Bridge: A New Algorithm for Rooting Orthologous Genes in Large-Scale Evolutionary Analyses.

Orthology information has been used for searching patterns in high-dimensional data, allowing transferring functional information between species. The key concept behind this strategy is that orthologous genes share ancestry to some extent. While reconstructing the history of a single gene is feasible with the existing computational resources, the reconstruction of entire biological systems remains challenging. In this study, we present Bridge, a new algorithm designed to infer the evolutionary root of orthologous genes in large-scale evolutionary analyses. The Bridge algorithm infers the evolutionary root of a given gene based on the distribution of its orthologs in a species tree. The Bridge algorithm is implemented in R and can be used either to assess genetic changes across the evolutionary history of orthologous groups or to infer the onset of specific traits in a biological system.

Severe COVID-19 patients exhibit elevated levels of autoantibodies targeting cardiolipin and platelet glycoprotein with age: a systems biology approach.

Age is a significant risk factor for the coronavirus disease 2019 (COVID-19) severity due to immunosenescence and certain age-dependent medical conditions (e.g., obesity, cardiovascular disorder, and chronic respiratory disease). However, despite the well-known influence of age on autoantibody biology in health and disease, its impact on the risk of developing severe COVID-19 remains poorly explored. Here, we performed a cross-sectional study of autoantibodies directed against 58 targets associated with autoimmune diseases in 159 individuals with different COVID-19 severity (71 mild, 61 moderate, and 27 with severe symptoms) and 73 healthy controls. We found that the natural production of autoantibodies increases with age and is exacerbated by SARS-CoV-2 infection, mostly in severe COVID-19 patients. Multiple linear regression analysis showed that severe COVID-19 patients have a significant age-associated increase of autoantibody levels against 16 targets (e.g., amyloid ß peptide, ß catenin, cardiolipin, claudin, enteric nerve, fibulin, insulin receptor a, and platelet glycoprotein). Principal component analysis with spectrum decomposition and hierarchical clustering analysis based on these autoantibodies indicated an age-dependent stratification of severe COVID-19 patients. Random forest analysis ranked autoantibodies targeting cardiolipin, claudin, and platelet glycoprotein as the three most crucial autoantibodies for the stratification of severe COVID-19 patients ≥50 years of age. Follow-up analysis using binomial logistic regression found that anti-cardiolipin and anti-platelet glycoprotein autoantibodies significantly increased the likelihood of developing a severe COVID-19 phenotype with aging. These findings provide key insights to explain why aging increases the chance of developing more severe COVID-19 phenotypes.

Metagenomic Analyses Reveal the Influence of Depth Layers on Marine Biodiversity on Tropical and Subtropical Regions.

The emergence of open ocean global-scale studies provided important information about the genomics of oceanic microbial communities. Metagenomic analyses shed light on the structure of marine habitats, unraveling the biodiversity of different water masses. Many biological and environmental factors can contribute to marine organism composition, such as depth. However, much remains unknown about microbial communities' taxonomic and functional features in different water layer depths. Here, we performed a metagenomic analysis of 76 publicly available samples from the Tara Ocean Project, distributed in 8 collection stations located in tropical or subtropical regions, and sampled from three layers of depth (surface water layer-SRF, deep chlorophyll maximum layer-DCM, and mesopelagic zone-MES). The SRF and DCM depth layers are similar in abundance and diversity, while the MES layer presents greater diversity than the other layers. Diversity clustering analysis shows differences regarding the taxonomic content of samples. At the domain level, bacteria prevail in most samples, and the MES layer presents the highest proportion of archaea among all samples. Taken together, our results indicate that the depth layer influences microbial sample composition and diversity.

MEDUSA: A Pipeline for Sensitive Taxonomic Classification and Flexible Functional Annotation of Metagenomic Shotgun Sequences.

Metagenomic studies unravel details about the taxonomic composition and the functions performed by microbial communities. As a complete metagenomic analysis requires different tools for different purposes, the selection and setup of these tools remain challenging. Furthermore, the chosen toolset will affect the accuracy, the formatting, and the functional identifiers reported in the results, impacting the results interpretation and the biological answer obtained. Thus, we surveyed state-of-the-art tools available in the literature, created simulated datasets, and performed benchmarks to design a sensitive and flexible metagenomic analysis pipeline. Here we present MEDUSA, an efficient pipeline to conduct comprehensive metagenomic analyses. It performs preprocessing, assembly, alignment, taxonomic classification, and functional annotation on shotgun data, supporting user-built dictionaries to transfer annotations to any functional identifier. MEDUSA includes several tools, as fastp, Bowtie2, DIAMOND, Kaiju, MEGAHIT, and a novel tool implemented in Python to transfer annotations to BLAST/DIAMOND alignment results. These tools are installed via Conda, and the workflow is managed by Snakemake, easing the setup and execution. Compared with MEGAN 6 Community Edition, MEDUSA correctly identifies more species, especially the less abundant, and is more suited for functional analysis using Gene Ontology identifiers.

TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves.

MOTIVATION: Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees. RESULTS: Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies. AVAILABILITY AND IMPLEMENTATION: TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species.

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

Which came first, the transcriptional regulator or its target genes? An evolutionary perspective into the construction of eukaryotic regulons.

Eukaryotic regulons are regulatory units formed by a set of genes under the control of the same transcription factor (TF). Despite the functional plasticity, TFs are highly conserved and recognize the same DNA sequences in different organisms. One of the main factors that confer regulatory specificity is the distribution of the binding sites of the TFs along the genome, allowing the configuration of different transcriptional regulatory networks (TRNs) from the same regulator. A similar scenario occurs between tissues of the same organism, where a TRN can be rewired by epigenetic factors, modulating the accessibility of the TF to its binding sites. In this article we discuss concepts that can help to formulate testable hypotheses about the construction of regulons, exploring the presence and absence of the elements that form a TRN throughout the evolution of an ancestral lineage. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Systems Biology-Based Analysis Indicates Global Transcriptional Impairment in Lead-Treated Human Neural Progenitor Cells.

Lead poisoning effects are wide and include nervous system impairment, peculiarly during development, leading to neural damage. Lead interaction with calcium and zinc-containing metalloproteins broadly affects cellular metabolism since these proteins are related to intracellular ion balance, activation of signaling transduction cascades, and gene expression regulation. In spite of lead being recognized as a neurotoxin, there are gaps in knowledge about the global effect of lead in modulating the transcription of entire cellular systems in neural cells. In order to investigate the effects of lead poisoning in a systemic perspective, we applied the transcriptogram methodology in an RNA-seq dataset of human embryonic-derived neural progenitor cells (ES-NP cells) treated with 30 µM lead acetate for 26 days. We observed early downregulation of several cellular systems involved with cell differentiation, such as cytoskeleton organization, RNA, and protein biosynthesis. The downregulated cellular systems presented big and tightly connected networks. For long treatment times (12 to 26 days), it was possible to observe a massive impairment in cell transcription profile. Taking the enriched terms together, we observed interference in all layers of gene expression regulation, from chromatin remodeling to vesicle transport. Considering that ES-NP cells are progenitor cells that can originate other neural cell types, our results suggest that lead-induced gene expression disturbance might impair cells' ability to differentiate, therefore influencing ES-NP cells' fate.

Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein-protein interaction.

MOTIVATION: Several freely available tools perform analysis using algorithms developed to identify significant variation of gene expression individually. The transcriptogramer R package uses protein-protein interaction to perform differential expression of functionally associated genes. The software assesses expression profile of entire genetic systems and reveals which biological systems are significantly altered in case-control designed transcriptome experiments. RESULTS: R/Bioconductor transcriptogramer package projects expression values on an ordered gene list to perform topological analysis, differential expression and gene ontology enrichment analysis, independently of data platform or operating system. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/transcriptogramer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of long noncoding RNA functions with co-expression network in esophageal squamous cell carcinoma.

BACKGROUND: Long non-coding RNAs (lncRNAs) are pervasively transcribed in the genome. They have important regulatory functions in chromatin remodeling and gene expression. Dysregulated lncRNAs have been studied in cancers, but their role in esophageal squamous cell carcinoma (ESCC) remains largely unknown. We have conducted lncRNA expression screening and a genome-wide analysis of lncRNA and coding gene expression on primary tumor and adjacent normal tissue from four ESCC patients, tend to understand the functionality of lncRNAs in carcinogenesis of esopheagus in combination with experimental and bioinformatics approach. METHODS: LncRNA array was used for coding and non-coding RNA expression. R program and Bioconductor packages (limma and RedeR) were used for differential expression and co-expression network analysis, followed by independent confirmation and functional studies of inferred onco-lncRNA ESCCAL-1 using quantitative real time polymerase chain reaction, small interfering RNA-mediated knockdown, apoptosis and invasion assays in vitro. RESULTS: The global coding and lncRNA gene expression pattern is able to distinguish ESCC from adjacent normal tissue. The co-expression network from differentially expressed coding and lncRNA genes in ESCC was constructed, and the lncRNA function may be inferred from the co-expression network. LncRNA ESCCAL-1 is such an example as a predicted novel onco-lncRNA, and it is overexpressed in 65% of an independent ESCC patient cohort (n = 26). More over, knockdown of ESCCAL-1 expression increases esophageal cancer cell apoptosis and reduces the invasion in vitro. CONCLUSION: Our study uncovered the landscape of ESCC-associated lncRNAs. The systematic analysis of coding and lncRNAs co-expression network increases our understanding of lncRNAs in biological network. ESCCAL-1 is a novel putative onco-lncRNA in esophageal cancer development.

Preferential duplication of intermodular hub genes: an evolutionary signature in eukaryotes genome networks.

Whole genome protein-protein association networks are not random and their topological properties stem from genome evolution mechanisms. In fact, more connected, but less clustered proteins are related to genes that, in general, present more paralogs as compared to other genes, indicating frequent previous gene duplication episodes. On the other hand, genes related to conserved biological functions present few or no paralogs and yield proteins that are highly connected and clustered. These general network characteristics must have an evolutionary explanation. Considering data from STRING database, we present here experimental evidence that, more than not being scale free, protein degree distributions of organisms present an increased probability for high degree nodes. Furthermore, based on this experimental evidence, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated with a probability that linearly grows with gene degree and decreases with its clustering coefficient. For the first time a model yields results that simultaneously describe different topological distributions. Also, this model correctly predicts that, to produce protein-protein association networks with number of links and number of nodes in the observed range for Eukaryotes, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. This scenario implies a universal mechanism for genome evolution.

Evolutionary plasticity determination by orthologous groups distribution.

BACKGROUND: Genetic plasticity may be understood as the ability of a functional gene network to tolerate alterations in its components or structure. Usually, the studies involving gene modifications in the course of the evolution are concerned to nucleotide sequence alterations in closely related species. However, the analysis of large scale data about the distribution of gene families in non-exclusively closely related species can provide insights on how plastic or how conserved a given gene family is. Here, we analyze the abundance and diversity of all Eukaryotic Clusters of Orthologous Groups (KOG) present in STRING database, resulting in a total of 4,850 KOGs. This dataset comprises 481,421 proteins distributed among 55 eukaryotes. RESULTS: We propose an index to evaluate the evolutionary plasticity and conservation of an orthologous group based on its abundance and diversity across eukaryotes. To further KOG plasticity analysis, we estimate the evolutionary distance average among all proteins which take part in the same orthologous group. As a result, we found a strong correlation between the evolutionary distance average and the proposed evolutionary plasticity index. Additionally, we found low evolutionary plasticity in Saccharomyces cerevisiae genes associated with inviability and Mus musculus genes associated with early lethality. At last, we plot the evolutionary plasticity value in different gene networks from yeast and humans. As a result, it was possible to discriminate among higher and lower plastic areas of the gene networks analyzed. CONCLUSIONS: The distribution of gene families brings valuable information on evolutionary plasticity which might be related with genetic plasticity. Accordingly, it is possible to discriminate among conserved and plastic orthologous groups by evaluating their abundance and diversity across eukaryotes.

Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case.

Analysis of genome-wide expression data poses a challenge to extract relevant information. The usual approaches compare cellular expression levels relative to a pre-established control and genes are clustered based on the correlation of their expression levels. This implies that cluster definitions are dependent on the cellular metabolic state, eventually varying from one experiment to another. We present here a computational method that order genes on a line and clusters genes by the probability that their products interact. Protein-protein association information can be obtained from large data bases as STRING. The genome organization obtained this way is independent from specific experiments, and defines functional modules that are associated with gene ontology terms. The starting point is a gene list and a matrix specifying interactions. Considering the Saccharomyces cerevisiae genome, we projected on the ordering gene expression data, producing plots of transcription levels for two different experiments, whose data are available at Gene Expression Omnibus database. These plots discriminate metabolic cellular states, point to additional conclusions, and may be regarded as the first versions of 'transcriptograms'. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms.

NFκB inhibitors induce cell death in glioblastomas.

Identification of novel target pathways in glioblastoma (GBM) remains critical due to poor prognosis, inefficient therapies and recurrence associated with these tumors. In this work, we evaluated the role of nuclear-factor-kappa-B (NFκB) in the growth of GBM cells, and the potential of NFκB inhibitors as antiglioma agents. NFκB pathway was found overstimulated in GBM cell lines and in tumor specimens compared to normal astrocytes and healthy brain tissues, respectively. Treatment of a panel of established GBM cell lines (U138MG, U87, U373 and C6) with pharmacological NFκB inhibitors (BAY117082, parthenolide, MG132, curcumin and arsenic trioxide) and NFκB-p65 siRNA markedly decreased the viability of GBMs as compared to inhibitors of other signaling pathways such as MAPKs (ERK, JNK and p38), PKC, EGFR and PI3K/Akt. In addition, NFκB inhibitors presented a low toxicity to normal astrocytes, indicating selectivity to cancerous cells. In GBMs, mitochondrial dysfunction (membrane depolarization, bcl-xL downregulation and cytochrome c release) and arrest in the G2/M phase were observed at the early steps of NFκB inhibitors treatment. These events preceded sub-G1 detection, apoptotic body formation and caspase-3 activation. Also, NFκB was found overstimulated in cisplatin-resistant C6 cells, and treatment of GBMs with NFκB inhibitors overcame cisplatin resistance besides potentiating the effects of the chemotherapeutics, cisplatin and doxorubicin. These findings support NFκB as a potential target to cell death induction in GBMs, and that the NFκB inhibitors may be considered for in vivo testing on animal models and possibly on GBM therapy.

ViaComplex: software for landscape analysis of gene expression networks in genomic context.

UNLABELLED: ViaComplex is an open-source application that builds landscape maps of gene expression networks. The motivation for this software comes from two previous publications (Nucleic Acids Res., 35, 1859-1867, 2007; Nucleic Acids Res., 36, 6269-6283, 2008). The first article presents a network-based model of genome stability pathways where we defined a set of genes that characterizes each genetic system. In the second article we analyzed this model by projecting functional information from several experiments onto the gene network topology. In order to systematize the methods developed in these articles, ViaComplex provides tools that may help potential users to assess different high-throughput experiments in the context of six core genome maintenance mechanisms. This model illustrates how different gene networks can be analyzed by the same algorithm. AVAILABILITY: (http://lief.if.ufrgs.br/pub/biosoftwares/viacomplex).

A systematic review of human antioxidant genes.

The balance between production and clearance of reactive species is essential for cell survival. Antioxidant cellular systems evolved to maintain a redox homeostasis under different physiological and pathological conditions. Therefore, many authors aim at better understanding the mechanisms and functions of cellular antioxidant components and their relationships between each other and with more general cell functions. Nonetheless, the definition of an "antioxidant system" is a wide and sometimes relative concept, and there is no consensus regarding the necessary requisites for classifying a cell functional component into such category. Here, we suggest a list of human antioxidant genes comprehending all gene products fulfilling specific inclusion criteria, such as antioxidant enzymatic function, participation in redox reactions and other molecular interactions directly related to antioxidant activity. The criteria are discussed and the gene-protein-substrate associations between the components of the list are presented. In addition, at http://www.ufrgs.br/icbs/hag we provide a network-based model of human antioxidant genes, which can be used as reference tool to access several database resources (e.g., RefSeq, Ensembl, HGNC and the NCBI Entrez database).

Evolutionary origins of human apoptosis and genome-stability gene networks.

Apoptosis is essential for complex multicellular organisms and its failure is associated with genome instability and cancer. Interactions between apoptosis and genome-maintenance mechanisms have been extensively documented and include transactivation-independent and -dependent functions, in which the tumor-suppressor protein p53 works as a 'molecular node' in the DNA-damage response. Although apoptosis and genome stability have been identified as ancient pathways in eukaryote phylogeny, the biological evolution underlying the emergence of an integrated system remains largely unknown. Here, using computational methods, we reconstruct the evolutionary scenario that linked apoptosis with genome stability pathways in a functional human gene/protein association network. We found that the entanglement of DNA repair, chromosome stability and apoptosis gene networks appears with the caspase gene family and the antiapoptotic gene BCL2. Also, several critical nodes that entangle apoptosis and genome stability are cancer genes (e.g. ATM, BRCA1, BRCA2, MLH1, MSH2, MSH6 and TP53), although their orthologs have arisen in different points of evolution. Our results demonstrate how genome stability and apoptosis were co-opted during evolution recruiting genes that merge both systems. We also provide several examples to exploit this evolutionary platform, where we have judiciously extended information on gene essentiality inferred from model organisms to human.

Retinoic acid induces apoptosis by a non-classical mechanism of ERK1/2 activation.

Even though RA is involved in differentiation and apoptosis of normal and cancer cells, being sometimes used as adjuvant in chemotherapy, its mechanisms of action involve multiple overlapping pathways that still remain unclear. Recent studies point out that RA exerts rapid and non-genomic effects, which are independent of RAR/RXR-mediated gene transcription. In this work, we reported that RA treatment for 24 h decreases cell viability, induces apoptosis dependent on caspase-3 activation, and activates the transcription factor AP-1 in cultured Sertoli cells. Moreover, RA induced a rapid and non-classical stimulation of ERK1/2. ERK1/2 activation was mediated by MEK1/2, and the protein synthesis inhibitor cycloheximide did not alter the pattern of RA-induced ERK1/2 phosphorylation. Pharmacological inhibition of MEK1/2-ERK1/2 pathway with UO126 blocked caspase-3 activation, decreased AP-1 binding to DNA and inhibited apoptosis. Overall, our data suggest that a rapid and non-genomic effect of RA upon MEK1/2-ERK1/2 pathway leads to caspase-3 activation and caspase-3-dependent apoptosis in cultured Sertoli cells. The non-canonical RA signaling presented in this work evokes new perspectives of RA action, which may play an important role in mediating early biological effects of RA modulating cell death in normal and tumor cells.

Retinol and retinoic acid increase MMP-2 activity by different pathways in cultured Sertoli cells.

Diseases such as atherosclerosis, arthritis and cancer have been related with imbalance in ROS production and failures in regulation of the MMPs. Authors suggested a relationship between MPP activity and ROS. Our research group has demonstrated that retinol 7 microM induced changes in Sertoli cell metabolism linking retinol treatment and oxidative stress. We verified MMP activity in Sertoli cells treated with vitamin A using gelatin zymography. We found that retinol (7 microM) and retinoic acid (1 nM) induced MMP-2 activity in Sertoli cells. Antioxidants reversed retinol-induced but not retinoic acid-induced MMP-2 activity. Moreover, retinol but not retinoic acid increased ROS production quantified by DCFH-DA oxidation. We found that retinol and retinoic acid induced ERK1/2 phosphorylation, but only retinol-increased MMP-2 activity was inhibited by UO126, an ERK1/2 phosphorylation inhibitor. Our findings suggested that retinol-induced MMP-2 activity, but not retinoic acid-induced MMP-2 activity, was related to ERK1/2 phosphorylation and ROS production.