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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

Every protagonist has a sidekick: Structural aspects of human xeroderma pigmentosum-binding proteins in nucleotide excision repair.

The seven xeroderma pigmentosum proteins (XPps), XPA-XPG, coordinate the nucleotide excision repair (NER) pathway, promoting the excision of DNA lesions caused by exposition to ionizing radiation, majorly from ultraviolet light. Significant efforts are made to investigate NER since mutations in any of the seven XPps may cause the xeroderma pigmentosum and trichothiodystrophy diseases. However, these proteins collaborate with other pivotal players in all known NER steps to accurately exert their purposes. Therefore, in the old and ever-evolving field of DNA repair, it is imperative to reexamine and describe their structures to understand NER properly. This work provides an up-to-date review of the protein structural aspects of the closest partners that directly interact and influence XPps: RAD23B, CETN2, DDB1, RPA (RPA70, 32, and 14), p8 (GTF2H5), and ERCC1. Structurally and functionally vital domains, regions, and critical residues are reexamined, providing structural lessons and perspectives about these indispensable proteins in the NER and other DNA repair pathways. By gathering all data related to the major human xeroderma pigmentosum-interacting proteins, this review will aid newcomers on the subject and guide structural and functional future studies.

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.

Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a Curated RNA-Seq Database for Cancer Research.

RNA-seq is gradually becoming the dominating technique employed to access the global gene expression in biological samples, allowing more flexible protocols and robust analysis. However, the nature of RNA-seq results imposes new data-handling challenges when it comes to computational analysis. With the increasing employment of machine learning (ML) techniques in biomedical sciences, databases that could provide curated data sets treated with state-of-the-art approaches already adapted to ML protocols, become essential for testing new algorithms. In this study, we present the Benchmarking of ARtificial intelligence Research: Curated RNA-seq Database (BARRA:CuRDa). BARRA:CuRDa was built exclusively for cancer research and is composed of 17 handpicked RNA-seq data sets for Homo sapiens that were gathered from the Gene Expression Omnibus, using rigorous filtering criteria. All data sets were individually submitted to sample quality analysis, removal of low-quality bases and artifacts from the experimental process, removal of ribosomal RNA, and estimation of transcript-level abundance. Moreover, all data sets were tested using standard approaches in the field, which allows them to be used as benchmark to new ML approaches. A feature selection analysis was also performed on each data set to investigate the biological accuracy of basic techniques. Results include genes already related to their specific tumoral tissue a large amount of long noncoding RNA and pseudogenes. BARRA:CuRDa is available at http://sbcb.inf.ufrgs.br/barracurda.

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.

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.

Network Analysis Reveals Proteins Associated with Aortic Dilatation in Mucopolysaccharidoses.

Mucopolysaccharidoses are caused by a deficiency of enzymes involved in the degradation of glycosaminoglycans. Heart diseases are a significant cause of morbidity and mortality in MPS patients, even in conditions in which enzyme replacement therapy is available. In this sense, cardiovascular manifestations, such as heart hypertrophy, cardiac function reduction, increased left ventricular chamber, and aortic dilatation, are among the most frequent. However, the downstream events which influence the heart dilatation process are unclear. Here, we employed systems biology tools together with transcriptomic data to investigate new elements that may be involved in aortic dilatation in Mucopolysaccharidoses syndrome. We identified candidate genes involved in biological processes related to inflammatory responses, deposition of collagen, and lipid accumulation in the cardiovascular system that may be involved in aortic dilatation in the Mucopolysaccharidoses I and VII. Furthermore, we investigated the molecular mechanisms of losartan treatment in Mucopolysaccharidoses I mice to underscore how this drug acts to prevent aortic dilation. Our data indicate that the association between the TGF-b signaling pathway, Fos, and Col1a1 proteins can play an essential role in aortic dilation's pathophysiology and its subsequent improvement by losartan treatment.