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.

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.

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.

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.

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.

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.

Multi-Approach Bioinformatics Analysis of Curated Omics Data Provides a Gene Expression Panorama for Multiple Cancer Types.

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research.

The employment of machine learning (ML) approaches to extract gene expression information from microarray studies has increased in the past years, specially on cancer-related works. However, despite this continuous interest in applying ML in cancer biomedical research, there are no curated repositories focused only on providing quality data sets exclusively for benchmarking and testing of such techniques for cancer research. Thus, in this work, we present the Curated Microarray Database (CuMiDa), a database composed of 78 handpicked microarray data sets for Homo sapiens that were carefully examined from more than 30,000 microarray experiments from the Gene Expression Omnibus using a rigorous filtering criteria. All data sets were individually submitted to background correction, normalization, sample quality analysis and were manually edited to eliminate erroneous probes. All data sets were tested using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses to observe sample division and were additionally tested using various ML approaches to provide a base accuracy for the major techniques employed for microarray data sets. CuMiDa is a database created solely for benchmarking and testing of ML approaches applied to cancer research.

Architects meets Repairers: The interplay between homeobox genes and DNA repair.

Homeobox genes are widely considered the major protagonists of embryonic development and tissue formation. For the past decades, it was established that the deregulation of these genes is intimately related to developmental abnormalities and a broad range of diseases in adults. Since the proper regulation and expression of homeobox genes are necessary for a successful developmental program and tissue function, their relation to DNA repair mechanisms become a necessary discussion. However, important as it is, studies focused on the interplay between homeobox genes and DNA repair are scarce, and there is no critical discussion on the subject. Hence, in this work, I aim to provide the first review of the current knowledge of the interplay between homeobox genes and DNA repair mechanisms, and offer future perspectives on this, yet, young ground for new researches. Critical discussion is conducted, together with a careful assessment of each reviewed topic.

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.

Dynamics of DDB2-DDB1 complex under different naturally-occurring mutants in Xeroderma Pigmentosum disease.

BACKGROUND: Xeroderma Pigmentosum (XP) is a disease caused by mutations in the nucleotide excision repair (NER) pathway. Patients with XP exhibit a high propensity to skin cancers and some subtypes of XP can even present neurological impairments. During NER, DDB2 (XPE), in complex with DDB1 (DDB-Complex), performs the DNA lesion recognition. However, not much is known about how mutations found in XP patients affect the DDB2 structure and complex assembly. Thus, we searched for structural evidence associated with the role of three naturally occurring mutations found in XPE patients: R273H, K244E, and L350P. METHODS: Each mutant was individually constructed and submitted to multiple molecular dynamics simulations, done in triplicate for each designed system. Additionally, Dynamic Residue Interaction Networks were designed for each system and analyzed parallel with the simulations. RESULTS: DDB2 mutations promoted loss of flexibility in the overall protein structure, producing a different conformational behavior in comparison to the WT, especially in the region comprising residues 354 to 371. Furthermore, the DDB-complex containing the mutated forms of DDB2 showed distinct behaviors for each mutant: R273H displayed higher structural instability when complexed; L350P affected DDB1 protein-protein binding with DDB2; and K244E, altered the complex binding trough different ways than L350P. CONCLUSIONS: The data gathered throughout the analyses helps to enlighten the structural basis for how naturally occurring mutations found in XPE patients impact on DDB2 and DDB1 function. GENERAL SIGNIFICANCE: Our data influence not only on the knowledge of XP but on the DNA repair mechanisms of NER itself.

Overview of xeroderma pigmentosum proteins architecture, mutations and post-translational modifications.

The xeroderma pigmentosum complementation group proteins (XPs), which include XPA through XPG, play a critical role in coordinating and promoting global genome and transcription-coupled nucleotide excision repair (GG-NER and TC-NER, respectively) pathways in eukaryotic cells. GG-NER and TC-NER are both required for the repair of bulky DNA lesions, such as those induced by UV radiation. Mutations in genes that encode XPs lead to the clinical condition xeroderma pigmentosum (XP). Although the roles of XPs in the GG-NER/TC-NER subpathways have been extensively studied, complete knowledge of their three-dimensional structure is only beginning to emerge. Hence, this review aims to summarize the current knowledge of mapped mutations and other structural information on XP proteins that influence their function and protein-protein interactions. We also review the possible post-translational modifications for each protein and the impact of these modifications on XP protein functions.

Toxicological effects of the different substances in tobacco smoke on human embryonic development by a systems chemo-biology approach.

The physiological and molecular effects of tobacco smoke in adult humans and the development of cancer have been well described. In contrast, how tobacco smoke affects embryonic development remains poorly understood. Morphological studies of the fetuses of smoking pregnant women have shown various physical deformities induced by constant fetal exposure to tobacco components, especially nicotine. In addition, nicotine exposure decreases fetal body weight and bone/cartilage growth in addition to decreasing cranial diameter and tibia length. Unfortunately, the molecular pathways leading to these morphological anomalies are not completely understood. In this study, we applied interactome data mining tools and small compound interaction networks to elucidate possible molecular pathways associated with the effects of tobacco smoke components during embryonic development in pregnant female smokers. Our analysis showed a relationship between nicotine and 50 additional harmful substances involved in a variety of biological process that can cause abnormal proliferation, impaired cell differentiation, and increased oxidative stress. We also describe how nicotine can negatively affect retinoic acid signaling and cell differentiation through inhibition of retinoic acid receptors. In addition, nicotine causes a stress reaction and/or a pro-inflammatory response that inhibits the agonistic action of retinoic acid. Moreover, we show that the effect of cigarette smoke on the developing fetus could represent systemic and aggressive impacts in the short term, causing malformations during certain stages of development. Our work provides the first approach describing how different tobacco constituents affect a broad range of biological process in human embryonic development.

Senescence; an endogenous anticancer mechanism.

Pre-malignant tumor cells enter a state of irreversible cell cycle arrest termed senescence (cellular senescence; CS). CS is a part of the aging program and involves multiple signaling cascades and transduction mechanisms. In general, senescence can be divided into replicative senescence and premature senescence. Replicative senescence (replicative CS) has been described for all metabolically active cells that undergo a spontaneous decline in growth rate. Notably, ectopic expression of telomerase holoenzyme (hTert) can prevent replicative CS. In cancer cells, premature senescence induced by oncogenes, named oncogene-induced senescence (oncogene induced CS; OIS), play an important role in preventing the development of cancer. Oncogene induced CS can be promoted by the loss of tumor suppressor genes, such as PTEN. Additionally, other interesting mechanisms, like selective microRNA expression, epigenetic modifications, or even stress conditions, are also able to activate the senescence program. Here, we will critically review the literature on the role of senescence in preventing the development of cancer and discuss the potential of senescence modulation for generating new molecular tools that could be explored as anticancer treatments.