Assessment of Kaistella jeonii esterase conformational dynamics in response to poly(ethylene terephthalate) binding.

The pervasive presence of plastic in the environment has reached a concerning scale, being identified in many ecosystems. Bioremediation is the cheapest and most eco-friendly alternative to remove this polymer from affected areas. Recent work described that a novel cold-active esterase enzyme extracted from the bacteria Kaistella jeonii could promiscuously degrade PET. Compared to the well-known PETase from Ideonella sakaiensis, this novel esterase presents a low sequence identity yet has a remarkably similar folding. However, enzymatic assays demonstrated a lower catalytic efficiency. In this work, we employed a strict computational approach to investigate the binding mechanism between the esterase and PET. Understanding the underlying mechanism of binding can shed light on the evolutive mechanism of how enzymes have been evolving to degrade these artificial molecules and help develop rational engineering approaches to improve PETase-like enzymes. Our results indicate that this esterase misses a disulfide bridge, keeping the catalytic residues closer and possibly influencing its catalytic efficiency. Moreover, we describe the structural response to the interaction between enzyme and PET, indicating local and global effects. Our results aid in deepening the knowledge behind the mechanism of biological catalysis of PET degradation and as a base for the engineering of novel PETases.

The MAPSTROKE project: A computational strategy to improve access to acute stroke care.

BACKGROUND: Global access to acute stroke treatment is variable worldwide, with notable gaps in low and middle-income countries (LMIC), especially in rural areas. Ensuring a standardized method for pinpointing the existing regional coverage and proposing potential sites for new stroke centers is essential to change this scenario. AIMS: To create and apply computational strategies (CSs) to determine optimal locations for new acute stroke centers (ASCs), with a pilot application in nine Latin American regions/countries. METHODS: Hospitals treating acute ischemic stroke (AIS) with intravenous thrombolysis (IVT) and meeting the minimum infrastructure requirements per structured protocols were categorized as ASCs. Hospitals with emergency departments, noncontrast computed tomography (NCCT) scanners, and 24/7 laboratories were identified as potential acute stroke centers (PASCs). Hospital geolocation data were collected and mapped using the OpenStreetMap data set. A 45-min drive radius was considered the ideal coverage area for each hospital based on the drive speeds from the OpenRouteService database. Population data, including demographic density, were obtained from the Kontur Population data sets. The proposed CS assessed the population covered by ASCs and proposed new ASCs or artificial points (APs) settled in densely populated areas to achieve a target population coverage (TPC) of 95%. RESULTS: The observed coverage in the region presented significant disparities, ranging from 0% in the Bahamas to 73.92% in Trinidad and Tobago. No country/region reached the 95% TPC using only its current ASCs or PASCs, leading to the proposal of APs. For example, in Rio Grande do Sul, Brazil, the introduction of 132 new centers was suggested. Furthermore, it was observed that most ASCs were in major urban hubs or university hospitals, leaving rural areas largely underserved. CONCLUSIONS: The MAPSTROKE project has the potential to provide a systematic approach to identify areas with limited access to stroke centers and propose solutions for increasing access to AIS treatment. DATA ACCESS STATEMENT: Data used for this publication are available from the authors upon reasonable request.

MC1R and age heteroclassification of face phenotypes in the Rio Grande do Sul population.

BACKGROUND: Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA). PURPOSE: Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population. METHODS: Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors. RESULTS: Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as ß values. CONCLUSIONS: There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.

Functional response of microbial communities in lab-controlled oil-contaminated marine sediment.

Crude oil contamination is one of the biggest problems in modern society. As oil enters into contact with the environment, especially if the point of contact is a body of water, it begins a weathering process by mixing and spreading. This is dangerous to local living organisms' communities and can impact diversity. However, despite unfavorable conditions, some microorganisms in these environments can survive using hydrocarbons as a nutrient source. Thus, understanding the local community dynamics of contaminated areas is essential. In this work, we analyzed the 16S rRNA amplicon sequencing and metatranscriptomic data of uncontaminated versus contaminated shallow marine sediment from publicly available datasets. We investigated the local population's taxonomic composition, species diversity, and fluctuations over time. Co-expression analysis coupled with functional enrichment showed us a prevalence of hydrocarbon-degrading functionality while keeping a distinct transcriptional profile between the late stages of oil contamination and the uncontaminated control. Processes related to the degradation of aromatic compounds and the metabolism of propanoate and butanoate were coupled with evidence of enhanced activity such as flagellar assembly and two-component system. Many enzymes of the anaerobic toluene degradation pathways were also enriched in our results. Furthermore, our diversity and taxonomical analyses showed a prevalence of the class Desulfobacteria, indicating interesting targets for bioremediation applications on marine sediment.

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.

Meta-analyses of host metagenomes from colorectal cancer patients reveal strong relationship between colorectal cancer-associated species.

Colorectal cancer (CRC) is one of the most common types of cancer, with many studies associating its development with changes in the gut microbiota. Recent developments in sequencing technologies and subsequent meta-analyses of gut metagenome provided a better understanding of species related to CRC tumorigenesis. Still, the importance of high-importance taxonomic singletons (i.e. species highly associated with a given condition but observed only in the minority of datasets) and the species interactions and co-occurrence across cohorts need further exploration. It has been shown that the gut metagenome presents a high functional redundancy, meaning that species interactions could mitigate the absence of any given species. In a CRC framework, this implies that species co-occurrence could play a role in tumorigenesis, even if CRC-associated species show low abundance. We propose to evaluate the prevalence of microbial species in tumor by initially analyzing each dataset individually and subsequently intersecting the results for differentially abundant species between CRC and healthy samples. We then identify metabolic pathways from these species based on KEGG orthologs, highlighting metabolic pathways associated with CRC. Our results indicate seven species with high prevalence across all projects and with high association to CRC, including the genus Bacteroides, Enterocloster and Prevotella. Finally, we show that CRC is also characterized by the co-occurrence of species that do not present significant differential abundance, but have been described in the literature as potential CRC biomarkers. These results indicate that between-species interactions could also play a role in CRC tumorigenesis.

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.

Ancestry resolution of South Brazilians by forensic 165 ancestry-informative SNPs panel.

Forensic DNA phenotyping (FDP) includes biogeographic ancestry (BGA) inference and externally visible characteristics (EVCs) prediction directly from an evidential DNA sample as alternatives to provide valuable intelligence when conventional DNA profiling fails to achieve identification. In this context, the application of Massively Parallel Sequencing (MPS) methodologies, which enables simultaneous typing of multiple samples and hundreds of forensic markers, has been gradually implemented in forensic genetic casework. The Precision ID Ancestry Panel (Thermo Fisher Scientific, Waltham, USA) is a forensic multiplex assay consisting of 165 autosomal SNPs designed to provide biogeographic ancestry information. In this work, a sample of 250 individuals from Rio Grande do Sul (RS) State, southern Brazil, apportioned into four main population groups (African-, European-, Amerindian-, and Admixed-derived Gauchos), was evaluated with this panel, to assess the feasibility of this approach in a highly heterogeneous population. Forensic descriptive parameters estimated for each population group revealed that this panel has enough polymorphic and informative SNPs to be used as a supplementary instrument in forensic individual identification and kinship testing regardless of ethnicity. No statistically significant deviation from Hardy-Weinberg equilibrium was observed after Bonferroni correction. However, seven loci pairs displayed linkage disequilibrium in pairwise LD testing (p < 3.70 × 10-6). Interpopulation comparisons by FST analysis, MDS plot, and STRUCTURE analysis among the four RS population groups apart and along with 89 reference worldwide populations demonstrated that Admixed- and African-derived Gauchos present the highest levels of admixture and population stratification, whereas European- and Amerindian-derived exhibit a more homogeneous genetic conformation.

Transcriptomic Analysis of Long Non-Coding RNA during Candida albicans Infection.

Candida albicans is one of the most commonly found species in fungal infections. Due to its clinical importance, molecular aspects of the host immune defense against the fungus are of interest to biomedical sciences. Long non-coding RNAs (lncRNAs) have been investigated in different pathologies and gained widespread attention regarding their role as gene regulators. However, the biological processes in which most lncRNAs perform their function are still unclear. This study investigates the association between lncRNAs with host response to C. albicans using a public RNA-Seq dataset from lung samples of female C57BL/6J wild-type Mus musculus with induced C. albicans infection. The animals were exposed to the fungus for 24 h before sample collection. We selected lncRNAs and protein-coding genes related to the host immune response by combining the results from different computational approaches used for gene selection: differential expression gene analysis, co-expression genes network analysis, and machine learning-based gene selection. Using a guilt by association strategy, we inferred connections between 41 lncRNAs and 25 biological processes. Our results indicated that nine up-regulated lncRNAs were associated with biological processes derived from the response to wounding: 1200007C13Rik, 4833418N02Rik, Gm12840, Gm15832, Gm20186, Gm38037, Gm45774, Gm4610, Mir22hg, and Mirt1. Additionally, 29 lncRNAs were related to genes involved in immune response, while 22 lncRNAs were associated with processes related to reactive species production. These results support the participation of lncRNAs during C. albicans infection, and may contribute to new studies investigating lncRNA functions in the immune response.

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.

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.

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants.

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

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.

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.

Evaluation of drug repositioning by molecular docking of pharmaceutical resources available in the Brazilian healthcare system against SARS-CoV-2.

In 2020 SARS-CoV-2 reached pandemic status, reaching Brazil in mid-February. As of now, no specific drugs for treating the disease are available. In this work, the possibility of interaction between SARS-CoV-2 viral proteins (open and closed spike protein, isolate spike protein RBD, NSP 10, NSP 16, main protease, and RdRp polymerase) and multiple molecules is addressed through the repositioning of drugs available for the treatment of other diseases that are approved by the FDA and covered by SUS, the Brazilian Public Health System. Three different docking software were used, followed by a unification of the results by independent evaluation. Afterwards, the chemical interactions of the compounds with the targets were inspected via molecular dynamics and analyzed. The results point to a potential effectiveness of Penciclovir, Ribavirin, and Zanamivir, from a set of 48 potential candidates. They may also be multi-target drugs, showing high affinity with more than one viral protein. Further in vitro and in vivo validation is required to assess the suitability of repositioning the proposed drugs for COVID-19.

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.