Search

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

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.

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.

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.

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.

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 Omp25

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.

The seven xeroderma pigmentosum proteins, XPA to XPG, coordinate the Nucleotide Excision Repair (NER) pathway, promoting the excision of DNA lesions caused by exposition to ionizing radiation, majorly from UV-light. Significant efforts are made to investigate NER since mutations in any of the seven XP proteins 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 closest partners that directly interact and influence xeroderma pigmentosum proteins: 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. This article is protected by copyright. All rights reserved.

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.

The COX-2 protein, encoded by the PTGS2 gene, is related to tumor progression in adult and pediatric cancer. In neuroblastoma (NB), COX-2 was associated with loss of heterozygosity on the long arm of chromosome 11 (Ch11q loss of heterozygosity, LOH), defining a subset of aggressive disease. The present study aimed to investigate the protein expression of COX-2 in a set of 82 pre-chemotherapy (CT) and 20 post-CT NB specimens and its correlation with clinical and genomic data. A systems biology approach elucidated the network interaction of PTGS2 and other inflammation-related genes with those codified in the Ch11q deleted regions. The results indicated a significantly higher expression of COX-2 in post-CT samples. In addition, a significant positive correlation between the presence of aberrations in Ch11q and COX-2 levels and an indirect connection between the COX-2 gene and extracellular matrix remodeling (ECM)-related proteins were observed. Our findings suggest that deregulation of ECM proteolysis in Ch11q–deleted NB could elicit stromal alterations, triggering inflammatory responses via COX-2 overexpression, ultimately supporting NB progression.

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.

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.

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.

The lysosome achieved a new protagonism that highlights its multiple cellular functions, such as in the catabolism of complex substrates, nutrient sensing, and signaling pathways implicated in cell metabolism and growth. Lysosomal storage diseases (LSDs) cause lysosomal accumulation of substrates and deficiency in trafficking of macromolecules. The substrate accumulation can impact one or several pathways which contribute to cell damage. Autophagy impairment and immune response are widely studied, but less attention is paid to morphogenic and growth pathways and its impact on the pathophysiology of LSDs. Hedgehog pathway is affected with abnormal expression and changes in distribution of protein levels, and a reduced number and length of primary cilia. Moreover, growth pathways are identified with delay in reactivation of mTOR that deregulate termination of autophagy and reformation of lysosomes. Insulin resistance caused by changes in lipids rafts has been described in different LSDs. While the genetic and biochemical bases of deficient proteins in LSDs are well understood, the secondary molecular mechanisms that disrupt wider biological processes associated with LSDs are only now becoming clearer. Therefore, we explored how specific signaling pathways can be related to specific LSDs, showing that a system medicine approach could be a valuable tool for the better understanding of LSD pathogenesis. This article is categorized under: Metabolic Diseases > Molecular and Cellular Physiology.

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-3β 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.

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.

In this report, we present a patient with brain alterations and dysmorphic features associated with chromosome duplication seen in 4p16.3 region and chromosomal deletion in a critical region responsible for Cri-du-chat syndrome (CdCS). Chromosomal microarray analysis (CMA) revealed a 41.1 Mb duplication encompassing the band region 4p16.3–p13, and a 14.7 Mb deletion located between the bands 5p15.33 and p15.1. The patient’s clinical findings overlap with previously reported cases of chromosome 4p duplication syndrome and CdCS. The patient’s symptoms are notably similar to those of CdCS patients as she presented with a weak, high-pitched voice and showed a similar pathogenicity observed in the brain MRI. These contiguous gene syndromes present with distinct clinical manifestations. However, the phenotypic and cytogenetic variability in affected individuals, such as the low frequency and the large genomic regions that can be altered, make it challenging to identify candidate genes that contribute to the pathogenesis of these syndromes. Therefore, systems biology and CMA techniques were used to investigate the extent of chromosome rearrangement on critical regions in our patient’s phenotype. We identified the candidate genes PPARGC1A, CTBP1, TRIO, TERT, and CCT5 that are associated with the neuropsychomotor delay, microcephaly, and neurological alterations found in our patient. Through investigating pathways that associate with essential nodes in the protein interaction network, we discovered proteins involved in cellular differentiation and proliferation, as well as proteins involved in the formation and disposition of the cytoskeleton. The combination of our cytogenomic and bioinformatic analysis provided these possible explanations for the unique clinical phenotype, which has not yet been described in scientific literature.

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.

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.

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

As the primary source of a wide range of industrial products, the study of petroleum-derived compounds is of pivotal importance. However, the process of oil extraction and refinement is among the most environmentally hazardous practices, impacting almost all levels of the ecological chain. So far, the most appropriate strategy to overcome such an issue is through bioremediation, which revolves around the employment of different microorganisms to degrade hazardous compounds, generating less environmental impact and lower monetary costs. In this sense, a myriad of organisms and enzymes are considered possible candidates for the bioremediation process. Amidst the potential candidates is α-amylase, an evolutionary conserved starch-degrading enzyme. Notably, α-amylase was not only seen to degrade n-alkanes, a subclass of alkanes considered the most abundant petroleum-derived compounds but also low-density polyethylene, a dangerous pollutant produced from petroleum. Thus, due to its high conservation in both eukaryotic and prokaryotic lineages, in addition to the capability to degrade different types of hazardous compounds, the study of α-amylase becomes a rising interest. Nevertheless, there are no studies that review all biotechnological applications of α-amylase for bioremediation. In this work, we critically review the potential biotechnological applications of α-amylase, focusing on the biodegradation of petroleum-derived compounds. Evolutionary aspects are discussed, as well for all structural information and all features that could impact on the employment of this protein in the biotechnological industry, such as pH, temperature, and medium conditions. New perspectives and critical assessments are conducted regarding the application of α-amylase in the bioremediation of n-alkanes.

Base and nucleotide excision repair (BER and NER) pathways are normally associated with removal of specific types of DNA damage: small base modifications (such as those induced by DNA oxidation) and bulky DNA lesions (such as those induced by ultraviolet or chemical carcinogens), respectively. However, growing evidence indicates that this scenario is much more complex and these pathways exchange proteins and cooperate with each other in the repair of specific lesions. In this review, we highlight studies discussing the involvement of NER in the repair of DNA damage induced by oxidative stress, and BER participating in the removal of bulky adducts on DNA. Adding to this complexity, UVA light experiments revealed that oxidative stress also causes protein oxidation, directly affecting proteins involved in both NER and BER. This reduces the cell’s ability to repair DNA damage with deleterious implications to the cells, such as mutagenesis and cell death, and to the organisms, such as cancer and aging. Finally, an interactome of NER and BER proteins is presented, showing the strong connection between these pathways, indicating that further investigation may reveal new functions shared by them, and their cooperation in maintaining genome stability.

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.

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.

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.

Fetal alcohol syndrome (FAS) is a prenatal disease characterized by fetal morphological and neurological abnormalities originating from exposure to alcohol. Although FAS is a well-described pathology, the molecular mechanisms underlying its progression are virtually unknown. Moreover, alcohol abuse can affect vitamin metabolism and absorption, although how alcohol impairs such biochemical pathways remains to be elucidated. We employed a variety of systems chemo-biology tools to understand the interplay between ethanol metabolism and vitamins during mouse neurodevelopment. For this purpose, we designed interactomes and employed transcriptomic data analysis approaches to study the neural tissue of Mus musculus exposed to ethanol prenatally and postnatally, simulating conditions that could lead to FAS development at different life stages. Our results showed that FAS can promote early changes in neurotransmitter release and glutamate equilibrium, as well as an abnormal calcium influx that can lead to neuroinflammation and impaired neurodifferentiation, both extensively connected with vitamin action and metabolism. Genes related to retinoic acid, niacin, vitamin D, and folate metabolism were underexpressed during neurodevelopment and appear to contribute to neuroinflammation progression and impaired synapsis. Our results also indicate that genes coding for tubulin, tubulin-associated proteins, synapse plasticity proteins, and proteins related to neurodifferentiation are extensively affected by ethanol exposure. Finally, we developed a molecular model of how ethanol can affect vitamin metabolism and impair neurodevelopment.

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.

The integrity of the DNA molecule is constantly threatened by a wide range of mutagenic agents such as pollutants, UV light, and consumed drugs. However, cells have established numerous mechanisms that act on identifying, removing, and reconstructing the DNA molecules after DNA damage. Failure or insufficiency in the regulation of such mechanisms can lead to various diseases, including cancer. Nevertheless, sometimes these repair mechanisms bear defects associated with mutations in one of the essential DNA-repair genes, generating the so-called genetic diseases. In this chapter, we review the repair machineries underlying rare genetic diseases and different cancer types. Cancer development derived from deregulations in epigenetic mechanisms, cell-cycle impairments, and chromosome instability are also discussed.

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.

One theory that attempts to explain how and why an organism ages is the developmental hypothesis of aging (DevAge), which describes how developmental programming leads to aging in adults. Interestingly, the developmental origins of health and disease hypothesis (DOHaD) asserts that some aging-associated diseases that occur in adults are closely related to development and to conditions in the intrauterine environment. Thus, both aging and aging-associated diseases can be viewed, at least in part, as the result of a developmental program that is activated early in embryogenesis and persists throughout the lifespan of the organism. We would expect this developmental program to be regulated by a set of interacting protein networks that connect environmental and molecular signals. However, the connection between aging and development is not clear. Thus, a systems biology approach that incorporates different "omic" databases for two mammalian models, Homo sapiens and Mus musculus, was used to evaluate how development and aging are interconnected. Interestingly, three major, evolutionarily conserved processes, namely the immune system, epigenetics, and aerobic metabolism, appear to regulate aging and development in both H. sapiens and M. musculus. Considering that these three processes are essential to embryogenesis, the protein networks within these processes are subjected to strong selective pressure to eliminate gross developmental abnormalities in early embryogenesis. This selective pressure becomes more relaxed in the adult organism, permitting the onset of aging-associated diseases and inflammation-related aging; this concept echoes the antagonistic pleiotropy hypothesis of aging.