Comparative genomics and evolution of avian specialized traits

Genomic data are important for understanding the origin and evolution of traits. Under the context of rapidly developing sequencing technologies and more widely available genome sequences, researchers are able to study evolutionary mechanisms of traits via comparative genomic methods. Compared with other vertebrates, bird genomes are relatively small and exhibit conserved synteny with few repetitive elements, which makes them suitable for evolutionary studies. Increasing genomic progress has been reported on the evolution of powered flight, body size variation, beak morphology, plumage coloration, high-elevation colonization, migration, and vocalization. By summarizing previous studies, we demonstrate the genetic bases of trait evolution, highlighting the roles of small-scale sequence variation, genomic structural variation, and changes in gene interaction networks. We suggest that future studies should focus on improving the quality of reference genomes, exploring the evolution of regulatory elements and networks, and combining genomic data with morphological, ecological, behavioral, and developmental biology data.

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

Background: Immune and skeletal systems physiologically and pathologically interact with each other. The immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown Objective: This study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia and fracture) Methods: The canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. Versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA. Results: About 157 (p<8.19E-6), 319 (p<3.90E-6) and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune disease, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1 and TSBP1-AS1 (p<E-300) were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, majority (18) of these 19 putative validated pleiotropic genes were associated with RA. Conclusion: The metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.

Novel MicroRNAs and Their Functional Targets from Phytophthora infestans and Phytophthora cinnamomi

Background: Even though miRNAs play viral roles in developmental biology by regulating the translation of mRNAs, they are poorly studied in oomycetes, especially in plant pathogen Phytophthora. Objective: The study was aimed to predict and identify the putative miRNAs and their targets in Phytophthora infestans and Phytophthora cinnamomi. Methods: Homology based comparative method was used to identify the unique miRNA sequences in P. infestans and P. cinnamomi with 148,689 EST and TSA sequences of these species. Secondary structure prediction of sRNAs for the 76 resultant sequences has been performed with MFOLD tool and their targets were predicted using psRNAtarget. Result: Novel miRNAs, miR-8210 and miR-4968 were predicted from P. infestans and P. cinnamomi, respectively along with their structural features. The newly identified miRNAs were identified to play important roles in gene regulation, with few of their target genes predicted as transcription factors, tumor suppressor genes, stress responsive genes, DNA repairing genes etc. Conclusion: The miRNAs and their targets identified have opened new interference and editing targets for the development of Phytophthora resistant crop varieties.

Molecular Docking and Interaction Studies of Identified Abscisic Acid Receptors in Oryza sativa: An In-Silico Perspective on Comprehending Stress Tolerance Mechanisms

Background: Abiotic stresses affect plants in several ways and as such, phytohormones such as abscisic acid (ABA) play an important role in conferring tolerance towards these stresses. Hence, to comprehend the role of ABA and its interaction with receptors of the plants, a thorough investigation is essential. Aim: The current study aimed to identify the ABA receptors in Oryza sativa, to find the receptor that binds best with ABA and to examine the mutations present to help predict better binding of the receptors with ABA Methods: Protein sequences of twelve PYL (Pyrabactin resistance 1) and seven PP2C (type 2C protein phosphatase) receptors were retrieved from Rice Annotation Project database and their 3D structures were predicted using RaptorX. Protein-ligand molecular docking studies between PYL and ABA was performed using AutoDock 1.5.6, followed by 100ns molecular dynamic simulation studies using Desmond to determine the acceptable conformational changes after docking via root mean square deviation RMSD plot analysis. Protein-protein docking was then carried out in three sets: PYL-PP2Cs, PYL-ABA-PP2C and PYL(mut)-ABA-PP2C to scrutinize changes in structural conformations and binding energies between complexes. The amino acids of interest were mapped at its respective genomic coordinates using SNP-seek database to ascertain if there were any naturally occurring single nucleotide polymorphisms (SNPs) responsible for triggering rice PYLs mutations Results: Initial protein-ligand docking studies revealed good binding between the complexes, wherein PYL6-ABA complex showed the best energy of -8.15 kcal/mol. The 100ns simulation studies revealed changes in the RMSD values after docking, indicating acceptable conformational changes. Furthermore, mutagenesis study performed at specific PYL-ABA interacting residues followed by downstream PYL(mut)-ABA-PP2C protein-protein docking results after induction of mutations demonstrated a binding energy of -8.17 kcal/mol for PP2C79-PYL11-ABA complex. No naturally occurring SNPs that were responsible for triggering rice PYL mutations were identified when specific amino acid coordinates were mapped at respective genomic coordinates. Conclusion: Thus, the present study provides valuable insights on the interactions of ABA receptors in rice and induced mutations in PYL11 that can enhance the downstream interaction with PP2C

Exploring the Lethality of Human-Adapted Coronavirus through Alignment-Free Machine Learning Approaches Using Genomic Sequences

Background: A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment. Methods: We developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains. Results: The results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge. Conclusion: The results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.

Could Small Neurotoxins-Peptides be Expressed during SARS-CoV-2 Infection?

: SARS-CoV-2 pathogenesis has been recently extended to human central nervous system (CNS), in addition to nasopharyngeal truck, eye, lung and gut. The recent literature highlights that some SARS-CoV-2 spike glycoprotein regions homologous to neurotoxin-like peptides might bind to human nicotinic Acetyl-Choline Receptors (nAChRs). Spike-nAChR interaction can probably cause dysregulation of CNS and cholinergic anti-inflammatory pathways and uncontrolled immune-response, both associated to a severe COVID-19 pathophysiology. Herein, we hypothesize that inside the Open Reading Frame (ORF) region of spike glycoprotein, the RNA polymerase can translate small neurotoxic peptides by means of a “jumping mechanism” already demonstrated in other coronaviruses. These small peptides can bind the snAChRs instead of Spike glycoproteins. A striking homology occurred between these small peptides observed by sequence retrieval and proteins alignment. Acting as nAChRs antagonists, these small peptides (conotoxins) could be the explanation for the extrapulmonary clinical manifestations (neurological, hemorrhagic and thrombotic expressions, the prolonged apnea, the cardiocirculatory collapse, the heart arrhythmias, the ventricular tachycardia, the body temperature alteration, the electrolyte K+ imbalance and finally the significant reduction of butyryl cholinesterase (BuChE) plasma levels, as observed in COVID-19 patients. Several factors might induce the expression of these small peptides, including microbiota. The main hypothesis regarding the presence of these small peptides opens a new scenario on the etiology of COVID-19 clinical symptoms observed so far, including the neurological manifestations.

Challenges That Face Behavioral Genetic Studies: Researchers Perspective from the MENA Region

Background: Behavioral genetic studies are important for the understanding of the contribution of genetic variations to human behavior. However, such studies might be associated with some ethical concerns. Methods: In the current study, ethical challenges related to studies of genetic variations contributing to human behavior were examined among researchers. To achieve the study purpose, the Middle East and North Africa (MENA) region researchers were taken as an example, where the aftermentioned ethical challenges were discussed among a group of researchers, who were the participants of an online forum. Discussions and responses of the participants were monitored and were later qualitatively analyzed. Results: Discussions revealed that several ethical challenges, including subjects’ recruitment, the difficulty of obtaining informed consents, and issues of privacy and confidentiality of obtained data as information leakage, in this case, will lead to social stigma and isolation of the participants and their immediate family members. Jordanian social and cultural norms, faith, and the tribal nature of the population were raised as a major challenge that might face conducting behavioral genetic studies in the Arab populations of the MENA. The lack of regulation related to the conduction of genetic studies, misunderstanding, and misuse of genetic information are other challenges. A full explanation of genetic research and the current and future possible benefits/risks of such research could be potential solutions. Conclusion: In conclusion, the MENA populations are tackled with major challenges in relation to conducting research studies in genetics/antisocial behavior field/s. Establishment of guidelines related to genetic studies, capacity building, increasing public awareness about the importance of genetic testing, and enhancing responsible conduct of research will facilitate the conduct of such sensitive studies in the future in the region.

Identification of Conserved Epitopes in SARS-CoV-2 Spike and Nucleocapsid Protein

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus that first occurred in Wuhan in December 2019. The spike glycoproteins and nucleocapsid proteins are the most common targets for the development of vaccines and antiviral drugs. Objective: We herein analyze the rate of evolution along with the sequences of spike and nucleocapsid proteins in relation to the spatial locations of their epitopes, previously suggested to contribute to the immune response caused by SARS-CoV-2 infections. Methods: We compare homologous proteins of seven human coronaviruses: HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV-2. We then focus on the local, structural order-disorder propensity of the protein regions where the SARS-CoV-2 epitopes are located. Results : We show that most of nucleocapsid protein epitopes overlap the RNA-binding and dimerization domains, and some of them are characterized by a low rate of evolutions. Similarly, spike protein epitopes are preferentially located in regions that are predicted to be ordered and well-conserved, in correspondence of the heptad repeats 1 and 2. Interestingly, both the receptor-binding motif to ACE2 and the fusion peptide of spike protein are characterized by a high rate of evolution. Conclusion: Our results provide evidence for conserved epitopes that might help develop broad-spectrum SARS-CoV-2 vaccines.

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.