PARP1 in the intersection of different DNA repair pathways, memory formation, and sleep pressure in neurons.

Poly(ADP-ribose) polymerase-1 (PARP1) is a bottleneck that connects different DNA pathways during a DNA damage response. Interestingly, PARP1 has a dualist role in neurons, acting as a neuroprotector and inducer of cell death in distinct neurological diseases. Recent studies significantly expanded our knowledge of how PARP1 regulates repair pathways in neurons and uncovered new roles for PARP1 in promoting sleep to enhance DNA repair. Likewise, PARP1 is deeply associated with memory consolidation, implying that it has multiple layers of regulation in the neural tissue. In this review, we critically discuss PARP1 recent advances in neurons, focusing on its interplay with different DNA repair mechanisms, memory, and sleep. Provocative questions about how oxidative damage is accessed, and different hypotheses about the molecular mechanisms influenced by PARP1 in neurons are presented to expand the debate of future studies.

NIAS-Server 2.0: A versatile complementary tool for structural biology studies.

Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

Gene Expression Variation Analysis (GEVA): A new R package to evaluate variations in differential expression in multiple biological conditions.

Nowadays, there are thousands of publicly available gene expression datasets which can be analyzed in silico using specialized software or the R programming language. However, transcriptomic studies consider experimental conditions individually, giving one independent result per comparison. Here we describe the Gene Expression Variation Analysis (GEVA), a new R package that accepts multiple differential expression analysis results as input and performs multiple statistical steps, such as weighted summarization, quantiles partition, and clustering to find genes whose differential expression varied less across all experiments. The experimental conditions can be divided into groups, which we call factors, where additional ANOVA (Fisher's and Levene's) tests are applied to identify differentially expressed genes in response either specifically to one factor or dependently to all factors. The final results present three possible classifications for relevant genes: similar, factor-dependent, and factor-specific. To validate these results subsequently to the GEVA's development, 28 transcriptomic datasets were tested using 11 different combinations of the available parameters, including several clustering, quantiles, and summarization methods. The final classifications were validated using knockout studies from different organisms, as they lack genes whose differential expression is expected. Although some of the final classifications differed depending on the parameters' choice, the test results from the default parameters corroborated with the published experimental studies regarding the selected datasets. Thus, we conclude that GEVA can effectively find similarities between groups of biological conditions, and therefore could be a robust alternative for multiple comparison analyses.

Network-based analysis using chromosomal microdeletion syndromes as a model.

Microdeletion syndromes (MSs) are a heterogeneous group of genetic diseases that can virtually affect all functions and organs in humans. Although systems biology approaches integrating multiomics and database information into biological networks have expanded our knowledge of genetic disorders, cytogenomic network-based analysis has rarely been applied to study MSs. In this study, we analyzed data of 28 MSs, using network-based approaches, to investigate the associations between the critical chromosome regions and the respective underlying biological network systems. We identified MSs-associated proteins that were organized in a network of linked modules within the human interactome. Certain MSs formed highly interlinked self-contained disease modules. Furthermore, we observed disease modules involving proteins from other disease groups in the MSs interactome. Moreover, analysis of integrated data from 564 genes located in known chromosomal critical regions, including those contributing to topological parameters, shared pathways, and gene-disease associations, indicated that complex biological systems and cellular networks may underlie many genotype to phenotype associations in MSs. In conclusion, we used a network-based analysis to provide resources that may contribute to better understanding of the molecular pathways involved in MSs.

Modifying the catalytic preference of alpha-amylase toward n-alkanes for bioremediation purposes using in silico strategies.

Since the beginning of oil exploration, whole ecosystems have been affected by accidents and bad practices involving petroleum compounds. In this sense, bioremediation stands out as the cheapest and most eco-friendly alternatives to reverse the damage done in oil-impacted areas. However, more efforts must be made to engineer enzymes that could be used in the bioremediation process. Interestingly, a recent work described that α-amylase, one of the most evolutionary conserved enzymes, was able to promiscuously degrade n-alkanes, a class of molecules abundant in the petroleum admixture. Considering that α-amylase is expressed in almost all known organisms, and employed in numerous biotechnological processes, using it can be a great leap toward more efficient applications of enzyme or microorganism-consortia bioremediation approaches. In this work, we employed a strict computational approach to design new α-amylase mutants with potentially enhanced catalytic efficiency toward n-alkanes. Using in silico techniques, such as molecular docking, molecular dynamics, metadynamics, and residue-residue interaction networks, we generated mutants potentially more efficient for degrading n-alkanes, L183Y, and N314A. Our results indicate that the new mutants have an increased binding rate for tetradecane, the longest n-alkane previously tested, which can reside in the catalytic center for more extended periods. Additionally, molecular dynamics and network analysis showed that the new mutations have no negative impact on protein structure than the WT. Our results aid in solidifying this enzyme as one more tool in the petroleum bioremediation toolbox.

Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform.

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.

Transcriptomic analysis reveals that mTOR pathway can be modulated in macrophage cells by the presence of cryptococcal cells.

Cryptococcus neoformans and Cryptococcus gattii are the etiological agents of cryptococcosis, a high mortality disease. The development of such disease depends on the interaction of fungal cells with macrophages, in which they can reside and replicate. In order to dissect the molecular mechanisms by which cryptococcal cells modulate the activity of macrophages, a genome-scale comparative analysis of transcriptional changes in macrophages exposed to Cryptococcus spp. was conducted. Altered expression of nearly 40 genes was detected in macrophages exposed to cryptococcal cells. The major processes were associated with the mTOR pathway, whose associated genes exhibited decreased expression in macrophages incubated with cryptococcal cells. Phosphorylation of p70S6K and GSK-3ß was also decreased in macrophages incubated with fungal cells. In this way, Cryptococci presence could drive the modulation of mTOR pathway in macrophages possibly to increase the survival of the pathogen.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research.

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

Integrated analysis of the critical region 5p15.3-p15.2 associated with cri-du-chat syndrome.

Cri-du-chat syndrome (CdCs) is one of the most common contiguous gene syndromes, with an incidence of 1:15,000 to 1:50,000 live births. To better understand the etiology of CdCs at the molecular level, we investigated theprotein-protein interaction (PPI) network within the critical chromosomal region 5p15.3-p15.2 associated with CdCs using systemsbiology. Data were extracted from cytogenomic findings from patients with CdCs. Based on clinical findings, molecular characterization of chromosomal rearrangements, and systems biology data, we explored possible genotype-phenotype correlations involving biological processes connected with CdCs candidate genes. We identified biological processes involving genes previously found to be associated with CdCs, such as TERT, SLC6A3, and CTDNND2, as well as novel candidate proteins with potential contributions to CdCs phenotypes, including CCT5, TPPP, MED10, ADCY2, MTRR, CEP72, NDUFS6, and MRPL36. Although further functional analyses of these proteins are required, we identified candidate proteins for the development of new multi-target genetic editing tools to study CdCs. Further research may confirm those that are directly involved in the development of CdCs phenotypes and improve our understanding of CdCs-associated molecular mechanisms.

Horizontal Gene Transfer Building Prokaryote Genomes: Genes Related to Exchange Between Cell and Environment are Frequently Transferred.

Horizontal gene transfer (HGT) has a major impact on the evolution of prokaryotic genomes, as it allows genes evolved in different contexts to be combined in a single genome, greatly enhancing the ways evolving organisms can explore the gene content space and adapt to the environment. A systematic analysis of HGT in a large number of genomes is of key importance in understanding the impact of HGT in the evolution of prokaryotes. We developed a method for the detection of genes that potentially originated by HGT based on the comparison of BLAST scores between homologous genes to 16S rRNA-based phylogenetic distances between the involved organisms. The approach was applied to 697 prokaryote genomes and estimated that in average approximately 15% of the genes in prokaryote genomes originated by HGT, with a clear correlation between the proportion of predicted HGT genes and the size of the genome. The methodology was strongly supported by evolutionary relationships, as tested by the direct phylogenetic reconstruction of many of the HGT candidates. Studies performed with Escherichia coli W3110 genome clearly show that HGT proteins have fewer interactions when compared to those predicted as vertical inherited, an indication that the number of protein partners imposes limitations to horizontal transfer. A detailed functional classification confirms that genes related to protein translation are vertically inherited, whereas interestingly, transport and binding proteins are strongly enriched among HGT genes. Because these genes are related to the cell exchange with their environment, their transfer most likely contributed to successful adaptation throughout evolution.

The importance of sphingolipids and reactive oxygen species in cardiovascular development.

The heart is the first organ in the embryo to form. Its structural and functional complexity is the result of a thorough developmental program, where sphingolipids play an important role in cardiogenesis, heart maturation, angiogenesis, the regulation of vascular tone and vessel permeability. Sphingolipids are necessary for signal transduction and membrane microdomain formation. In addition, recent evidence suggests that sphingolipid metabolism is directly interconnected to the modulation of oxidative stress. However, cardiovascular development is highly sensitive to excessive reactive species production, and disturbances in sphingolipid metabolism can lead to abnormal development and cardiac disease. Therefore, in this review, we address the molecular link between sphingolipids and oxidative stress, connecting these pathways to cardiovascular development and cardiovascular disease.

Potential role of exitron-containing homeobox genes in cancer.

Homeobox genes are protagonists in developmental and cancer biology, making comprehending their regulation pivotal in multiple molecular pathways. Exitrons, also known as intronic exons, are new players in the transcriptional organization, providing additional splicing variants whose functions are still vastly unknown. Exitron splicing sites were identified in eight homeobox genes, which has not been yet debated in the scientific literature. Due to the intimate connection between homeobox genes and tumorigenesis, it is worth investing more time in understanding how these less explored exitron-containing transcriptional isoforms could play a role in modulating the homeobox gene's biological functions. The perspectives devised in this article are meant to instigate fresh debates on how the transcriptional variants retaining exitrons identified in the human homeobox genes HOXA1, HOXA9, HOXD8, NKX3.1, and DLX6 can be examined in the context of tumorigenesis. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics.

The nucleotide excision repair proteins through the lens of molecular dynamics simulations.

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

Revisiting the structural features of the xeroderma pigmentosum proteins: Focus on mutations and knowledge gaps.

The nucleotide excision repair pathway is a broadly studied DNA repair mechanism because impairments of its key players, the xeroderma pigmentosum proteins (XPA to XPG), are associated with multiple hereditary diseases. Due to the massive number of novel mutations reported for these proteins and new structural data published every year, proper categorization and discussion of relevant observations is needed to organize this extensive inflow of knowledge. This review aims to revisit the structural data of all XP proteins while updating it with the information developed in of the past six years. Discussions and interpretations of mutation outcomes, mechanisms of action, and knowledge gaps regarding their structures are provided, as well as new perspectives based on recent research.

Current state of knowledge of human DNA polymerase eta protein structure and disease-causing mutations.

POLη, encoded by the POLH gene, is a crucial protein for replicating damaged DNA and the most studied specialized translesion synthesis polymerases. Mutations in POLη are associated with cancer and the human syndrome xeroderma pigmentosum variant, which is characterized by extreme photosensitivity and an increased likelihood of developing skin cancers. The myriad of structural information about POLη is vast, covering dozens of different mutants, numerous crucial residues, domains, and posttranslational modifications that are essential for protein function within cells. Since POLη is key vital enzyme for cell survival, and mutations in this protein are related to aggressive diseases, understanding its structure is crucial for biomedical sciences, primarily due to its similarities with other Y-family polymerases and its potential as a targeted therapy-drug for tumors. This work provides an up-to-date review on structural aspects of the human POLη: from basic knowledge about critical residues and protein domains to its mutant variants, posttranslational modifications, and our current understanding of therapeutic molecules that target POLη. Thus, this review provides lessons about POLη's structure and gathers critical discussions and hypotheses that may contribute to understanding this protein's vital roles within the cells.

Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador.

The investigation of conventional complete blood-count (CBC) data for classifying the SARS-CoV-2 infection status became a topic of interest, particularly as a complementary laboratory tool in developing and third-world countries that financially struggled to test their population. Although hematological parameters in COVID-19-affected individuals from Asian and USA populations are available, there are no descriptions of comparative analyses of CBC findings between COVID-19 positive and negative cases from Latin American countries. In this sense, machine learning techniques have been employed to examine CBC data and aid in screening patients suspected of SARS-CoV-2 infection. In this work, we used machine learning to compare CBC data between two highly genetically distinguished Latin American countries: Brazil and Ecuador. We notice a clear distribution pattern of positive and negative cases between the two countries. Interestingly, almost all red blood cell count parameters were divergent. For males, neutrophils and lymphocytes are distinct between Brazil and Ecuador, while eosinophils are distinguished for females. Finally, neutrophils, lymphocytes, and monocytes displayed a particular distribution for both genders. Therefore, our findings demonstrate that the same set of CBC features relevant to one population is unlikely to apply to another. This is the first study to compare CBC data from two genetically distinct Latin American countries.

Genetic and molecular Omp25 analyses from worldwide Brucella canis strains: Possible mutational influences in protein function.

Brucella canis is responsible for canine brucellosis, a neglected zoonotic disease. The omp25 gene has been described as an important marker for Brucella intra-species differentiation, in addition to the ability to interact with the host immune system. Therefore, this study investigated the omp25 sequence from B. canis strains associated to a phylogenetic characterization and the unveiling of the molecular structure. In vitro analyses comprised DNA extraction, PCR, and sequencing of omp25 from 19 B. canis strains. Moreover, in silico analyses were performed at nucleotide level for phylogenetic characterization and evolutionary history of B. canis omp25 gene; and in amino acid level including modeling, dynamics, and epitope prediction of B. canis Omp25 protein. Here, we identified a new mutation, L109P, which diverges the worldwide omp25 sequences in two large branches. Interestingly, this mutation appears to have epidemiology importance, based on a geographical distribution of B. canis strains. Structural and molecular dynamics analyses of Omp25 revealed that Omp25L109P does not sustain its native ß-barrel. Likewise, the conformation of B-cell epitope on the mutated region was changed in Omp25L109P protein. Even without an evolutive marker, the new identified mutation appears to affect the basic function of B. canis Omp25 protein, which could indicate virulence adaptation for some B. canis strains in a context of geographical disposition.

The Drosophila melanogaster ACE2 ortholog genes are differently expressed in obesity/diabetes and aging models: Implications for COVID-19 pathology.

The Spike glycoprotein of SARS-CoV-2, the virus responsible for coronavirus disease 2019, binds to its ACE2 receptor for internalization in the host cells. Elderly individuals or those with subjacent disorders, such as obesity and diabetes, are more susceptible to COVID-19 severity. Additionally, several SARS-CoV-2 variants appear to enhance the Spike-ACE2 interaction, which increases transmissibility and death. Considering that the fruit fly is a robust animal model in metabolic research and has two ACE2 orthologs, Ance and Acer, in this work, we studied the effects of two hypercaloric diets (HFD and HSD) and aging on ACE2 orthologs mRNA expression levels in Drosophila melanogaster. To complement our work, we analyzed the predicted binding affinity between the Spike protein with Ance and Acer. We show for the first time that Ance and Acer genes are differentially regulated and dependent on diet and age in adult flies. At the molecular level, Ance and Acer proteins exhibit the potential to bind to the Spike protein in different regions, as shown by a molecular docking approach. Acer, in particular, interacts with the Spike protein in the same region as in humans. Overall, we suggest that the D. melanogaster is a promising animal model for translational studies on COVID-19 associated risk factors and ACE2.

Systems biology network reveals the correlation between COX-2 expression and Ch 7q copy number alterations in Ch 11q-deleted pediatric neuroblastoma tumors.

Tumor-associated inflammation and chromosomal aberrations can play crucial roles in cancer development and progression. In neuroblastoma (NB), the enzyme cyclooxygenase-2 (COX-2) is associated with copy number alterations on the long arm of chromosome 11 (Ch 11q), defining an aggressive disease subset. This retrospective study included formalin-fixed paraffin-embedded tumor samples collected from nine patients during diagnosis at the pediatric Pequeno Principe Hospital, Curitiba, PR, Brazil, and post-chemotherapy (CT). COX-2 expression was evaluated using immunohistochemistry and correlated with the genome profile of paired pre- and post-CT samples, determined by array comparative genomic hybridization. A systems biology approach elucidated the PTGS2 network interaction. The results showed positive correlations between pre-CT Ch 7q gain and COX-2 expression (ρ = 0.825; p-value = 0.006) and negative correlations between Ch 7q gain and Ch 11q deletion (ρ = -0.919; p-value = 0.0005). Three samples showed Ch 11q deletion and Ch 7q gain. Network analysis identified a direct connection between CAV-1 (Ch 7q) and COX-2 in NB tumors and highlighted the connection between amplified genes in Ch 7q and deleted ones in 11q. The identification of hub-bottleneck-switch genes provides new biological insights into this connection between NB, tumorigenesis, and inflammation.

Melatonin as a central molecule connecting neural development and calcium signaling.

Melatonin (MEL) is a neuroendocrine hormone secreted by the pineal gland in association with the suprachiasmatic nucleus and peripheral tissues. MEL has been observed to play a critical role in the reproductive process and in the fetomaternal interface. Extrapineal synthesis has been reported in mammalian models during pregnancy, especially by the placenta tissue. MEL can regulate intracellular processes (e.g., G-proteins) and the activity of second messengers (e.g., cAMP, IP(3,) Ca(2+)). During neurodevelopment, these activities regulated by melatonin have an important role as an intracellular signaling for gene expression regulation. To review the role of MEL in neurodevelopment, we built interactome networks of different proteins that act in these processes using systems biology tools. The analyses of interactome networks revealed that MEL could modulate neurodevelopment through the regulation of Ca(2+) intracellular levels and influencing BMP/SMAD signaling, thus affecting neural gene responses and neuronal differentiation.