics will address issues related to privacy rights such as those raised from large scale integration of crime, public health and population genetic susceptibility-to-diseases databases.

The human genome contains repetitive DNA at different level of sequence length, number and dispersion. Highly repetitive DNA is particularly rich in homo- and di-nucleotide repeats, while middle repetitive DNA is rich of families of interspersed, mobile elements hundreds of base pairs (bp) long, among which belong the Alu families. A link between homo- and di-polymeric tracts and mobile elements has been recently highlighted. In particular, the mobility of Alu repeats, which form 10% of the human genome, has been correlated with the length of poly(A) tracts located at one end of the Alu. These tracts have a rigid and non-bendable structure and have an inhibitory effect on nucleosomes, which normally compact the DNA. We performed a statistical analysis of the genome-wide distribution of lengths and inter-tract separations of poly(X) and poly(XY) tracts in the human genome. Our study shows that in humans the length distributions of these sequences reflect the dynamics of their expansion and DNA replication. By means of general tools from linguistics, we show that the latter play the role of highly-significant content-bearing terms in the DNA text. Furthermore, we find that such tracts are positioned in a non-random fashion, with an apparent periodicity of 150 bases. This allows us to extend the link between repetitive, highly mobile elements such as Alus and low-complexity words in human DNA. More precisely, we show that Alus are sources of poly(X) tracts, which in turn affect in a subtle way the combination and diversification of gene expression and the fixation of multigene families.

Tracing the history of molecular changes in coronaviruses using phylogenetic methods can provide powerful insights into the patterns of modification to sequences that underlie alteration to selective pressure and molecular function in the SARS-CoV (severe acute respiratory syndrome coronavirus) genome. The topology and branch lengths of the phylogenetic relationships among the family Coronaviridae, including SARS-CoV, have been estimated using the replicase polyprotein. The spike protein fragments S1 (involved in receptor-binding) and S2 (involved in membrane fusion) have been found to have different mutation rates. Fragment S1 can be further divided into two regions (S1A, which comprises approximately the first 400 nucleotides, and S1B, comprising the next 280) that also show different rates of mutation. The phylogeny presented on the basis of S1B shows that SARS-CoV is closely related to MHV (murine hepatitis virus), which is known to bind the murine receptor CEACAM1. The predicted structure, accessibility and mutation rate of the S1B region is also presented. Because anti-SARS drugs based on S2 heptads have short half-lives and are difficult to manufacture, our findings suggest that the S1B region might be of interest for anti-SARS drug discovery.

Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes, including inflammation, allergy and neoplasia. Here we use maximum likelihood methods and bootstrap methods to investigate both the phylogenetic relationships in a large set of human chemokine receptor sequences and the relationships between chemokine receptors and their nearest neighbors. We found that CCR and CXCR families are not homogeneous. We also provide evidences that angiotensin receptors are the closest neighbors. Other close neighbors include opioid, somatostatin and melanin-concentrating hormone receptors. The phylogenetic analysis suggests ancient paralogous relationships and establishes a link between immune, metabolic and neural systems modulation. We complement our findings with a structural analysis based on wavelet methods of the major branches of chemokine receptors phylogeny. We hypothesize that receptors very close in the tree can form heterodimers. Our analyses reveal different characteristics of amino acid hydrophobicity and volume propensity in the different subfamilies. We also found that the second extra-cytoplasmic loop has higher rates of evolution than the internal loops and transmembrane segments, suggesting that selection, shifting, reassignments and broadening of receptor binding specificities involve mainly this loop.

It is probable that, increasingly, genome investigations are going to be based on statistical formalization. This review summarizes the state of art and potentiality of using statistics in microbial genome analysis. First, I focus on recent advances in functional genomics, such as finding genes and operons, identifying gene conversion events, detecting DNA replication origins and analysing regulatory sites. Then I describe how to use phylogenetic methods in genome analysis and methods for genome-wide scanning for positively selected amino acids. I conclude with speculations on the future course of genome statistical modeling.

In this paper, we discuss data and methodological challenges for building bacterial communication networks using two examples: E. coli as a flagellate bacterium and G. sulfurreducens as a biofilm forming bacterium. We first highlight the link between the bacterial network communication design with respect to metabolic information processing design. The potentialities of designing routing network schemes described previously in the literature and based on bacteria motility and genetic message exchanges will depend on the genes coding for the intracellular and intercellular signalling pathways. In bacteria, the “mobilome” is related to horizontal gene transfer. Bacteria trade-off the acquisition of new genes which could improve their survival (and often their communication bandwidth), keeping their genome small enough to ensure quick DNA replication and fast increase of the biomass to speed up cell division. First, by using a multi-objective optimisation procedure, we search for the optimal trade-off between energy production, which is a requirement for the motility, and biomass growth, which is related to the overall survival and fitness of the bacterium. We use flux balance analysis of the genome-scale biochemical network of E. coli k-13 MG1655. Then, as a second case study, we analyse the electric properties and biomass trade-off of the bacterium G. sulfurreducens , which constructs an electric biofilm where electrons move across the nanowires. Here we discuss the potentialities of optimisation methodologies to design and select bacterial strains with desiderata properties. The optimisation methodologies establish also a relation between metabolism, network communication and computing. Moreover, we point to genetic design and synthetic biology as key areas to develop bacterial nano communication networks.

Motivation: Completing the genome sequence of an organism is an important task in comparative, functional and structural genomics. However, this remains a challenging issue from both a computational and an experimental viewpoint. Genome scaffolding (i.e. the process of ordering and orientating contigs) of de novo assemblies usually represents the first step in most genome finishing pipelines. Results: In this article we present MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalizes the scaffolding problem by means of a combinatorial optimization formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries. This makes usability and running time two additional important features of our method. Moreover, benchmarks and tests on real bacterial datasets showed that MeDuSa is highly accurate and, in most cases, outperforms traditional scaffolders. The possibility to use MeDuSa on eukaryotic datasets has also been evaluated, leading to interesting results. Availability and implementation: MeDuSa web server: http://combo.dbe.unifi.it/medusa. A stand-alone version of the software can be downloaded from https://github.com/combogenomics/medusa/releases. All results presented in this work have been obtained with MeDuSa v. 1.3. Contact: marco.fondi@unifi.it Supplementary information: Supplementary data are available at Bioinformatics online.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied. In our analysis, we compared the most recent network penalized Cox models for high-dimensional survival data aimed to determine pathway structures and biomarkers involved into cancer progression.

Bacterial metabolism is an important source of novel products/processes for everyday life and strong efforts are being undertaken to discover and exploit new usable substances of microbial origin. Computational modeling and in silico simulations are powerful tools in this context since they allow the exploration and a deeper understanding of bacterial metabolic circuits. Many approaches exist to quantitatively simulate chemical reaction fluxes within the whole microbial metabolism and, regardless of the technique of choice, metabolic model reconstruction is the first step in every modeling pipeline. Reconstructing a metabolic network consists in drafting the list of the biochemical reactions that an organism can carry out together with information on cellular boundaries, a biomass assembly reaction, and exchange fluxes with the external environment. Building up models able to represent the different functional cellular states is universally recognized as a tricky task that requires intensive manual effort and much additional information besides genome sequence. In this chapter we present a general protocol for metabolic reconstruction in bacteria and the main challenges encountered during this process.