Sex-specific gene expression differences in the prefrontal cortex of major depressive disorder individuals.

Major depressive disorder (MDD) is a leading global cause of disability, being more prevalent in females, possibly due to molecular and neuronal pathway differences between females and males. However, the connection between transcriptional changes and MDD remains unclear. We identified transcriptionally altered genes (TAGs) in MDD through gene and transcript expression analyses, focusing on sex-specific differences. Analyzing 263 brain samples from both sexes, we conducted differential gene expression, differential transcript expression, and differential transcript usage analyses, revealing 1169 unique TAGs, primarily in the prefrontal areas, with nearly half exhibiting transcript-level alterations. Females showed notable RNA splicing and export process disruptions in the orbitofrontal cortex, alongside altered DDX39B gene expression in five of the six brain regions in both sexes. Our findings suggest that disruptions in RNA processing pathways may play a vital role in MDD.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma.

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

What Powers Trastuzumab's Cardiotoxicity? Decoding Mitochondrial-Related Gene Expression Through Integrative Review and Meta-Analysis in Cardiomyocytes.

Trastuzumab is a monoclonal antibody used in oncotherapy for HER2-positive tumors. However, as an adverse effect, trastuzumab elevates the risk of heart failure, implying the involvement of energy production and mitochondrial processes. Past studies with transcriptome analysis have offered insights on pathways related to trastuzumab safety and toxicity but limited study sizes hinder conclusive findings. Therefore, we meta-analyzed mitochondria-related gene expression data in trastuzumab-treated cardiomyocytes. We searched the transcriptome databases for trastuzumab-treated cardiomyocytes in the ArrayExpress, DDBJ Omics Archive, Gene Expression Omnibus, Google Scholar, PubMed, and Web of Science repositories. A subset of 1270 genes related to mitochondrial functions (biogenesis, organization, mitophagy, and autophagy) was selected from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology Resource databases to conduct the present meta-analysis using the Metagen package (Study register at PROSPERO: CRD42021270645). Three datasets met the inclusion criteria and 1243 genes were meta-analyzed. We observed 69 upregulated genes after trastuzumab treatment which were related mainly to autophagy (28 genes) and mitochondrial organization (28 genes). We also found 37 downregulated genes which were related mainly to mitochondrial biogenesis (11 genes) and mitochondrial organization (24 genes). The present meta-analysis indicates that trastuzumab therapy causes an unbalance in mitochondrial functions, which could, in part, help explain the development of heart failure and yields a list of potential molecular targets. These findings contribute to our understanding of the molecular mechanisms underlying the cardiotoxic effects of trastuzumab and may have implications for the development of targeted therapies to mitigate such effects.

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.

Bridging the Gaps in Meta-Omic Analysis: Workflows and Reproducibility.

The past few years have seen significant advances in the study of complex microbial communities associated with the evolution of sequencing technologies and increasing adoption of whole genome shotgun sequencing methods over the once more traditional Amplicon-based methods. Although these advances have broadened the horizon of meta-omic analyses in planetary health, human health, and ecology from simple sample composition studies to comprehensive taxonomic and metabolic profiles, there are still significant challenges in processing these data. First, there is a widespread lack of standardization in data processing, including software choices and the ease of installing and running attendant software. This can lead to several inconsistencies, making comparing results across studies and reproducing original results difficult. We argue that these drawbacks are especially evident in metatranscriptomic analysis, with most analyses relying on ad hoc scripts instead of pipelines implemented in workflow managers. Additional challenges rely on integrating meta-omic data, since methods have to consider the biases in the library preparation and sequencing methods and the technical noise that can arise from it. Here, we critically discuss the current limitations in metagenomics and metatranscriptomics methods with a view to catalyze future innovations in the field of Planetary Health, ecology, and allied fields of life sciences. We highlight possible solutions for these constraints to bring about more standardization, with ease of installation, high performance, and reproducibility as guiding principles.

Revealing metastatic castration-resistant prostate cancer master regulator through lncRNAs-centered regulatory network.

BACKGROUND: Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive form of cancer unresponsive to androgen deprivation therapy (ADT) that spreads quickly to other organs. Despite reduced androgen levels after ADT, mCRPC development and lethality continues to be conducted by the androgen receptor (AR) axis. The maintenance of AR signaling in mCRPC is a result of AR alterations, androgen intratumoral production, and the action of regulatory elements, such as noncoding RNAs (ncRNAs). ncRNAs are key elements in cancer signaling, acting in tumor growth, metabolic reprogramming, and tumor progression. In prostate cancer (PCa), the ncRNAs have been reported to be associated with AR expression, PCa proliferation, and castration resistance. In this study, we aimed to reconstruct the lncRNA-centered regulatory network of mCRPC and identify the lncRNAs which act as master regulators (MRs). METHODS: We used publicly available RNA-sequencing to infer the regulatory network of lncRNAs in mCRPC. Five gene signatures were employed to conduct the master regulator analysis. Inferred MRs were then subjected to functional enrichment and symbolic regression modeling. The latter approach was applied to identify the lncRNAs with greater predictive capacity and potential as a biomarker in mCRPC. RESULTS: We identified 31 lncRNAs involved in cellular proliferation, tumor metabolism, and invasion-metastasis cascade. SNHG18 and HELLPAR were the highlights of our results. SNHG18 was downregulated in mCRPC and enriched to metastasis signatures. It accurately distinguished both mCRPC and primary CRPC from normal tissue and was associated with epithelial-mesenchymal transition (EMT) and cell-matrix adhesion pathways. HELLPAR consistently distinguished mCRPC from primary CRPC and normal tissue using only its expression. CONCLUSION: Our results contribute to understanding the regulatory behavior of lncRNAs in mCRPC and indicate SNHG18 and HELLPAR as master regulators and potential new diagnostic targets in this tumor.

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.

The evolution of knowledge on genes associated with human diseases.

Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

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.

Reverse Engineering of the Pediatric Sepsis Regulatory Network and Identification of Master Regulators.

Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.

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.

Reverse Engineering of Ewing Sarcoma Regulatory Network Uncovers PAX7 and RUNX3 as Master Regulators Associated with Good Prognosis.

Ewing Sarcoma (ES) is a rare malignant tumor occurring most frequently in adolescents and young adults. The ES hallmark is a chromosomal translocation between the chromosomes 11 and 22 that results in an aberrant transcription factor (TF) through the fusion of genes from the FET and ETS families, commonly EWSR1 and FLI1. The regulatory mechanisms behind the ES transcriptional alterations remain poorly understood. Here, we reconstruct the ES regulatory network using public available transcriptional data. Seven TFs were identified as potential MRs and clustered into two groups: one composed by PAX7 and RUNX3, and another composed by ARNT2, CREB3L1, GLI3, MEF2C, and PBX3. The MRs within each cluster act as reciprocal agonists regarding the regulation of shared genes, regulon activity, and implications in clinical outcome, while the clusters counteract each other. The regulons of all the seven MRs were differentially methylated. PAX7 and RUNX3 regulon activity were associated with good prognosis while ARNT2, CREB3L1, GLI3, and PBX3 were associated with bad prognosis. PAX7 and RUNX3 appear as highly expressed in ES biopsies and ES cell lines. This work contributes to the understanding of the ES regulome, identifying candidate MRs, analyzing their methilome and pointing to potential prognostic factors.

Ionotropic Receptors as a Driving Force behind Human Synapse Establishment.

The origin of nervous systems is a main theme in biology and its mechanisms are largely underlied by synaptic neurotransmission. One problem to explain synapse establishment is that synaptic orthologs are present in multiple aneural organisms. We questioned how the interactions among these elements evolved and to what extent it relates to our understanding of the nervous systems complexity. We identified the human neurotransmission gene network based on genes present in GABAergic, glutamatergic, serotonergic, dopaminergic, and cholinergic systems. The network comprises 321 human genes, 83 of which act exclusively in the nervous system. We reconstructed the evolutionary scenario of synapse emergence by looking for synaptic orthologs in 476 eukaryotes. The Human-Cnidaria common ancestor displayed a massive emergence of neuroexclusive genes, mainly ionotropic receptors, which might have been crucial to the evolution of synapses. Very few synaptic genes had their origin after the Human-Cnidaria common ancestor. We also identified a higher abundance of synaptic proteins in vertebrates, which suggests an increase in the synaptic network complexity of those organisms.

Anti-inflammatory and antixidant properties of blend formulated with compounds of Malpighia emarginata D.C (acerola) and Camellia sinensis L. (green tea) in lipopolysaccharide-stimulated RAW 264.7 macrophages.

The antioxidant and anti-inflammatory properties of Malpighia emarginata D.C (acerola) and Camellia sinensis L. (green tea) have been studied, particularly as an alternative in medicinal approach for different physio pathological conditions. Here we develop an powder blend formulated with both Malpighia emarginata D.C and Camellia sinensis L. which have in the composition higher content of ascorbic acid and epigallatocathechin-3-gallate respectively. Using different conditions for microencapsulation of biocompounds, we performed the powder production through spray-drying process. After, we evaluate the antioxidant and anti-inflammatory properties of blends formulated with Malpighia emarginata D.C and Camellia sinensis L. in an in vitro model of inflammation, using LPS-stimulated RAW-264.7 macrophage cell line. We observed that co-treatment with blends was able to modulate the redox parameters in cells during the in vitro inflammatory response. Moreover, the co-treatment with blends were able to modulate inflammatory response by altering the secretion of cytokines IL-1ß, IL-6, IL-10, and TNF-α. Taken together, our results demonstrate for the first time the synergistic effects antioxidant and anti-inflammatory of Malpighia emarginata D.C and Camellia sinensis L. These results warrant further use of the blend powder for use in the products to heath beneficial, principally in terms of prevention of chronic diseases.

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.

Analysis of Arabidopsis thaliana Redox Gene Network Indicates Evolutionary Expansion of Class III Peroxidase in Plants.

Reactive oxygen species (ROS) are byproducts of aerobic metabolism and may cause oxidative damage to biomolecules. Plants have a complex redox system, involving enzymatic and non-enzymatic compounds. The evolutionary origin of enzymatic antioxidant defense in plants is yet unclear. Here, we describe the redox gene network for A. thaliana and investigate the evolutionary origin of this network. We gathered from public repositories 246 A. thaliana genes directly involved with ROS metabolism and proposed an A. thaliana redox gene network. Using orthology information of 238 Eukaryotes from STRINGdb, we inferred the evolutionary root of each gene to reconstruct the evolutionary history of A. thaliana antioxidant gene network. We found two interconnected clusters: one formed by SOD-related, Thiol-redox, peroxidases, and other oxido-reductase; and the other formed entirely by class III peroxidases. Each cluster emerged in different periods of evolution: the cluster formed by SOD-related, Thiol-redox, peroxidases, and other oxido-reductase emerged before opisthokonta-plant divergence; the cluster composed by class III peroxidases emerged after opisthokonta-plant divergence and therefore contained the most recent network components. According to our results, class III peroxidases are in expansion throughout plant evolution, with new orthologs emerging in each evaluated plant clade divergence.

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.