PlanTEnrichment: A How-to Guide on Rapid Identification of Transposable Elements Associated with Regions of Interest in Select Plant Genomes.

Transposable elements (TEs) are repeat elements that can relocate or create novel copies of themselves in the genome and contribute to genomic complexity and expansion, via events such as chromosome recombination or regulation of gene expression. However, given the large number of such repeats across the genome, identifying repeats of interest can be a challenge in even well-annotated genomes, especially in more complex, TE-rich plant genomes. Here, we describe a protocol for PlanTEnrichment, a database we created comprising information on 11 plant genomes to analyze stress-associated TEs using publicly available data. By selecting a genome and providing a list of genes or genomic regions whose TE associations the user wants to identify, the user can rapidly obtain TE subfamilies found near the provided regions, as well as their superfamily and class, and the enrichment values of the repeats. The results also provide the locations of individual repeat instances found, alongside the input regions or genes they are associated with, and a bar graph of the top ten most significant repeat subfamilies identified. PlanTEnrichment is freely available at http://tools.ibg.deu.edu.tr/plantenrichment/ and can be used by researchers with rudimentary or no proficiency in computational analysis of TE elements, allowing for expedience in the identification of TEs of interest and helping further our understanding of the potential contributions of TEs in plant genomes.

Physicochemical differences between camelid single-domain antibodies and mammalian antibodies.

Background/aim: In recent years, single-domain antibodies, also known as nanobodies, have emerged as an alternative to full immunoglobulin Gs (IgGs), due to their various advantages, including increased solubility, faster clearance, and cheaper production. Nanobodies are generally derived from the variable domain of the camelid heavy-chain-only immunoglobulin Gs (hcIgGs). Due to the high sequence homology between variable heavy chains of camelids (VHHs) and humans (VHs), hcIgGs are ideal candidates for nanobody development. However, further examination is needed to understand the structural differences between VHs and VHHs. This analysis is essential for nanobody engineering to mitigate potential immunogenicity, while preserving stability, functionality, and antigen specificity. Materials and methods: We obtained the VH and VHH sequences of various camelid and non-camelid mammalian antibodies from public databases and used multiple sequence alignment based on the Chothia numbering scheme. Aligned sequences were subjected to diverse analyses encompassing paratope length, binding prediction, motif, disulfide bridge, salt bridge profiling, and physicochemical characteristic distribution. Logistic Regression coupled with the Boruta - Random Forest algorithm facilitated the comprehensive examination of physicochemical properties. Results: Our findings revealed longer, less variable paratope sequences in VHHs, along with specific antigen binding residues with increased binding potential compared to VHs. Although the VHs showed more heterogeneous noncanonical disulfide bond patterns, the VHHs had a higher number of noncanonical disulfide bridges. Intriguingly, a typical salt bridge between the 94th and 101st positions in the VHs had a very low encounter rate in the VHHs. Surprisingly, we also identified notable differences in the physicochemical patterns of mostly conserved frameworks (FWs), especially the FW2 and FW3 regions, between VHs and VHHs. Conclusion: Our findings point to possible key sites in VHHs as candidate residues for nanobody engineering efforts.

Transcriptome Dynamics of Human Neuronal Differentiation From iPSC.

The generation and use of induced pluripotent stem cells (iPSCs) in order to obtain all differentiated adult cell morphologies without requiring embryonic stem cells is one of the most important discoveries in molecular biology. Among the uses of iPSCs is the generation of neuron cells and organoids to study the biological cues underlying neuronal and brain development, in addition to neurological diseases. These iPSC-derived neuronal differentiation models allow us to examine the gene regulatory factors involved in such processes. Among these regulatory factors are long non-coding RNAs (lncRNAs), genes that are transcribed from the genome and have key biological functions in establishing phenotypes, but are frequently not included in studies focusing on protein coding genes. Here, we provide a comprehensive analysis and overview of the coding and non-coding transcriptome during multiple stages of the iPSC-derived neuronal differentiation process using RNA-seq. We identify previously unannotated lncRNAs via genome-guided de novo transcriptome assembly, and the distinct characteristics of the transcriptome during each stage, including differentially expressed and stage specific genes. We further identify key genes of the human neuronal differentiation network, representing novel candidates likely to have critical roles in neurogenesis using coexpression network analysis. Our findings provide a valuable resource for future studies on neuronal differentiation.

In Silico Identification of Stress-Associated Transposable Elements in Arabidopsis thaliana Using Public Transcriptome Data.

Transposable elements (TEs) have been associated with stress response in many plants, making them a key target of study. However, the high variability, genomic repeat-heavy nature, and widely noncoding character of TEs have made them difficult to study using non-specialized methods, whether experimental or computational. In this chapter, we introduce two computational workflows to analyze transposable elements using publicly available transcriptome data. In the first of these methods, we identify TEs, which show differential expression under salt stress using sample transcriptome libraries that includes noncoding transcripts. In the second, we identify protein-coding genes with differential expression under the same conditions, and determine which TEs are enriched in the promoter regions of these stress-related genes.

Different selection dynamics of S and RdRp between SARS-CoV-2 genomes with and without the dominant mutations.

SARS-CoV-2 is a betacoronavirus responsible for the COVID-19 pandemic that has affected millions of people worldwide. Pharmaceutical research against COVID-19 and the most frequently used tests for SARS-CoV-2 both depend on the genomic and peptide sequences of the virus for their robustness. Therefore, understanding the mutation rates and content of the virus is critical. Two key proteins for SARS-CoV-2 infection and replication are the S protein, responsible for viral entry into the cells, and RdRp, the RNA polymerase responsible for replicating the viral genome. Due to their roles in the viral cycle, these proteins are crucial for the fitness and infectiousness of the virus. Our previous findings had shown that the two most frequently observed mutations in the SARS-CoV-2 genome, 14408C>T in the RdRp coding region, and 23403A>G in the S gene, are correlated with higher mutation density over time. In this study, we further detail the selection dynamics and the mutation rates of SARS-CoV-2 genes, comparing them between isolates carrying both mutations, and isolates carrying neither. We find that the S gene and the RdRp coding region show the highest variance between the genotypes, and their selection dynamics contrast each other over time. The S gene displays higher tolerance for positive selection in mutant isolates early during the appearance of the double mutant genotype, and undergoes increasing negative selection over time, whereas the RdRp region in the mutant isolates shows strong negative selection throughout the pandemic.

Current mutatome of SARS-CoV-2 in Turkey reveals mutations of interest.

As the underlying pathogen for the COVID-19 pandemic that has affected tens of millions of lives worldwide, SARS-CoV-2 and its mutations are among the most urgent research topics worldwide. Mutations in the virus genome can complicate attempts at accurate testing or developing a working treatment for the disease. Furthermore, because the virus uses its own proteins to replicate its genome, rather than host proteins, mutations in the replication proteins can have cascading effects on the mutation load of the virus genome. Due to the global, rapidly developing nature of the COVID-19 pandemic, local demographics of the virus can be difficult to accurately analyze and track, disproportionate to the importance of such information. Here, we analyzed available, high-quality genome data of SARS-CoV-2 isolates from Turkey and identified their mutations, in comparison to the reference genome, to understand how the local mutatome compares to the global genomes. Our results indicate that viral genomes in Turkey has one of the highest mutation loads and certain mutations are remarkably frequent compared to global genomes. We also made the data on Turkey isolates available on an online database to facilitate further research on SARS-CoV-2 mutations in Turkey.

Mutations of SARS-CoV-2 nsp14 exhibit strong association with increased genome-wide mutation load.

SARS-CoV-2 is a betacoronavirus responsible for COVID-19, a pandemic with global impact that first emerged in late 2019. Since then, the viral genome has shown considerable variance as the disease spread across the world, in part due to the zoonotic origins of the virus and the human host adaptation process. As a virus with an RNA genome that codes for its own genomic replication proteins, mutations in these proteins can significantly impact the variance rate of the genome, affecting both the survival and infection rate of the virus, and attempts at combating the disease. In this study, we analyzed the mutation densities of viral isolates carrying frequently observed mutations for four proteins in the RNA synthesis complex over time in comparison to wildtype isolates. Our observations suggest mutations in nsp14, an error-correcting exonuclease protein, have the strongest association with increased mutation load without selective pressure and across the genome, compared to nsp7, nsp8 and nsp12, which form the core polymerase complex. We propose nsp14 as a priority research target for understanding genomic variance rate in SARS-CoV-2 isolates and nsp14 mutations as potential predictors for high mutability strains.

Mutation density changes in SARS-CoV-2 are related to the pandemic stage but to a lesser extent in the dominant strain with mutations in spike and RdRp.

Since its emergence in Wuhan, China in late 2019, the origin and evolution of SARS-CoV-2 have been among the most debated issues related to COVID-19. Throughout its spread around the world, the viral genome continued acquiring new mutations and some of them became widespread. Among them, 14408 C>T and 23403 A>G mutations in RdRp and S, respectively, became dominant in Europe and the US, which led to debates regarding their effects on the mutability and transmissibility of the virus. In this study, we aimed to investigate possible differences between time-dependent variation of mutation densities (MDe) of viral strains that carry these two mutations and those that do not. Our analyses at the genome and gene level led to two important findings: First, time-dependent changes in the average MDe of circulating SARS-CoV-2 genomes showed different characteristics before and after the beginning of April, when daily new case numbers started levelling off. Second, this pattern was much delayed or even non-existent for the "mutant" (MT) strain that harbored both 14408 C>T and 23403 A>G mutations. Although these differences were not limited to a few hotspots, it is intriguing that the MDe increase is most evident in two critical genes, S and Orf1ab, which are also the genes that harbor the defining mutations of the MT genotype. The nature of these unexpected relationships warrants further research.

RdRp mutations are associated with SARS-CoV-2 genome evolution.

COVID-19, caused by the novel SARS-CoV-2 virus, started in China in late 2019, and soon became a global pandemic. With the help of thousands of viral genome sequences that have been accumulating, it has become possible to track the evolution of the viral genome over time as it spread across the world. An important question that still needs to be answered is whether any of the common mutations affect the viral properties, and therefore the disease characteristics. Therefore, we sought to understand the effects of mutations in RNA-dependent RNA polymerase (RdRp), particularly the common 14408C>T mutation, on mutation rate and viral spread. By focusing on mutations in the slowly evolving M or E genes, we aimed to minimize the effects of selective pressure. Our results indicate that 14408C>T mutation increases the mutation rate, while the third-most common RdRp mutation, 15324C>T, has the opposite effect. It is possible that 14408C>T mutation may have contributed to the dominance of its co-mutations in Europe and elsewhere.

Genes associated with T helper 17 cell differentiation and function.

Interleukin-17 (IL-17)-producing T helper cells (Th17 cells) constitute a lineage of CD4 effector T helper cells that is distinct from the Th1 and Th2 CD4 phenotypes. In humans, Th17 differentiation is induced in the presence of the cytokines IL-1 beta, IL-6 and TGF beta, whereas IL-23 maintains Th17 survival. Effector human Th17 cells express several cytokines and cell surface markers, including IL-17A, IL-17F, IL-22, IL-26, CCR6 and TNFalpha. Studies on human cells have revealed that the RORC2 transcription factor plays an effective role in Th17 differentiation. Th17 cells contribute to the host immune response by involving various pathologies, including rheumatoid arthritis, multiple sclerosis and Crohn's disease. However, the full extent of their contribution to diseases is being investigated. The differentiation of Th17 cells is controlled by many transcription factors, including ROR gammat, IRF4, RUNX1, BATF, and STAT3. This review covers the general principles of CD4 T helper differentiation and the known transcription factors that play a role in the recently discovered Th17 cells.