bioinformatics

Laccase catalyzes oxidation of lignin and aromatic compound with similar structure to this one. Their low substrate specificity results on degradation of similar phenolic compounds. In this context, Molecular Docking was performed with different ligands suggesting potential bio-degradation. Binding active-sites prediction of fungal laccase (access number uniprotkb: A0A166P2X0), from Ganoderma weberianum was performed using machine learning algorithm based on Deep Convolutional Neural Networks (DeepSite-CNNs chemoinformatic tool). Herein, ligands like 2,4 - dichlorophenol, benzidine, sulfisoxazole, trimethoprim and tetracycline were analyzed and two additional reference controls which were 2,2 – azinobis 3 – ethylbenzothiazoline – 6 - sulfonic acid (ABTS) and 2,6 - dimetoxyphenol (2,6 DMP) were used in comparison with the other former mentioned ligands based on high laccase affinity. The five ligands were carried out because their potential biotechnological interest: the antibiotics sulfisoxazole, trimethoprim and tetracycline, and xenobiotics 2,4 - dichlorophenol and benzidine. Molecular docking experiments returned Gibbs free energy of binding (FEB or affinity) for laccase-ligand complexes. The best docking binding-interaction from each laccase-ligand conformation complexes suggest great ability of these ligands to interact with the laccase active-binding site. Herein, FEB values (kcal/mol) were obtained with higher affinity values for reference controls like 2,6 - dimethoxyphenol with -4.8 Kcal/mol and ABTS with -7.1 Kcal/mol. Furthermore, the FEB values were -4.7, -6.5, -6.8, -5.2 and -6.5 Kcal/mol, for 2,4 - dichlorophenol, benzidine, sulfisoxazole, tetracycline and trimethoprim respectively with high prevalence of hydrophobic interaction with functional laccase binding residues. Lastly, this study presents for first time at the bioinformatics field a molecular docking approach for the prediction of potential substrate of laccase from Ganoderma weberianum towards biotechnological application.

Colon cancer (CC) screening is important for diagnosing early stage for malignancy and therefore potentially reduces mortality from this disease because the cancer could be cured at the early disease stage. Early detection is needed if accurate and cost effective diagnostic methods are available. Mortality from colon cancer is theoretically preventable through screening. The Current screening method, the immunological fecal occult blood test, FOBTi, lacks sensitivity and requires dietary restriction, which impedes compliance. Moreover colonoscopy is invasive and costly, which decreases compliance, and in certain cases could lead to mortality. Compared to the FOBT test, a noninvasive sensitive screen that does not require dietary restriction would be more convenient. Colonoscopy screening is recommended for colorectal cancer (CRC). Although it is a reliable screening method, colonoscopy is an invasive test, often accompanied by abdominal pain, has potential complications and has high cost, which have hampered its application worldwide. A screening approach that uses the relatively stable and nondegradable microRNA molecules when extracted from either the noninvasive human stool, or the semi-invasive blood samples by available commercial kits and manipulated thereafter, would be more preferable than a transcriptomic messenger (m)RNA-, a mutation DNA-, an epigenetic-or a proteomic-based test. That approach utilizes reverse transcriptase (RT), followed by a modified quantitative real-time polymerase chain reaction (qPCR). To compensate for exosomal miRNAs that would not be measured, a parallel test could be performed on stool or plasma’s total RNAs, and corrections for exosomal loss are made to obtain accurate results. Ultimately, a chip would be developed to facilitate diagnosis, as has been carried out for the quantification of genetically modified organisms (GMOs) in foods. The gold standard to which the miRNA test is compared to is colonoscopy. If laboratory performance criteria are met, a miRNA test in human stool or blood samples based on high throughput automated technologies and quantitative expression measurements currently employed in the diagnostic clinical laboratory, would eventually be advanced to the clinical setting, making a noticeable impact on the prevention of colon cancer.

Modern-day biology is witnessing a data explosion with a vast amount of information generated from ongoing genome and sequencing projects. The abundance of data from genome sequences, functional genomics and another high throughput (HTP) technique with the potential of computing has led to rising of a new discipline namely ‘bioinformatics’. Bioinformatics is a young but fast-growing field for biological data collection, organization, interpretation, and modeling. Tools and techniques for bioinformatics are derived from multidisciplinary combinations of varied disciplines from natural and physical sciences. Previously various disciplines were carved out as and when sufficient specialization was achieved. However, now bioinformatics is borne out of an alliance between existing disciplines from life and non-life. Bioinformatics encompasses new foundations for the collection, organization, and mining of gene/ protein sequences, three-dimensional structures, and biochemical functions, for modeling biological processes of functioning cells. DNA sequencing performed on an industrial scale has produced a vast amount of data to analyze. Although the Human Genome Project is officially over, improvements in DNA sequencing continue to be made. The field of forensic science is increasingly based on biomolecular data and many European countries are establishing forensic databases to store DNA profiles of crime scenes of known offenders and apply DNA testing.

Background: Acute rheumatic fever (ARF) is a systemic inflammatory disease resulting from an abnormal immune response to group A β-hemolytic streptococci. ARF is a major public health problem in developing countries, particularly in Senegal. The aim of this study was to evaluate the mutation penetrance and genetic diversity of exon 2 of the HLA-DRB1 gene in Senegalese patients with ARF. Results: DNA was extracted from the blood of patients with ARF. Exon 2 of the HLA-DRB1 gene was amplified by polymerase chain reaction and sequenced using the Sanger method. Bioinformatics software and databases (polyphen-2, SIFT and ProVean) were used to assess the pathogenicity of missense mutations. The results revealed a high level of polymorphism in exon 2 of the HLA-DRB1 gene, with 73 non-synonymous mutations between codons 21 and 89, which lie in the hypervariable region encoded by exon 2. Of the 73 variants tested, 44% were pathogenic, indicating their potential involvement in ARF onset. Conclusion: Our results indicate that the HLA-DRB1 mutations involvement in the onset of rheumatic fever.

The determination of a protein structure by using X-ray diffraction encompasses a series of sequential steps (including gene identification and cloning, protein expression and purification, crystallization, phasing model building, refinement, and validation), which need the application of several methodologies derived from molecular biology, bioinformatics, and physical sciences. This article thoroughly examines the complicated procedure of elucidating protein structures within plant biology, using X-ray diffraction as the primary methodology. Commencing with the gene identification process and progressing toward crystallography, this article explores the many obstacles and achievements in acquiring diffraction pictures and their subsequent conversion into electron density maps. The ensuing phases of model construction, refinement, and structural validation are thoroughly examined, providing insight into the inherent complexity associated with each stage. The paper also discusses the critical component of understanding the resultant model and scrutinizing its biological significance. By comprehensively examining these stages, this article presents a nuanced comprehension of the intricate procedure in ascertaining protein structures within plant biology. It offers valuable insights into the obstacles encountered and the biological importance of the acquired structural data.

Sepsis, a life-threatening condition triggered by infection, poses a significant healthcare challenge with high mortality rates. The interplay between genetics and the immune response in sepsis, particularly in surgical and trauma patients, is complex and critical. Genetic polymorphisms, particularly in cytokine genes like TNF-α, IL-6, and IL-8, have been extensively studied for their influence on sepsis susceptibility, severity, and outcomes. Polymorphisms can alter gene expression and cytokine production, leading to variations in immune responses. Studies have also explored polymorphisms concerning sepsis in genes encoding CD86, TLR4, and SIRT6. This review highlights the association between genetic polymorphisms and inflammatory responses, focusing on their impact on sepsis outcomes in surgical and trauma patients. Genetic variations play a significant role in sepsis risk, severity, and prognosis, with potential implications for personalized therapeutic strategies. Biomarkers such as cytokine gene polymorphisms may aid in predicting sepsis risk and guiding treatment decisions. Complementary therapies like acupuncture and novel biomarkers like microvesicles carrying mitochondrial content provide additional avenues for personalized sepsis management. Furthermore, multiomics approaches offer promise in predicting postoperative outcomes in surgical patients. Understanding the genetic basis of sepsis is essential for improving prevention, diagnosis, and treatment, ultimately leading to better clinical outcomes. Combining genomics, bioinformatics, and clinical expertise, precision medicine can revolutionize sepsis management by tailoring interventions to individual genetic profiles, thus enhancing patient care and outcomes.

Herbarium records provide a valuable historical database for assessing plant phenology shifts in the context of global climate change. The herbarium specimens, collected from diverse locations and periods, offer comprehensive data illustrating how many plants are altering their blooming times in response to global climate change. The appropriate use and analysis of long-term herbarium records offer an additional dimension for the study of plant phenology through the application of advanced experimental methodologies such as bioinformatics and satellite imagery, statistics, and Artificial Intelligence (AI) which, coupled with field observations, will improve ecosystems evaluation. These efforts can significantly contribute to conservation strategies and climate change mitigation and further support the synchronization of scientific inputs for evaluating the impacts of climate change and its ecological implications.