Science, medicine, and the future: Bioinformatics

Exploring the evolutionary trajectory and functional landscape of cannabinoid receptors: A comprehensive bioinformatic analysis

The bioinformatic analysis of cannabinoid receptors (CBRs) CB1 and CB2 reveals a detailed picture of their structure, evolution, and physiological significance within the endocannabinoid system (ECS). The study highlights the evolutionary conservation of these receptors evidenced by sequence alignments across diverse species including humans, amphibians, and fish. Both CBRs share a structural hallmark of seven transmembrane (TM) helices, characteristic of class A G-protein-coupled receptors (GPCRs), which are critical for their signalling functions. The study reports a similarity of 44.58 % between both CBR sequences, which suggests that while their evolutionary paths and physiological roles may differ, there is considerable conservation in their structures. Pathway databases like KEGG, Reactome, and WikiPathways were employed to determine the involvement of the receptors in various signalling pathways. The pathway analyses integrated within this study offer a detailed view of the CBRs interactions within a complex network of cannabinoid-related signalling pathways. High-resolution crystal structures (PDB ID: 5U09 for CB1 and 5ZTY for CB2) provided accurate structural information, showing the binding pocket volume and surface area of the receptors, essential for ligand interaction. The comparison between these receptors' natural sequences and their engineered pseudo-CBRs (p-CBRs) showed a high degree of sequence identity, confirming the validity of using p-CBRs in receptor-ligand interaction studies. This comprehensive analysis enhances the understanding of the structural and functional dynamics of cannabinoid receptors, highlighting their physiological roles and their potential as therapeutic targets within the ECS.

Combination therapy synergism prediction for virus treatment using machine learning models

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.

Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma

BACKGROUND

The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

METHODS

Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis.

RESULTS

In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction.

CONCLUSIONS

The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer

Background/aim

LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets. LUNGBANK functions not merely as a repository but as a sophisticated platform crucial for advancing lung cancer research, poised to facilitate significant discoveries.

Materials and methods

LUNGBANK was meticulously designed to optimize every stage of biospecimen handling, from collection and storage to processing. Rigorous standard operating procedures and stringent quality control measures guarantee the integrity of collected biospecimens. Advanced data management protocols facilitate the efficient integration and analysis of various datasets, enhancing the depth and breadth of research possibilities in lung cancer.

Results

LUNGBANK has amassed a comprehensive collection of biospecimens essential for unraveling the intricate molecular mechanisms of lung cancer. The integration of state-of-the-art technologies ensures the acquisition of top-tier data, fostering breakthroughs in translational and histological research. Moreover, the establishment of patient-derived systems by LUNGBANK underscores its pivotal role in personalized medicine approaches.

Conclusion

The establishment of LUNGBANK marks a significant milestone in addressing the critical challenges of lung cancer research. By providing researchers with high-quality biospecimens and advanced research tools, LUNGBANK not only supports Project LUNGMARK's objectives but also contributes extensively to the broader landscape of personalized medicine. It promises to enhance our understanding of lung cancer initiation, progression, and therapeutic interventions tailored to individual patient needs, thereby advancing the field towards more effective diagnostic and therapeutic strategies.

Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation

Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are "endophyte," "green synthesis," "antimicrobial," "anti-cancer," and "biological activities," indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.

Integrative Bioinformatics-Gene Network Approach Reveals Linkage between Estrogenic Endocrine Disruptors and Vascular Remodeling in Peripheral Arterial Disease

Vascular diseases, including peripheral arterial disease (PAD), pulmonary arterial hypertension, and atherosclerosis, significantly impact global health due to their intricate relationship with vascular remodeling. This process, characterized by structural alterations in resistance vessels, is a hallmark of heightened vascular resistance seen in these disorders. The influence of environmental estrogenic endocrine disruptors (EEDs) on the vasculature suggests a potential exacerbation of these alterations. Our study employs an integrative approach, combining data mining with bioinformatics, to unravel the interactions between EEDs and vascular remodeling genes in the context of PAD. We explore the molecular dynamics by which EED exposure may alter vascular function in PAD patients. The investigation highlights the profound effect of EEDs on pivotal genes such as ID3, LY6E, FOS, PTP4A1, NAMPT, GADD45A, PDGF-BB, and NFKB, all of which play significant roles in PAD pathophysiology. The insights gained from our study enhance the understanding of genomic alterations induced by EEDs in vascular remodeling processes. Such knowledge is invaluable for developing strategies to prevent and manage vascular diseases, potentially mitigating the impact of harmful environmental pollutants like EEDs on conditions such as PAD.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications

BACKGROUND

The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.

RESULTS

CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Deep learning in bioinformatics

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Advancements in portable instruments based on affinity-capture-migration and affinity-capture-separation for use in clinical testing and life science applications

The shift from testing at centralized diagnostic laboratories to remote locations is being driven by the development of point-of-care (POC) instruments and represents a transformative moment in medicine. POC instruments address the need for rapid results that can inform faster therapeutic decisions and interventions. These instruments are especially valuable in the field, such as in an ambulance, or in remote and rural locations. The development of telehealth, enabled by advancements in digital technologies like smartphones and cloud computing, is also aiding in this evolution, allowing medical professionals to provide care remotely, potentially reducing healthcare costs and improving patient longevity. One notable POC device is the lateral flow immunoassay (LFIA), which played a major role in addressing the COVID-19 pandemic due to its ease of use, rapid analysis time, and low cost. However, LFIA tests exhibit relatively low analytical sensitivity and provide semi-quantitative information, indicating either a positive, negative, or inconclusive result, which can be attributed to its one-dimensional format. Immunoaffinity capillary electrophoresis (IACE), on the other hand, offers a two-dimensional format that includes an affinity-capture step of one or more matrix constituents followed by release and electrophoretic separation. The method provides greater analytical sensitivity, and quantitative information, thereby reducing the rate of false positives, false negatives, and inconclusive results. Combining LFIA and IACE technologies can thus provide an effective and economical solution for screening, confirming results, and monitoring patient progress, representing a key strategy in advancing diagnostics in healthcare.

Building awareness and capacity of bioinformatics and open science skills in Kenya: a sensitize, train, hack, and collaborate model

We have applied the sensitize-train-hack-community model to build awareness of and capacity in bioinformatics in Kenya. Open science is the practice of science openly and collaboratively, with tools, techniques, and data freely shared to facilitate reuse and collaboration. Open science is not a mandatory curriculum course in schools, whereas bioinformatics is relatively new in some African regions. Open science tools can significantly enhance bioinformatics, leading to increased reproducibility. However, open science and bioinformatics skills, especially blended, are still lacking among students and researchers in resource-constrained regions. We note the need to be aware of the power of open science among the bioinformatics community and a clear strategy to learn bioinformatics and open science skills for use in research. Using the OpenScienceKE framework-Sensitize, Train, Hack, Collaborate/Community-the BOSS (Bioinformatics and Open Science Skills) virtual events built awareness and empowered researchers with the skills and tools in open science and bioinformatics. Sensitization was achieved through a symposium, training through a workshop and train-the-trainer program, hack through mini-projects, community through conferences, and continuous meet-ups. In this paper, we discuss how we applied the framework during the BOSS events and highlight lessons learnt in planning and executing the events and their impact on the outcome of each phase. We evaluate the impact of the events through anonymous surveys. We show that sensitizing and empowering researchers with the skills works best when the participants apply the skills to real-world problems: project-based learning. Furthermore, we have demonstrated how to implement virtual events in resource-constrained settings by providing Internet and equipment support to participants, thus improving accessibility and diversity.

Molecular docking analysis of novel quercetin derivatives for combating SARS-CoV-2

Quercetin belongs to the flavonoid family, which is one of the most frequent types of plant phenolics. This flavonoid compound is a natural substance having a number of pharmacological effects, including anticancer and antioxidant capabilities, as well as being a strong inhibitor of various toxicologically important enzymes. We discuss the potential of newly recently synthesized quercetin-based derivatives to inhibit SARS-CoV-2 protein. ADMET analysis indicated that all of the studied compounds had low toxicities and good absorption and solubility properties. The molecular docking results revealed that the propensity for binding to SARS-CoV-2 main protease is extraordinary. The results are remarkable not only for the binding energy values, which outperform several compounds in prior studies, but also for the number of hydrogen bonds formed. Compound 7a was capable of forming 10 strong hydrogen bonds as well as interact to the protein receptor with a binding energy of -7.79 kcal/mol. Therefore, these compounds should be highlighted in further experimental studies in the context of treating SARS-CoV-2 infection and its effects.

Zhachong Shisanwei Pill resists ischemic stroke by lysosome pathway based on proteomics and bioinformatics

ETHNOPHARMACOLOGICAL RELEVANCE

Zhachong Shisanwei Pill (ZSP) is a commonly used Mongolian medicine in treating cerebrovascular diseases and plays a role in the clinical treatment of ischemic stroke (IS).

AIM OF THE STUDY

Based on determining the protective effect of ZSP on cerebral ischemia, they adopted the proteomics method to explore the mechanism of ZSP against IS.

MATERIALS AND METHODS

Rats with middle cerebral artery occlusion (MCAO) model were prepared by wire embolization method, and divided into sham group, model group, ZSP high-dose group, medium-dose group, low-dose group and positive drug group. We collected the brain tissue of rats for 12 h after modeling. Neurological deficit score and cerebral infarction volume ratio evaluated pharmacodynamics, and we selected the optimal dose for subsequent experiments. Proteomics was used to screen out possible ZSP anti-IS mediated pathways and differentially expression proteins. Network pharmacology was used to verify the correlation between diseases and drugs. Hematoxylin-eosin (HE) staining and transmission electron microscope (TEM) were used to explore further the pharmacodynamic effect of ZSP against IS and its possible mechanism.

RESULTS

The cerebral infarction rate and neurological function score in rats showed that the medium-dose ZSP group had the best efficacy. Proteomics results showed that the anti-IS action of ZSP was mainly through lysosome pathway. LAMP2, AP3M1, and SCARB2 were the differentially changed proteins in this pathway. Network pharmacology verified this. HE staining and TEM results showed that ZSP could improve the pathological state of neurons in MCAO rats and reduce the number of lysosomes in MCAO rats. Western blot (WB) results showed that compared with the model group, the protein expression levels of LAMP2 and AP3M1 in the ZSP group were significantly down-regulated, and the protein expression levels of SCARB2 were significantly up-regulated.

CONCLUSION

This study confirms that ZSP regulates the lysosomal pathway, which may protect IS by down-regulating LAMP2 and AP3M1 and up-regulating SCARB2.

Analysis of Persian Bioinformatics Research with Topic Modeling

Purpose

As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively.

Conclusion

The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other.

Exploring the Role of Obesity in Dilated Cardiomyopathy Based on Bio-informatics Analysis

(1) Background: Obesity is a major risk factor for cardiovascular disease (CVD), contributing to increasing global disease burdens. Apart from heart failure, coronary artery disease, and arrhythmia, recent research has found that obesity also elevates the risk of dilated cardiomyopathy (DCM). The main purpose of this study was to investigate the underlying biological role of obesity in increasing the risk of DCM. (2) Methods: The datasets GSE120895, GSE19303, and GSE2508 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using GSE120895 for DCM and GSE2508 for obesity, and the findings were compiled to discover the common genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted for the common genes in RStudio. In addition, CIBERSORT was used to obtain the immune cellular composition from DEGs. The key genes were identified in the set of common genes by the least absolute shrinkage and selection operator (LASSO) algorithm, the prognostic risk models of which were verified by receiver operator characteristic (ROC) curves in GSE19303. Finally, Spearman's correlation was used to explore the connections between key genes and immune cells. (3) Results: GO and KEGG pathway enrichment analyses showed that the main enriched terms of the common genes were transforming growth factor-beta (TGF-β), fibrillar collagen, NADPH oxidase activity, and multiple hormone-related signaling pathways. Both obesity and DCM had a disordered immune environment, especially obesity. The key genes NOX4, CCDC80, COL1A2, HTRA1, and KLHL29 may be primarily responsible for the changes. Spearman's correlation analysis performed for key genes and immune cells indicated that KLHL29 closely correlated to T cells and M2 macrophages, and HTRA1 very tightly correlated to plasma cells. (4) Conclusions: Bio-informatics analyses performed for DCM and obesity in our study suggested that obesity disturbed the immune micro-environment, promoted oxidative stress, and increased myocardial fibrosis, resulting in ventricular remodeling and an increased risk of DCM. The key genes KLHL29 and HTRA1 may play critical roles in obesity-related DCM.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

Identification and Validation of Novel Biomarkers and Potential Targeted Drugs in Cholangiocarcinoma: Bioinformatics, Virtual Screening, and Biological Evaluation

Cholangiocarcinoma (CCA) is a complex and refractor type of cancer with global prevalence. Several barriers remain in CCA diagnosis, treatment, and prognosis. Therefore, exploring more biomarkers and therapeutic drugs for CCA management is necessary. CCA gene expression data was downloaded from the TCGA and GEO databases. KEGG enrichment, GO analysis, and protein-protein interaction network were used for hub gene identification. miRNA were predicted using Targetscan and validated according to several GEO databases. The relative RNA and miRNA expression levels and prognostic information were obtained from the GEPIA. The candidate drug was screened using pharmacophore-based virtual screening and validated by molecular modeling and through several in vitro studies. 301 differentially expressed genes (DEGs) were screened out. Complement and coagulation cascades-related genes (including AHSG, F2, TTR, and KNG1), and cell cycle-related genes (including CDK1, CCNB1, and KIAA0101) were considered as the hub genes in CCA progression. AHSG, F2, TTR, and KNG1 were found to be significantly decreased and the eight predicted miRNA targeting AHSG, F2, and TTR were increased in CCA patients. CDK1, CCNB1, and KIAA0101 were found to be significantly abundant in CCA patients. In addition, Molport-003-703-800, which is a compound that is derived from pharmacophores-based virtual screening, could directly bind to CDK1 and exhibited anti-tumor activity in cholangiocarcinoma cells. AHSG, F2, TTR, and KNG1 could be novel biomarkers for CCA. Molport-003-703-800 targets CDK1 and work as potential cell cycle inhibitors, thereby having potential for consideration for new chemotherapeutics for CCA.

DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

Role of lipidomics in assessing the functional lipid composition in breast milk

Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.

Comprehensive mutations analyses of FTO (fat mass and obesity-associated gene) and their effects on FTO’s substrate binding implicated in obesity

An excessive amount of fat deposition in the body leads to obesity which is a complex disease and poses a generic threat to human health. It increases the risk of various other diseases like diabetes, cardiovascular disease, and multiple types of cancer. Genomic studies have shown that the expression of the fat mass obesity (FTO) gene was highly altered and identified as one of the key biomarkers for obesity. This study has been undertaken to investigate the mutational profile of the FTO gene and elucidates its effect on the protein structure and function. Harmful effects of various missense mutations were predicted using different independent tools and it was observed that all mutations were highly pathogenic. Molecular dynamics (MD) simulations were performed to study the structure and function of FTO protein upon different mutations and it was found that mutations decreased the structure stability and affected protein conformation. Furthermore, a protein residue network analysis suggested that the mutations affected the overall residues bonding and topology. Finally, molecular docking coupled with MD simulation suggested that mutations affected FTO substrate binding by changing the protein-ligand affinity. Hence, the results of this finding would help in an in-depth understanding of the molecular biology of the FTO gene and its variants and lead to the development of effective therapeutics against associated diseases and disorders.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Taohong Siwu Decoction exerts anticancer effects on breast cancer via regulating MYC, BIRC5, EGF and PIK3R1 revealed by HTS2 technology

Taohong Siwu Decoction (TSD), a classical gynecological prescription that was firstly reported 600 years ago, has been widely used in the adjuvant treatment of breast cancer (BRCA) in China. However, the mechanism of action of TSD in treating BRCA has remained unclear. Here, high-throughput sequencing-based high-throughput screening (HTS²) technology was used to reveal the molecular mechanism of TSD, combination with bioinformatics and systems pharmacology in this study. Firstly, our results showed that TSD exerts an anticancer effect on BRCA cells by inhibiting cell proliferation, migration and inducing apoptosis as well as cell-cycle arrest. And our results from HTS² suggested that herbs of TSD could significantly inhibit KRAS pathway and pathway in cancer, and activate apoptosis pathway, p53 pathway and hypoxia pathway, which may lead to the anticancer function of TSD. Further, we found that TSD clearly regulates MYC, BIRC5, EGF, and PIK3R1 genes, which play an important role in the development and progression of tumor and have significant correlation with overall survival in BRCA patients. By molecular docking, we discovered that Pentagalloylglucose, a compound derived from TSD, might directly bind to and inhibit the function of BRD4, which is a reported transcriptional activator of MYC gene, and thus repress the expression of MYC. Taken together, this study explores the mechanism of TSD in anti-BRCA by combining HTS² technology, bioinformatics analysis and systems pharmacology.

Machine Learning for Vestibular Schwannoma Diagnosis Using Audiometrie Data Alone

OBJECTIVE

The aim of this study is to compare machine learning algorithms and established rule-based evaluations in screening audiograms for the purpose of diagnosing vestibular schwannomas. A secondary aim is to assess the performance of rule-based evaluations for predicting vestibular schwannomas using the largest dataset in the literature.

STUDY DESIGN

Retrospective case-control study.

SETTING

Tertiary referral center.

PATIENTS

Seven hundred sixty seven adult patients with confirmed vestibular schwannoma and a pretreatment audiogram on file and 2000 randomly selected adult controls with audiograms.

INTERVENTIONS

Audiometric data were analyzed using machine learning algorithms and standard rule-based criteria for defining asymmetric hearing loss.

MAIN OUTCOME MEASURES

The primary outcome is the ability to identify patients with vestibular schwannomas based on audiometric data alone, using machine learning algorithms and rule-based formulas. The secondary outcome is the application of conventional rule-based formulas to a larger dataset using advanced computational techniques.

RESULTS

The machine learning algorithms had mildly improved specificity in some fields compared with rule-based evaluations and had similar sensitivity to previous rule-based evaluations in diagnosis of vestibular schwannomas.

CONCLUSIONS

Machine learning algorithms perform similarly to rule-based evaluations in identifying patients with vestibular schwannomas based on audiometric data alone. Performance of established rule-based formulas was consistent with earlier performance metrics, when analyzed using a large dataset.

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data

Bioinformatic analysis-such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, file format conversion, and processing and analysis-is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses, but unified toolkits that conduct all these analyses are lacking. To address this gap, we introduce BioKIT, a versatile command line toolkit that has, upon publication, 42 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwyk-biokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

Improving bioinformatics software quality through incorporation of software engineering practices

BACKGROUND

Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software.

METHODOLOGY

A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software.

RESULTS

The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers.

CONCLUSIONS

While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Infoecology of the deep learning and smart manufacturing: thematic and concept interactions

Purpose

This infecological study mainly aimed to know the thematic and conceptual relationship in published papers in deep learning (DL) and smart manufacturing (SM).

Design/methodology/approach

The research methodology has specific research objectives based on the type and method of research, data analysis tools. In general, description methods are applied by Web of Science (WoS) analysis tools and Voyant tools as a web-based reading and analysis environment for digital texts. The Yewno tool is applied to draw a knowledge map to show the concept's interaction between DL and SM.

Findings

The knowledge map of DL and SM concepts shows that there are currently few concepts interacting with each other, while the rapid growth of technology and the needs of today's society have revealed the need to use more and more DL in SM. The results of this study can provide a coherent and well-mapped road map to the main policymakers of the field of research in DL and SM, through the study of coexistence and interactions of the thematic categories with other thematic areas. In this way, they can design more effective guidelines and strategies to solve the problems of researchers in conducting their studies and direct. The analysis results demonstrated that the information ecosystem of DL and SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of subjects and concepts in this area.

Originality/value

The paper investigated the thematic interaction of the scientific productions in DL and SM and discussed possible implications. We used of the variety tools and techniques to draw our own perspective. Also, we drew arguments from other research work to back up our findings.

Prediction of the Effects of Variants and Differential Expression of Key Host Genes ACE2, TMPRSS2, and FURIN in SARS-CoV-2 Pathogenesis: An In Silico Approach

A new strain of the beta coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is solely responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic. Although several studies suggest that the spike protein of this virus interacts with the cell surface receptor, angiotensin-converting enzyme 2 (ACE2), and is subsequently cleaved by TMPRSS2 and FURIN to enter into the host cell, conclusive insight about the interaction pattern of the variants of these proteins is still lacking. Thus, in this study, we analyzed the functional conjugation among the spike protein, ACE2, TMPRSS2, and FURIN in viral pathogenesis as well as the effects of the mutations of the proteins through the implementation of several bioinformatics approaches. Analysis of the intermolecular interactions revealed that T27A (ACE2), G476S (receptor-binding domain [RBD] of the spike protein), C297T (TMPRSS2), and P812S (cleavage site for TMPRSS2) coding variants may render resistance in viral infection, whereas Q493L (RBD), S477I (RBD), P681R (cleavage site for FURIN), and P683W (cleavage site for FURIN) may lead to increase viral infection. Genotype-specific expression analysis predicted several genetic variants of ACE2 (rs2158082, rs2106806, rs4830971, and rs4830972), TMPRSS2 (rs458213, rs468444, rs4290734, and rs6517666), and FURIN (rs78164913 and rs79742014) that significantly alter their normal expression which might affect the viral spread. Furthermore, we also found that ACE2, TMPRSS2, and FURIN proteins are functionally co-related with each other, and several genes are highly co-expressed with them, which might be involved in viral pathogenesis. This study will thus help in future genomics and proteomics studies of SARS-CoV-2 and will provide an opportunity to understand the underlying molecular mechanism during SARS-CoV-2 pathogenesis.

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Converting tabular data into images for deep learning with convolutional neural networks

Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

Intrinsic and extrinsic pathways of apoptosis: Role in cancer development and prognosis

Apoptosis, also named programmed cell death, is a fundament process required for morphogenetic homeostasis during early development and in pathophysiological conditions. It is come into existence in 1972 by work of Kerr, Wyllie and Currie and later on investigated during the research on development of the C. elegans. Trigger by several stimuli, apoptosis is necessary during the embryonic development and aging as homeostatic mechanism to control the cell population and also play a key role as defense mechanism against the immune responses and elimination of damaged cells. Cancer, a genetic disease, is a growing burden on the health and economy of both developing and developed countries. Every year there is tremendously increasing in the number of new cancer cases and mortality rate. Although, there is a significant improvement have been made in biotechnological and bioinformatic fields however, the therapeutic advantages and cancer etiology is still under explored. Several studies determined the deregulation of different apoptotic components during the cancer development and progression. Apoptosis relies on activation of distinct signaling pathways that are often deregulated in cancer. Thus, exploring the single or more than one apoptotic component underlying their expression in carcinogenesis could help to track the disease progression. Current book chapter will provide the several evidences supporting the use of different apoptotic components as prognosis and prediction markers in various human cancer types.

Application of Viral Metagenomics for Study of Emerging and Reemerging Tick-Borne Viruses

Ticks are important vectors for different tick-borne viruses, some of which cause diseases and death in humans, livestock, and wild animals. Tick-borne encephalitis virus, Crimean-Congo hemorrhagic fever virus, Kyasanur forest disease virus, severe fever with thrombocytopenia syndrome virus, Heartland virus, African swine fever virus, Nairobi sheep disease virus, and Louping ill virus are just a few examples of important tick-borne viruses. The majority of tick-borne viruses have RNA genomes that routinely undergo rapid genetic modifications such as point mutations during their replication. These genomic changes can influence the spread of viruses to new habitats and hosts and lead to the emergence of novel viruses that can pose a threat to public health. Therefore, investigation of the viruses circulating in ticks is important to understand their diversity, host and vector range, and evolutionary history, as well as to predict new emerging pathogens. The choice of detection method is important, as most methods detect only those viruses that have been previously well described. On the other hand, viral metagenomics is a useful tool to simultaneously identify all the viruses present in a sample, including novel variants of already known viruses or completely new viruses. This review describes tick-borne viruses, their historical background of emergence, and their reemergence in nature, and the use of viral metagenomics for viral discovery and studies of viral evolution.

Big data and stratified medicine: what does it mean for children?

Stratified medicine in paediatrics is increasingly becoming a reality, as our understanding of disease pathogenesis improves and novel treatment targets emerge. We have already seen some success in paediatrics in targeted therapies such as cystic fibrosis for specific cystic fibrosis transmembrane conductance regulator variants. With the increased speed and decreased cost of processing and analysing data from rare disease registries, we are increasingly able to use a systems biology approach (including '-omics') to screen across populations for molecules and genes of interest. Improving our understanding of the molecular mechanisms underlying disease, and how to classify patients according to these will lead the way for targeted therapies for individual patients. This review article will summarise how 'big data' and the 'omics' are being used and developed, and taking examples from paediatric renal medicine and rheumatology, demonstrate progress being made towards stratified medicine for children.

Colombia, an unknown genetic diversity in the era of Big Data

BACKGROUND

Latin America harbors some of the most biodiverse countries in the world, including Colombia. Despite the increasing use of cutting-edge technologies in genomics and bioinformatics in several biological science fields around the world, the region has fallen behind in the inclusion of these approaches in biodiversity studies. In this study, we used data mining methods to search in four main public databases of genetic sequences such as: NCBI Nucleotide and BioProject, Pathosystems Resource Integration Center, and Barcode of Life Data Systems databases. We aimed to determine how much of the Colombian biodiversity is contained in genetic data stored in these public databases and how much of this information has been generated by national institutions. Additionally, we compared this data for Colombia with other countries of high biodiversity in Latin America, such as Brazil, Argentina, Costa Rica, Mexico, and Peru.

RESULTS

In Nucleotide, we found that 66.84% of total records for Colombia have been published at the national level, and this data represents less than 5% of the total number of species reported for the country. In BioProject, 70.46% of records were generated by national institutions and the great majority of them is represented by microorganisms. In BOLD Systems, 26% of records have been submitted by national institutions, representing 258 species for Colombia. This number of species reported for Colombia span approximately 0.46% of the total biodiversity reported for the country (56,343 species). Finally, in PATRIC database, 13.25% of the reported sequences were contributed by national institutions. Colombia has a better biodiversity representation in public databases in comparison to other Latin American countries, like Costa Rica and Peru. Mexico and Argentina have the highest representation of species at the national level, despite Brazil and Colombia, which actually hold the first and second places in biodiversity worldwide.

CONCLUSIONS

Our findings show gaps in the representation of the Colombian biodiversity at the molecular and genetic levels in widely consulted public databases. National funding for high-throughput molecular research, NGS technologies costs, and access to genetic resources are limiting factors. This fact should be taken as an opportunity to foster the development of collaborative projects between research groups in the Latin American region to study the vast biodiversity of these countries using 'omics' technologies.

Bioinformatics and Its Therapeutic Applications

Bioinformatics has emerged as a major element in contemporary biomedical and pharmaceutical region. Bioinformatics deals with growth in biological data and has led to development of many databases. Bioinformatics deals with collection of data that is relevant clinically and these days separate term clinical information has come up. Data mimics are another field which is gaining importance. This chapter shall deal with introduction of bioinformatics and its applications in medicine and health care.

Human Endogenous Retroviruses (HERVs) and Autoimmune Rheumatic Disease: Is There a Link?

Autoimmune rheumatic diseases, such as RA and SLE, are caused by genetic, hormonal and environmental factors. Human Endogenous Retroviruses (HERVs) may be triggers of autoimmune rheumatic disease. HERVs are fossil viruses that began to be integrated into the human genome some 30-40 million years ago and now make up 8% of the genome. Evidence suggests HERVs may cause RA and SLE, among other rheumatic diseases. The key mechanisms by which HERVS are postulated to cause disease include molecular mimicry and immune dysregulation. Identification of HERVs in RA and SLE could lead to novel treatments for these chronic conditions. This review summarises the evidence for HERVs as contributors to autoimmune rheumatic disease and the clinical implications and mechanisms of pathogenesis are discussed.

Systems biology of human benzene exposure

Toxicogenomic studies, including genome-wide analyses of susceptibility genes (genomics), gene expression (transcriptomics), protein expression (proteomics), and epigenetic modifications (epigenomics), of human populations exposed to benzene are crucial to understanding gene-environment interactions, providing the ability to develop biomarkers of exposure, early effect and susceptibility. Comprehensive analysis of these toxicogenomic and epigenomic profiles by bioinformatics in the context of phenotypic endpoints, comprises systems biology, which has the potential to comprehensively define the mechanisms by which benzene causes leukemia. We have applied this approach to a molecular epidemiology study of workers exposed to benzene. Hematotoxicity, a significant decrease in almost all blood cell counts, was identified as a phenotypic effect of benzene that occurred even below 1 ppm benzene exposure. We found a significant decrease in the formation of progenitor colonies arising from bone marrow stem cells with increasing benzene exposure, showing that progenitor cells are more sensitive to the effects of benzene than mature blood cells, likely leading to the observed hematotoxicity. Analysis of transcriptomics by microarray in the peripheral blood mononuclear cells of exposed workers, identified genes and pathways (apoptosis, immune response, and inflammatory response) altered at high (>10 ppm) and low (<1 ppm) benzene levels. Serum proteomics by SELDI-TOF-MS revealed proteins consistently down-regulated in exposed workers. Preliminary epigenomics data showed effects of benzene on the DNA methylation of specific genes. Genomic screens for candidate genes involved in susceptibility to benzene toxicity are being undertaken in yeast, with subsequent confirmation by RNAi in human cells, to expand upon the findings from candidate gene analyses. Data on these and future biomarkers will be used to populate a large toxicogenomics database, to which we will apply bioinformatic approaches to understand the interactions among benzene toxicity, susceptibility genes, mRNA, and DNA methylation through a systems biology approach.