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Objective: To explore different mRNA transcriptome patterns and RNA N6-methyladenosine (m6A) alteration in yunaconitine (YA)-treated HT22 mouse hippocampal neuron, and uncover the role of abnormal mRNA expression and RNA m6A modification in YA-induced neurotoxicity. Methods: HT22 cells were treated with 0, 5, 10, and 50 μM of YA for 72 h to evaluate their viability and GSH content. Subsequently, mRNA-seq and MeRIP-seq analyses were performed on HT22 cells treated with 0 and 10 μM YA for 72 h, and molecular docking was used to simulate interactions between YA and differentially expressed m6A regulators. The mitochondrial membrane potential was examined using the JC-10 probe, and RT-qPCR was conducted to verify the expression levels of differentially expressed m6A regulatory factors, as well as to assess alterations in the mRNA expression levels of antioxidant genes. Results: YA treatment significantly reduced the viability of HT22 cells and decreased GSH content. The mRNA-seq analysis obtained 1018 differentially expressed genes, KEGG and GO enrichment results of differentially expressed genes mainly comprise the nervous system development, cholinergic synapse, response to oxidative stress, and mitochondrial inner membrane. A total of 7 differentially expressed m6A regulators were identified by MeRIP-seq. Notably, molecular docking results suggested a stable interaction between YA and most of the differentially expressed m6A regulators. Conclusion: This study showed that YA-induced HT22 cell damage was associated with the increased methylation modification level of target gene m6A and abnormal expression of m6A regulators.

Introduction: Predicting Self-interacting proteins (SIPs) is a crucial area of research in predicting protein functions, as well as in understanding gene-disease and disease-drug associations. These interactions are integral to numerous cellular processes and play pivotal roles within cells. However, traditional methods for identifying SIPs through biological experiments are often expensive, time-consuming, and have long cycles. Therefore, the development of effective computational methods for accurately predicting SIPs is not only necessary but also presents a significant challenge. Results: In this research, we introduce a novel computational prediction technique, VGGNGLCM, which leverages protein sequence data. This method integrates the VGGNet deep convolutional neural network (VGGN) with the Gray-Level Co-occurrence Matrix (GLCM) to detect Self-interacting proteins associations. Specifically, we initially utilized Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and integrated key features from PSSM using GLCM. We then employed VGGNet as a predictive classifier, leveraging its capabilities for powerful learning and classification prediction. Subsequently, the extracted features were input into the VGGNet deep convolutional neural network to identify Self-interacting proteins. To evaluate the performance of the VGGNGLCM model, we conducted experiments using yeast and human datasets, achieving average accuracies of 95.68% and 97.72% respectively. Additionally, we compared the prediction performance of the VGGNet classifier with that of the Convolutional Neural Network (CNN) and the state-of-the-art Support Vector Machine (SVM) using the same feature extraction method. We also compared the prediction ability of VGGNGLCM with other existing approaches. The comparison results further demonstrate the superior performance of VGGNGLCM over other prediction models in this domain. Conclusion: The experimental verification further strengthens the evidence that VGGNGLCM is effective and robust compared to existing methods. It also highlights the high accuracy and robustness of the VGGNGLCM model in predicting Self-interacting proteins (SIPs). Consequently, we believe that the VGGNGLCM method serves as a valuable computational tool and can catalyze extensive bioinformatics research related to SIPs prediction.

The identification of potential interactions and relationships between diseases and drugs is significant in public health care and drug discovery. As we all know, experimenting to determine the drug-disease interactions is very expensive in both time and money. However, there are still many drug-disease associations that are still undiscovered and potential. Therefore, the development of computational methods to explore the relationship between drugs and diseases is very important and essential. Many computational methods for predicting drug-disease associations have been developed based on known interactions to learn potential interactions of unknown drug-disease pairs. In this paper, we propose 3 new main groups of meta-paths based on the heterogeneous biological network of drug-protein-disease objects. For each meta-path, we design a machine learning model, then an integrated learning method is formed by these models. We evaluated our approach on 3 standard datasets which are DrugBank, OMIM, and Gottlieb’s dataset. Experimental results demonstrate that the proposed method is better than some recent methods such as EMP-SVD, LRSSL, MBiRW, MPG-DDA, SCMFDD,. . . in some measures such as AUC, AUPR, and F1-score.

Background: Age-related Macular Degeneration (AMD) poses a growing global health concern as the leading cause of central vision loss in elderly people. Objection: This study focuses on unraveling the intricate involvement of Natural Killer (NK) cells in AMD, shedding light on their immune responses and cytokine regulatory roles. Methods: Transcriptomic data from the Gene Expression Omnibus database were utilized, employing single-cell RNA-seq analysis. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and single-cell regulatory network inference and clustering (SCENIC) analysis were applied to reveal the regulatory mechanisms of NK cells in early-stage AMD patients. Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD. Results: Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. The integration of hub genes and TFs identified CREM, FOXP1, IRF1, NFKB2 , and USF2 as potential predictors for AMD through machine learning. Conclusion: This comprehensive approach enhances our understanding of NK cell dynamics, signaling alterations, and potential predictive models for AMD. The identified TFs provide new avenues for molecular interventions and highlight the intricate relationship between NK cells and AMD pathogenesis. Overall, this study contributes valuable insights for advancing our understanding and management of AMD.

The recombination plays a key role in promoting evolution of RNA viruses and emergence of potentially epidemic variants. Some studies investigated the recombination occurrence among SARS-CoV-2, without exploring its impact on virus-host interaction. In the aim to investigate the burden of recombination in terms of frequency and distribution, the occurrence of recombination was first explored in 44 230 Omicron sequences among BQ subvariants and the under investigation “ML” (Multiple Lineages) denoted sequences, using 3seq software. Second, the recombination impact on interaction between the Spike protein and ACE2 receptor as well as neutralizing antibodies (nAbs), was analyzed using docking tools. Recombination was detected in 56.91% and 82.20% of BQ and ML strains, respectively. It took place mainly in spike and ORF1a genes. For BQ recombinant strains, the docking analysis showed that the spike interacted strongly with ACE2 and weakly with nAbs. The mutations S373P, S375F and T376A constitute a residue network that enhances the RBD interaction with ACE2. Thirteen mutations in RBD (S373P, S375F, T376A, D405N, R408S, K417N, N440K, S477N, P494S, Q498R, N501Y, and Y505H) and NTD (Y240H) seem to be implicated in immune evasion of recombinants by altering spike interaction with nAbs. In conclusion, this “in silico” study demonstrated that the recombination mechanism is frequent among Omicron BQ and ML variants. It highlights new key mutations, that potentially implicated in enhancement of spike binding to ACE2 (F376A) and escape from nAbs (RBD: F376A, D405N, R408S, N440K, S477N, P494S, and Y505H; NTD: Y240H). Our findings present considerable insights for the elaboration of effective prophylaxis and therapeutic strategies against future SARS-CoV-2 waves.

In metazoans, microRNAs (miRNAs) are essential regulators of gene expression, affecting critical cellular processes from differentiation and proliferation, to homeostasis. During miRNA biogenesis, the miRNA strand that loads onto the RNA-induced Silencing Complex (RISC) can vary, leading to changes in gene targeting and modulation of biological pathways. To investigate the impact of these “arm switching” events on gene regulation, we analyzed a diverse range of tissues and developmental stages in zebrafish by comparing 5p and 3p arms accumulation dynamics between embryonic developmental stages, adult tissues, and sexes. We also compared variable arm usage patterns observed in zebrafish to other vertebrates including arm switching data from fish, birds, and mammals. Our comprehensive analysis revealed that variable arm usage events predominantly take place during embryonic development. It is also noteworthy that isomiR occurrence correlates to changes in arm selection evidencing an important role of microRNA distinct isoforms in reinforcing and modifying gene regulation by promoting dynamics switches on miRNA 5p and 3p arms accumulation. Our results shed new light on the emergence and coordination of gene expression regulation and pave the way for future investigations in this field.

Background: Pseudogenes are sequences that have lost the ability to transcribe RNA molecules or encode truncated but possibly functional proteins. While they were once considered to be meaningless remnants of evolution, recent researches have shown that pseudogenes play important roles in various biological processes. However, the studies of pseudogenes in the silkworm, an important model organism, are limited and have focused on single or only a few specific genes. Objective: To fill these gaps, we present a systematic genome-wide studies of pseudogenes in the silkworm. Methods: We identified the pseudogenes in the silkworm using the silkworm genome assemblies, transcriptome, protein sequences from silkworm and its related species. Then we used transcriptome datasets from 832 RNA-seq analyses to construct spatio-temporal expression profiles for these pseudogenes. Additionally, we identified tissue-specifically expressed and differentially expressed pseudogenes to further understand their characteristics. Finally, the functional roles of pseudogenes as lncRNAs were systematically analyzed. Results: We identified a total of 4410 pseudogenes, which were grouped into 4 groups, including duplications (DUPs), unitary pseudogenes (Unitary), processed pseudogenes (retropseudogenes, RETs), and fragments (FRAGs). The most of pseudogenes in the domestic silkworm were generated before the divergence of wild and domestic silkworm, however, the domestication may also involve in the accumulation of pseudogenes. These pseudogenes were clearly divided into 2 cluster, a highly expressed and a lowly expressed, and the posterior silk gland was the tissue with the most tissue-specific pseudogenes (199), implying these pseudogenes may be involved in the development and function of silkgland. We identified 3299 lncRNAs in these pseudogenes, and the target genes of these lncRNAs in silkworm pseudogenes were enriched in the egg formation and olfactory function. Conclusions: This study replenishes the genome annotations for silkworm, provide valuable insights into the biological roles of pseudogenes. It will also contribute to our understanding of the complex gene regulatory networks in the silkworm and will potentially have implications for other organisms as well.

In diploid organisms, half of the chromosomes in each cell come from the father and half from the mother. Through previous studies, it was found that the paternal chromosome and the maternal chromosome can be regulated and expressed independently, leading to the emergence of allele specific expression (ASE). In this study, we analyzed the differential expression of alleles in the high-altitude population and the normal population based on the RNA sequencing data. Through gene cluster analysis and protein interaction network analysis, we found some changes occurred at the gene level, and some negative effects. During the study, we realized that the calmodulin homology domain may have a certain correlation with long-term survival at high altitude. The plateau environment is characterized by hypoxia, low air pressure, strong ultraviolet radiation, and low temperature. Accordingly, the genetic changes in the process of adaptation are mainly reflected in these characteristics. High altitude generation living is also highly related to cancer, immune disease, cardiovascular disease, neurological disease, endocrine disease, and other diseases. Therefore, the medical system in high altitude areas should pay more attention to these diseases.

Single nucleotide polymorphisms are most common type of genetic variation in human genome. Analyzing genetic variants can help us better understand the genetic basis of diseases and develop predictive models which are useful to identify individuals who are at increased risk for certain diseases. Several SNP analysis tools have already been developed. For running these tools, the user needs to collect data from various databases. Secondly, often researchers have to use multiple variant analysis tools for cross validating their results and increase confidence in their findings. Extracting data from multiple databases and running multiple tools at a time, increases complexity and time required for analysis. There are some web-based tools that integrate multiple genetic variant databases and provide variant annotations for a few tools. These approaches have some limitations such as retrieving annotation information, filtering common pathogenic variants. The proposed web-based tool, namely IPSNP: An Integrated Platform for Predicting Impact of SNPs is written in Django which is a python-based framework. It uses RESTful API of MyVariant.info to extract annotation information of variants associated with a given gene, rsID, HGVS format variants specified in a VCF file for 29 tools. The results are in the form of a CSV file of predictions (1) derived from the consensus decision, (2) a file having annotations for the variants associated with the given gene, (3) a file showing variants declared as pathogenic commonly by the selected tools, and (4) a CSV file containing chromosome coordinates based on GRCh37 and GRCh38 genome assemblies, rsIDs and proteomic data, so that users may use tools of their choice and avoiding manual parameter collection for each tool. IPSNP is a valuable resource for researchers and clinicians and it can help to save time and effort in discovering the novel disease-associated variants and the development of personalized treatments.

Background: Intestinal metaplasia (IM) of gastric epithelium has traditionally been regarded as an irreversible stage in the process of the Correa cascade. Exploring the potential molecular mechanism of IM is significant for effective gastric cancer prevention. Methods: The GSE78523 dataset, obtained from the Gene Expression Omnibus (GEO) database, was analyzed using RStudio software to identify the differently expressed genes (DEGs) between IM tissues and normal gastric epithelial tissues. Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GESA), and protein-protein interaction (PPI) analysis were used to find potential genes. Additionally, the screened genes were analyzed for clinical, immunological, and genetic correlation aspects using single gene clinical correlation analysis (UALCAN), Tumor–Immune System Interactions Database (TISIDB), and validated through western blot experiments. Results: Enrichment analysis showed that the lipid metabolic pathway was significantly associated with IM tissues and the apolipoprotein B ( APOB ) gene was identified in the subsequent analysis. Experiment results and correlation analysis showed that the expression of APOB was higher in IM tissues than in normal tissues. This elevated expression of APOB was also found to be associated with the expression levels of hepatocyte nuclear factor 4A ( HNF4A ) gene. HNF4A was also found to be associated with immune cell infiltration to gastric cancer and was linked to the prognosis of gastric cancer patients. Moreover, HNF4A was also highly expressed in both IM tissues and gastric cancer cells. Conclusion: Our findings indicate that HNF4A regulates the microenvironment of lipid metabolism in IM tissues by targeting APOB . Higher expression of HNF4A tends to lead to a worse prognosis in gastric cancer patients implying it may serve as a predictive indicator for the progression from IM to gastric cancer.

Mycobacterium orygis , a subspecies of the Mycobacterium tuberculosis complex (MTBC), has emerged as a significant concern in the context of One Health, with implications for zoonosis or zooanthroponosis or both. MTBC strains are characterized by the unique insertion element IS 6110 , which is widely used as a diagnostic marker. IS 6110 transposition drives genetic modifications in MTBC, imparting genome plasticity and profound biological consequences. While IS 6110 insertions are customarily found in the MTBC genomes, the evolutionary trajectory of strains seems to correlate with the number of IS 6110 copies, indicating enhanced adaptability with increasing copy numbers. Here, we present a comprehensive analysis of IS 6110 insertions in the M. orygis genome, utilizing ISMapper, and elucidate their genetic consequences in promoting successful host adaptation. Our study encompasses a panel of 67 paired-end reads, comprising 11 isolates from our laboratory and 56 sequences downloaded from public databases. Among these sequences, 91% exhibited high-copy, 4.5% low-copy, and 4.5% lacked IS 6110 insertions. We identified 255 insertion loci, including 141 intragenic and 114 intergenic insertions. Most of these loci were either unique or shared among a limited number of isolates, potentially influencing strain behavior. Furthermore, we conducted gene ontology and pathway analysis, using eggNOG-mapper 5.0, on the protein sequences disrupted by IS 6110 insertions, revealing 63 genes involved in diverse functions of Gene Ontology and 45 genes participating in various KEGG pathways. Our findings offer novel insights into IS 6110 insertions, their preferential insertion regions, and their impact on metabolic processes and pathways, providing valuable knowledge on the genetic changes underpinning IS 6110 transposition in M. orygis .

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), an infectious disease that is a major killer worldwide. Due to selection pressure caused by the use of antibacterial drugs, Mtb is characterised by mutational events that have given rise to multi drug resistant (MDR) and extensively drug resistant (XDR) phenotypes. The rate at which mutations occur is an important factor in the study of molecular evolution, and it helps understand gene evolution. Within the same species, different protein-coding genes evolve at different rates. To estimate the rates of molecular evolution of protein-coding genes, a commonly used parameter is the ratio d N/ d S, where d N is the rate of non-synonymous substitutions and d S is the rate of synonymous substitutions. Here, we determined the estimated rates of molecular evolution of select biological processes and molecular functions across 264 strains of Mtb. We also investigated the molecular evolutionary rates of core genes of Mtb by computing the d N/ d S values, and estimated the pan genome of the 264 strains of Mtb. Our results show that the cellular amino acid metabolic process and the kinase activity function evolve at a significantly higher rate, while the carbohydrate metabolic process evolves at a significantly lower rate for M. tuberculosi s. These high rates of evolution correlate well with Mtb physiology and pathogenicity. We further propose that the core genome of M. tuberculosis likely experiences varying rates of molecular evolution which may drive an interplay between core genome and accessory genome during M. tuberculosis evolution.

Pantoea sp. strain MHSD4 is a bacterial endophyte isolated from the leaves of the medicinal plant Pellaea calomelanos. Here, we report on strain MHSD4 draft whole genome sequence and annotation. The draft genome size of Pantoea sp. strain MHSD4 is 4 647 677 bp with a G+C content of 54.2% and 41 contigs. The National Center for Biotechnology Information Prokaryotic Genome Annotation Pipeline tool predicted a total of 4395 genes inclusive of 4235 protein-coding genes, 87 total RNA genes, 14 non-coding (nc) RNAs and 70 tRNAs, and 73 pseudogenes. Biosynthesis pathways for naphthalene and anthracene degradation were identified. Putative genes involved in bioremediation such as copA, copD, cueO, cueR, glnGm , and trxC were identified. Putative genes involved in copper homeostasis and tolerance were identified which may suggest that Pantoea sp. strain MHSD4 has biotechnological potential for bioremediation of heavy metals.

Genetic variations in the human genome represent the differences in DNA sequence within individuals. This highlights the important role of whole human genome sequencing which has become the keystone for precision medicine and disease prediction. Morocco is an important hub for studying human population migration and mixing history. This study presents the analysis of 3 Moroccan genomes; the variant analysis revealed 6 379 606 single nucleotide variants (SNVs) and 1 050 577 small InDels. Of those identified SNVs, 219 152 were novel, with 1233 occurring in coding regions, and 5580 non-synonymous single nucleotide variants (nsSNP) variants were predicted to affect protein functions. The InDels produced 1055 coding variants and 454 non-3n length variants, and their size ranged from -49 and 49 bp. We further analysed the gene pathways of 8 novel coding variants found in the 3 genomes and revealed 5 genes involved in various diseases and biological pathways. We found that the Moroccan genomes share 92.78% of African ancestry, and 92.86% of Non-Finnish European ancestry, according to the gnomAD database. Then, population structure inference, by admixture analysis and network-based approach, revealed that the studied genomes form a mixed population structure, highlighting the increased genetic diversity in Morocco.

Aims: Autophagy plays a significant role in the development of acute myocardial infarction (AMI), and cardiomyocyte autophagy is of major importance in maintaining cardiac function. We aimed to identify key genes associated with autophagy in AMI through bioinformatics analysis and verify them through clinical validation. Materials and Methods: We downloaded an AMI expression profile dataset GSE166780 from Gene Expression Omnibus (GEO). Autophagy-associated genes potentially differentially expressed in AMI were screened using R software. Then, to identify key autophagy-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) analysis, Receiver Operating Characteristic (ROC) curve analysis, and correlation analysis were performed on the differentially expressed autophagy-related genes in AMI. Finally, we used quantificational real-time polymerase chain reaction (qRT-PCR) to verify the RNA expression of the screened key genes. Results: TSC2, HSPA8, and HIF1A were screened out as key autophagy-related genes. qRT-PCR results showed that the expression levels of HSPA8 and TSC2 in AMI blood samples were lower, while the expression level of HIF1A was higher than that in the healthy controls. Conclusions: TSC2, HSPA8, and HIF1A were identified as key autophagy-related genes in this study. They may influence the development of AMI through autophagy. These findings may help deepen our understanding of AMI and may be useful for the treatment of AMI.

Background: G-quadruplexes (G4s) are secondary structures in DNA and RNA that impact various cellular processes, such as transcription, splicing, and translation. Due to their numerous functions, G4s are involved in many diseases, making their study important. Yet, G4s evolution remains largely unknown, due to their low sequence similarity and the poor quality of their sequence alignments across several species. To address this, we designed a strategy that avoids direct G4s alignment to study G4s evolution in the 3 species kingdoms. We also explored the coevolution between RBPs and G4s. Methods: We retrieved one-to-one orthologous genes from the Ensembl Compara database and computed groups of one-to-one orthologous genes. For each group, we aligned gene sequences and identified G4 families as groups of overlapping G4s in the alignment. We analyzed these G4 families using Count, a tool to infer feature evolution into a gene or a species tree. Additionally, we utilized these G4 families to predict G4s by homology. To establish a control dataset, we performed mono-, di- and tri-nucleotide shuffling. Results: Only a few conserved G4s occur among all living kingdoms. In eukaryotes, G4s exhibit slight conservation among vertebrates, and few are conserved between plants. In archaea and bacteria, at most, only 2 G4s are common. The G4 homology-based prediction increases the number of conserved G4s in common ancestors. The coevolution between RNA-binding proteins and G4s was investigated and revealed a modest impact of RNA-binding proteins evolution on G4 evolution. However, the details of this relationship remain unclear. Conclusion: Even if G4 evolution still eludes us, the present study provides key information to compute groups of homologous G4 and to reveal the evolution history of G4 families.

The overexpression of the Epidermal Growth Factor Receptor (EGFR) marks it as a pivotal target in cancer treatment, with the aim of reducing its proliferation and inducing apoptosis. This study aimed at the CADD of a new apoptotic EGFR inhibitor. The natural alkaloid, theobromine, was used as a starting point to obtain a new semisynthetic (di-ortho-chloro acetamide) derivative ( T-1-DOCA ). Firstly, T-1-DOCA ’s total electron density, energy gap, reactivity indices, and electrostatic surface potential were determined by DFT calculations, Then, molecular docking studies were carried out to predict the potential of T-1-DOCA against wild and mutant EGFR proteins. T-1-DOCA ’s correct binding was further confirmed by molecular dynamics (MD) over 100 ns, MM-GPSA, and PLIP experiments. In vitro, T-1-DOCA showed noticeable efficacy compared to erlotinib by suppressing EGFR WT and EGFR T790M with IC 50 values of 56.94 and 269.01 nM, respectively. T-1-DOCA inhibited also the proliferation of H1975 and HCT-116 malignant cell lines, exhibiting IC 50 values of 14.12 and 23.39 µM, with selectivity indices of 6.8 and 4.1, respectively, indicating its anticancer potential and general safety. The apoptotic effects of T-1-DOCA were indicated by flow cytometric analysis and were further confirmed through its potential to increase the levels of BAX, Casp3, and Casp9, and decrease Bcl-2 levels. In conclusion, T-1-DOCA , a new apoptotic EGFR inhibitor, was designed and evaluated both computationally and experimentally. The results suggest that T-1-DOCA is a promising candidate for further development as an anti-cancer drug.

Nociception and pain sensation are important neural processes in humans to avoid injury. Many proteins are involved in nociception and pain sensation in humans; however, the evolution of these proteins in animals is unknown. Here, we chose nociception- and pain-related proteins, including G protein-coupled receptors (GPCRs), ion channels (ICs), and neuropeptides (NPs), which are reportedly associated with nociception and pain in humans, and identified their homologs in various animals by BLAST, phylogenetic analysis and protein architecture comparison to reveal their evolution from protozoans to humans. We found that the homologs of transient receptor potential channel A 1 (TRPA1), TRAPM, acid-sensing IC (ASIC), and voltage-dependent calcium channel (VDCC) first appear in Porifera. Substance-P receptor 1 (TACR1) emerged from Coelenterata. Somatostatin receptor type 2 (SSTR2), TRPV1 and voltage-dependent sodium channels (VDSC) appear in Platyhelminthes. Calcitonin gene-related peptide receptor (CGRPR) was first identified in Nematoda. However, opioid receptors (OPRs) and most NPs were discovered only in vertebrates and exist from agnatha to humans. The results demonstrated that homologs of nociception and pain-related ICs exist from lower animal phyla to high animal phyla, and that most of the GPCRs originate from low to high phyla sequentially, whereas OPRs and NPs are newly evolved in vertebrates, which provides hints of the evolution of nociception and pain-related proteins in animals and humans.

Bluetongue virus (BTV) is an arbovirus considered as a major threat for the global livestock economy. Since 1999, Tunisia has experienced several incursions of BTV, during which numerous cases of infection and mortality have been reported. However, the geographical origin and epidemiological characteristics of these incursions remained unclear. To understand the evolutionary history of BTV emergence in Tunisia, we extracted from Genbank the segment 6 sequences of 7 BTV strains isolated in Tunisia during the period 2000 to 2017 and blasted them to obtain a final dataset of 67 sequences. We subjected the dataset to a Bayesian phylogeography framework inferring geographical origin and serotype as phylodynamic models. Our results suggest that BTV-2 was first introduced in Tunisia in the 1960s and that since 1990s, the country has witnessed the emergence of other typical and atypical BTV serotypes notably BTV-1, BTV-3 and BTV-Y. The reported serotypes have a diverse geographical origin and have been transmitted to Tunisia from countries in the Mediterranean Basin. Interserotype reassortments have been identified among BTV-1, BTV-2 and BTV-Y. This study has provided new insights on the temporal and geographical origin of BTV in Tunisia, suggesting the contribution of animal trade and environment conditions in virus spread.

Objective: The Fas-activated serine/threonine kinase (FASTK) family of proteins has been recently found to be able to regulate mitochondrial gene expression post-transcriptionally. Nonetheless, there is a paucity of study about the role of the FASTK family in kidney renal clear cell carcinoma (KIRC). This study was conducted to explore the correlation of FASTK family genes with expression, prognosis, and immune infiltration in KIRC. Methods: We collected the data from the UALCAN, GeneMANIA, STRING, CancerSEA, cBioPortal, Kaplan-Meier plotter, GEPIA, TISIDB and TIMER databases to evaluate the genetic alterations, differential expression, prognostic significance, and immune cell infiltration of FASTKs in patients with KIRC. Results: In tumor tissues of KIRC, the mRNA expression level of FASTK and TBRG4 was elevated, whereas that of FASTKD1, FASTKD2, and FASTKD5 was lowered compared with normal tissues ( P

Rooting and root development in Acer rubrum have important effects on overall growth. A. rubrum does not take root easily in natural conditions. In this study, the mechanisms of the miR396b- GRF1 module underlying rooting regulation in A. rubrum were studied. The subcellular localization and transcriptional activation of miR396b and its target gene growth regulating factor 1 ( GRF1 ) were investigated. These experiments showed that GRF1 was localized in the nucleus and had transcriptional activation activity. Functional validation experiments in transgenic plants demonstrated that overexpression of Ar- miR396b inhibited adventitious root growth, whereas overexpression of ArGRF1 increased adventitious root growth. These results help clarify the molecular regulatory mechanisms underlying adventitious root growth in A. rubrum and provide some new insights into the rooting rate in this species.

Pearl millet (Pennisetum glaucum (L.)) is a remarkable cereal crop known for its ability to thrive in challenging environmental conditions. Despite its resilience, the intricate molecular mechanisms behind its toughness remain a mystery. To address this knowledge gap, we conducted advanced next-generation RNA sequencing. This approach allowed us to compare the gene expression profiles of pearl millet seedlings exposed to heat stress with those grown under standard conditions. Our main focus was on the shoots of 13-day-old pearl millet plants, which we subjected to a brief heat stress episode at 50°C for 60 seconds. Within the vast genomic landscape comprising 36 041 genes, we successfully identified a set of 10 genes that exhibited significant fold changes, ranging from 11 to 14-fold compared to the control conditions. These 10 genes were previously unknown to have such substantial changes in expression compared to the control. To uncover the functional significance hidden within these transcriptomic findings, we utilized computational tools such as MEME, String, and phylogenetic tree analysis. These efforts collectively revealed conserved domains within the transcriptomic landscape, hinting at potential functions associated with these genetic sequences. Of particular note, the distinct transcriptomic patterns specific to pearl millet leaves under thermal stress shed light on intricate connections to fundamental biological processes. These processes included the Ethylene-activated signaling pathway, Regulation of intracellular signal transduction, Negative regulation of signal transduction, Protein autophosphorylation, and Intracellular signal transduction. Together, these processes provide insight into the molecular strategies employed by pearl millet to overcome thermal stress challenges. By integrating cutting-edge RNA sequencing techniques and computational analyses, we have embarked on unraveling the genetic components and pathways that empower pearl millet’s resilience in the face of adversity. This newfound understanding has the potential to not only advance our knowledge of plant stress responses but also contribute to enhancing crop resilience in challenging environmental conditions.

Ochnaceae DC. includes more than 600 species that exhibit potential values for environmental ecology, ornamental, pharmaceutical, and timber industries. Although studies on phylogeny and phytochemicals have been intensively conducted, chloroplast genome data of Ochnaceae species have not been fully explored. In this study, the next-generation sequencing method was used to sequence the chloroplast genomes of Ochna integerrima and Ochna serrulata which were 157 329 and 157 835 bp in length, respectively. These chloroplast genomes had a quadripartite structure and contained 78 protein-coding genes, 30 tRNAs, and 4 rRNAs. Comparative analysis revealed 8 hypervariable regions, including trnK_UUU-trnQ_UUG, rpoB-psbM, trnS_GGA-rps4, accD-psaI, rpl33-rps18, rpl14-rpl16, ndhF-trnL_UAG , and rps15-ycf1 among 6 Ochnaceae taxa. Additionally, there were shared and unique repeats among 6 examined chloroplast genomes. The notable changes were the loss of rpl32 in Ochna species and the deletion of rps16 exon 2 in O. integerrima compared to other taxa. This study is the first comprehensive comparative genomic analysis of complete chloroplast genomes of Ochna species and related taxa in Ochnaceae. Consequently, the current study provides initial results for further research on genomic evolution, population genetics, and developing molecular markers in Ochnaceae and related taxa.

MicroRNAs (miRNAs) are a class of non-coding RNAs that act as regulators of disease. An evolutionary approach to the disease could reveal factors such as diagnosis, treatment, and prognosis prediction. The expression patterns of transposable element (TEs)-derived miRNAs could help elucidate diseases, and their evolutionary patterns are also valuable. The 34 miRNAs were compared in terms of stage survival and tumor status in 33 carcinomas from TCGA. Expression levels were compared using a t -test and presented as differentially expressed miRNAs (DEMs). For DEMs showing statistically specific expression patterns for 3 conditions (normal and cancer, early and advanced stage, and survival), interactions with related genes in 10 species, including humans, were compared. The enrichment term was discovered for the gene-miRNA interactions. In 18 out of the 33 carcinomas, at least one miRNA was retrieved with P .05. A total of 128 DEMs for the 9 miRNAs were identified. Based on the TargetScan database, interactions between miRNAs and genes in 10 species, including humans, were confirmed. The evolutionarily conserved miR-130a was observed in all 10 species, whereas miR-151a was only observed in humans. GO terms of related genes were selected for the miRNAs commonly found in each species. Evolutionary analysis of TE-derived disease-associated miRNAs was performed, and the evolutionarily conserved miR-130a-related carcinomas included renal and thyroid cancers. Human and rhesus monkey-specific miR-625 is associated with various carcinomas.

Corynebacterium striatum is a Gram-positive bacterium that is straight or slightly curved and non-spore-forming. Although it was originally believed to be a part of the normal microbiome of human skin, a growing number of studies have identified it as a cause of various chronic diseases, bacteremia, and respiratory infections. However, despite its increasing importance as a pathogen, the genetic characteristics of the pathogen population, such as genomic characteristics and differences, the types of resistance genes and virulence factors carried by the pathogen and their distribution in the population are poorly understood. To address these knowledge gaps, we conducted a pan-genomic analysis of 314 strains of C. striatum isolated from various tissues and geographic locations. Our analysis revealed that C. striatum has an open pan-genome, comprising 5692 gene families, including 1845 core gene families, 2362 accessory gene families, and 1485 unique gene families. We also found that C. striatum exhibits a high degree of diversity across different sources, but strains isolated from skin tissue are more conserved. Furthermore, we identified 53 drug resistance genes and 42 virulence factors by comparing the strains to the drug resistance gene database (CARD) and the pathogen virulence factor database (VFDB), respectively. We found that these genes and factors are widely distributed among C. striatum , with 77.7% of strains carrying 2 or more resistance genes and displaying primary resistance to aminoglycosides, tetracyclines, lincomycin, macrolides, and streptomycin. The virulence factors are primarily associated with pathogen survival within the host, iron uptake, pili, and early biofilm formation. In summary, our study provides insights into the population diversity, resistance genes, and virulence factors of C. striatum from different sources. Our findings could inform future research and clinical practices in the diagnosis, prevention, and treatment of C. striatum -associated diseases.

SARS-CoV-2 has been highly susceptible to mutations since its emergence in Wuhan, China, and its subsequent propagation due to containing an RNA as its genome. The emergence of variants with improved transmissibility still poses a grave threat to global health. The spike protein mutation is mainly responsible for higher transmissibility and risk severity. This study retrieved SARS-CoV-2 variants structural and nonstructural proteins (NSPs) sequences from several geographic locations, including Africa, Asia, Europe, Oceania, and North and South America. First, multiple sequence alignments with BioEdit and protein homology modeling were performed using the SWISS Model. Then the structure visualization and structural analysis were performed by superimposing against the Wuhan sequence by Pymol to retrieve the RMSD values. Sequence alignment revealed familiar, uncommon regional among variants and, interestingly, a few unique mutations in Beta, Delta, and Omicron. Structural analysis of such unique mutations revealed that they caused structural deviations in Beta, Delta, and Omicron spike proteins. In addition, these variants were more severe in terms of hospitalization, sickness, and higher mortality, which have a substantial relationship with the structural deviations because of those unique mutations. Such evidence provides insight into the SARS-CoV-2 spike protein vulnerability toward mutation and their structural and functional deviations, particularly in Beta, Delta, and Omicron, which might be the cause of their broader coverage. This knowledge can help us with regional vaccine strain selection, virus pathogenicity testing, diagnosis, and treatment with more specific vaccines.

Based on the important role of antibiotic treatment in the research of the interaction between Wolbachia and insect hosts, this study aimed to identify the most suitable antibiotic and concentration for Wolbachia elimination in the P. xylostella , and to investigate the effect of Wolbachia and antibiotic treatment on the bacterial community of P. xylostella . Our results showed that the Wolbachia -infected strain was plutWB1 of supergroup B in the P. xylostella population collected in Nepal in this study; 1 mg/mL rifampicin could remove Wolbachia infection in P. xylostella after 1 generation of feeding treatment and the toxic effect was relatively low; among the 29 samples of adult P. xylostella in our study (10 WU samples, 10 WA samples, and 9 WI samples), 52.5% of the sequences were of Firmicutes and 47.5% were of Proteobacteria, with the dominant genera being mainly Carnobacterium (46.2%), Enterobacter (10.1%), and Enterococcus (6.2%); Moreover, antibiotic removal of Wolbachia infection in P. xylostella and transfer to normal conditions for 10 generations no longer significantly affected the bacterial community of P. xylostella . This study provides a theoretical basis for the elimination method of Wolbachia in the P. xylostella , as well as a reference for the elimination method of Wolbachia in other Wolbachia -infected insect species, and a basis for the study of the extent and duration of the effect of antibiotic treatment on the bacterial community of the P. xylostella .

Autosomal dominant hyper-IgE syndrome (AD-HIES) is linked to dominant negative mutations of the STAT3 protein whose molecular basis for dysfunction is unclear and presenting with a variety of clinical manifestations with only supportive treatment. To establish the relationship between the impact of STAT3 mutations in different domains and the severity of the clinical manifestations, 105 STAT3 mutations were analyzed for their impact on protein stability, flexibility, function, and binding affinity using in Silico approaches. Our results showed that 73% of the studied mutations have an impact on the physicochemical properties of the protein, altering the stability, flexibility and function to varying degrees. In particular, mutations affecting the DNA binding domain (DBD) and the Src Homology 2 (SH2) have a significant impact on the protein structure and disrupt its interaction either with DNA or other STAT3 to form a heterodomain complex, leading to severe clinical phenotypes. Collectively, this study suggests that there is a close relationship between the domain involving the mutation, the degree of variation in the properties of the protein and the degree of loss of function ranging from partial loss to complete loss, explaining the variability of clinical manifestations between mild and severe.

Many viral diseases exhibit seasonal behavior and can be affected by environmental stressors. Using time-series correlation charts extrapolated from worldwide data, we provide strong support for the seasonal development of COVID-19 regardless of the immunity of the population, behavioral changes, and the periodic appearance of new variants with higher rates of infectivity and transmissibility. Statistically significant latitudinal gradients were also observed with indicators of global change. Using the Environmental Protection Index (EPI) and State of Global Air (SoGA) metrics, a bilateral analysis of environmental health and ecosystem vitality effects showed associations with COVID-19 transmission. Air quality, pollution emissions, and other indicators showed strong correlations with COVID-19 incidence and mortality. Remarkably, EPI category and performance indicators also correlated with latitude, suggesting cultural and psychological diversity in human populations not only impact wealth and happiness but also planetary health at latitudinal level. Looking forward, we conclude there will be a need to disentangle the seasonal and global change effects of COVID-19 noting that countries that go against the health of the planet affect health in general.

Background: A worldwide outbreak of coronavirus disease 2019 (COVID-19) has resulted in millions of deaths. Ferroptosis is a form of iron-dependent cell death which is characterized by accumulation of lipid peroxides on cellular membranes, and is related with many physiological and pathophysiological processes of diseases such as cancer, inflammation and infection. However, the role of ferroptosis in COVID-19 has few been studied. Material and Method: Based on the RNA-seq data of 100 COVID-19 cases and 26 Non-COVID-19 cases from GSE157103, we identified ferroptosis related differentially expressed genes (FRDEGs, adj. P -value

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver. Although the RNA modification N6-methyladenine (m6A) has been reported to be involved in HCC carcinogenesis, early diagnostic markers and promising personalized therapeutic targets are still lacking. In this study, we identified that 19 m6A regulators and 34 co-expressed lncRNAs were significantly upregulated in HCC samples; based on these factors, we established a prognostic signal of HCC associated with 9 lncRNAs and 19 m6A regulators using LASSO Cox regression analysis. Kaplan-Meier survival estimate revealed correlations between the risk scores and patients’ OS in the training and validation dataset. The ROC curve demonstrated that the risk score-based curve has satisfactory prediction efficiency for both training and validation datasets. Multivariate Cox’s proportional hazard regression analysis indicated that the risk score was an independent risk factor within the training and validation dataset. In addition, the risk score could distinguish HCC patients from normal non-cancerous samples and HCC samples of different pathological grades. Eventually, 232 mRNAs were co-expressed with these 9 lncRNAs according to GSE101685 and GSE112790; these mRNAs were enriched in cell cycle and cell metabolic activities, drug metabolism, liver disease-related pathways, and some important cancer related pathways such as p53, MAPK, Wnt, RAS and so forth. The expression of the 9 lncRNAs was significantly higher in HCC samples than that in the neighboring non-cancerous samples. Altogether, by using the Consensus Clustering, PCA, ESTIMATE algorithm, LASSO regression model, Kaplan-Meier survival assessment, ROC curve analysis, and multivariate Cox’s proportional hazard regression model analysis, we established a prognostic marker consisting of 9 m6A regulator-related lncRNAs that markers may have prognostic and diagnostic potential for HCC.

B12D-Like is a member of the B12D domain-containing protein family, which includes several transmembrane proteins in plants. In this study, the cDNA of PgB12D-Like from Pennisetum glaucum subsp. monodii (Maire) Brunken was sequenced and characterized. The 446-bp cDNA for PgB12D-Like encodes for a deduced protein of 95 amino acids. The PgB12D-Like protein contains a B12D domain and a transmembrane helix embedded in the mitochondrial membrane. Cis-regulatory elements analysis reveals binding sites for various transcription factors involved in responses to stress, light, and plant hormones in the putative promoter sequence for PgB12D-Like . Several proteins involved in floral organ development were also found to have binding sites in the PgB12D-Like promoter, such as agamous-like proteins and squamosa promoter binding proteins. Real-time PCR reveals high expression of PgB12D-Like in flowers during heading, whereas its expression in a 4-day-old seedling shoot was the lowest. Moreover, cold, drought, and heat stress were found to upregulate PgB12D-Like , whereas gibberellic acid downregulated its expression in seedlings. The present study helps to uncover the function of the B12D-Like in response to plant hormones and abiotic stress during P. glaucum development.

Lineage-specific genes can contribute to the emergence and evolution of novel traits and adaptations. Tardigrades are animals that have adapted to tolerate extreme conditions by undergoing a form of cryptobiosis called anhydrobiosis, a physical transformation to an inactive desiccated state. While studies to understand the genetics underlying the interspecies diversity in anhydrobiotic transitions have identified tardigrade-specific genes and family expansions involved in this process, the contributions of species-specific genes to the variation in tardigrade development and cryptobiosis are less clear. We used previously published transcriptomes throughout development and anhydrobiosis (5 embryonic stages, 7 juvenile stages, active adults, and tun adults) to assess the transcriptional biases of different classes of genes between 2 tardigrade species, Hypsibius exemplaris and Ramazzottius varieornatus . We also used the transcriptomes of 2 other tardigrades, Echiniscoides sigismundi and Richtersius coronifer , and data from 3 non-tardigrade species ( Adenita vaga , Drosophila melanogaster , and Caenorhabditis elegans ) to help identify lineage-specific genes. We found that lineage-specific genes have generally low and narrow expression but are enriched among biased genes in different stages of development depending on the species. Biased genes tend to be specific to early and late development, but there is little overlap in functional enrichment of biased genes between species. Gene expansions in the 2 tardigrades also involve families with different functions despite homologous genes being expressed during anhydrobiosis in both species. Our results demonstrate the interspecific variation in transcriptional contributions and biases of lineage-specific genes during development and anhydrobiosis in 2 tardigrades.

It is postulated that the human digestive tract (DT) from mouth to intestine is differentiated into diverse niches. For example, Segata et al. discovered that the microbiomes of diverse habitats along the DT could be distinguished as 4 types (niches) including (i) stool; (ii) sub-gingival plaques (SubP) and supra-gingival plaques (SupP); (iii) tongue dorsum (TD), throat (TH), palatine tonsils (PT), and saliva (Sal); and (iv) hard palate (HP) and buccal mucosa (BM), and keratinized gingiva (KG). These niches are different not only in composition, but also in metabolic potentials. In a previous study, we applied Harris et al’s multi-site neutral and Tang and Zhou’s niche-neutral hybrid models to characterize the DT niches discovered by Segata et al. Here, we complement the previous study by applying Sloan’s near-neural model and Ning et al’s stochasticity analysis framework to quantify the niche-neutral continuum of the DT microbiome distribution to shed light on the possible ecological/evolutionary mechanism that shapes the continuum. Overall but excluding the stool site, the proportion of neutral OTUs (46%) is slightly higher than that of the positive selection (38%), but significantly higher than negative selection (15%). The gut (stool) exhibited 3 to 12 times lower neutrality than other DT sites. The analysis also cross-verified our previous hypothesis that the KG ( keratinized gingiva ) is of distinct assembly dynamics in the DT microbiome, should be treated as a fifth niche. Our findings offer new insight on the long-standing debate concerning whether a minimum of 2-mm of KG width is necessary for marginal periodontal health.

Vibrio vulnificus is an emergent marine pathogen and is the cause of a deadly septicemia. However, the evolution mechanism of antibiotic-resistant genes (ARGs) is still unclear. Twenty-two high-quality complete genomes of V. vulnificus were obtained and grouped into 16 clinical isolates and 6 environmental isolates. Genomic annotations found 23 ARG orthologous genes, among which 14 ARGs were shared by V. vulnificus and other Vibrio members. Furthermore, those ARGs were located in their chromosomes, rather than in the plasmids. Phylogenomic reconstruction based on single-copy orthologous protein sequences and ARG protein sequences revealed that clinical and environmental V. vulnificus isolates were in a scattered distribution. The calculation of non-synonymous and synonymous substitutions indicated that most of ARGs evolved under purifying selection with the Ka / Ks ratios lower than one, while h-ns, rsmA , and soxR in several clinical isolates evolved under the positive selection with Ka / Ks ratios >1. Our result indicated that V. vulnificus antibiotic-resistant armory was not only confined to clinical isolates, but to environmental ones as well and clinical isolates inclined to accumulate beneficial non-synonymous substitutions that could be retained to improve competitiveness.

Background: Osteosarcoma (OS) is the most common malignant bone tumor in clinical practice, and currently, the ability to predict prognosis in the diagnosis of OS is limited. There is an urgent need to find new diagnostic methods and treatment strategies for OS. Material and methods: We downloaded the multi-omics data for OS from the TARGET database. Prognosis-associated methylation sites were used to identify clustered subtypes of OS, and OS was classified into 3 subtypes (C1, C2, C3). Survival analysis showed significant differences between the C3 subtype and the other subtypes. Subsequently, differentially expressed genes (DEGs) across subtypes were screened and subjected to pathway enrichment analysis. Results: A total of 249 DEGs were screened from C3 subtype to other subtypes. Metabolic pathway enrichment analysis showed that DEGs were significantly enriched to the hypoxic pathway. Based on univariate and multivariate COX regression analysis, 12 genes from the hypoxia pathway were further screened and used to construct hypoxia-related prognostic model (HRPM). External validation of the HRPM was performed on the GSE21257 dataset. Finally, differences in survival and immune infiltration between high and low risk score groups were compared. Conclusion: In summary, we proposed a hypoxia-associated risk model based on a 12-gene expression signature, which is potentially valuable for prognostic diagnosis of patients with OS.

Background: Zika virus, which is widely spread and infects humans through the bites of Aedes albopictus and Aedes aegypti female mosquitoes, represents a serious global health issue. Objective: The objective of the present study is to computationally characterize Zika virus polyproteins (UniProt Name: PRO_0000443018 [residues 1-3423], PRO_0000445659 [residues 1-3423] and PRO_0000435828 [residues 1-3419]) and their envelope proteins using their physico-chemical properties. Methods: To achieve this, the Polarity Index Method (PIM) profile and the Protein Intrinsic Disorder Predisposition (PIDP) profile of 3 main groups of proteins were evaluated: structural proteins extracted from specific Databases, Zika virus polyproteins, and their envelope proteins (E) extracted from UniProt Database. Once the PIM profile of the Zika virus envelope proteins (E) was obtained and since the Zika virus polyproteins were also identified with this profile, the proteins defined as “reviewed proteins” extracted from the UniProt Database were searched for the similar PIM profile. Finally, the difference between the PIM profiles of the Zika virus polyproteins and their envelope proteins (E) was tested using 2 non-parametric statistical tests. Results: It was found and tested that the PIM profile is an efficient discriminant that allows obtaining a “computational fingerprint” of each Zika virus polyprotein from its envelope protein (E). Conclusion: PIM profile represents a computational tool, which can be used to effectively discover Zika virus polyproteins from Databases, from their envelope proteins (E) sequences.

Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference. Methods: We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off. Results: Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Polar fishes have evolved antifreeze proteins (AFPs) that allow them to survive in subzero temperatures. We performed deep transcriptomic sequencing on a postlarval/juvenile variegated snailfish, Liparis gibbus (Actinopterygii: Scorpaeniformes: Cottoidei: Liparidae), living in an iceberg habitat (−2°C) in Eastern Greenland and report detection of highly expressed transcripts that code for putative AFPs from 2 gene families, Type I and LS-12-like proteins (putative Type IV AFPs). The transcripts encoding both proteins have expression levels among the top

Neuroblastoma (NB) is the most common solid malignancy in children. MYCN gene amplification is the most relevant genetic alteration in patients with NB and is associated with poor prognosis. Autophagy plays specific roles in the occurrence, development, and progression of NB. Here, we aimed to identify and assess the prognostic effects of autophagy-related genes (ARGs) in patients with NB and MYCN gene amplification. Differentially expressed ARGs were identified in patients with NB with and without MYCN gene amplification, and the ARG expression patterns and related clinical data from the Therapeutically Applicable Research to Generate Effective Treatments database were used as the training cohort. Least absolute shrinkage and selection operator analyses were used to identify prognostic ARGs associated with event-free survival (EFS), and a prognostic risk score model was developed. Model performance was assessed using the Kaplan–Meier method and receiver operating characteristic (ROC) curves. The prognostic ARG mode l was verified using the validation cohort dataset, GSE49710. Finally, a nomogram was constructed by combining the ARGbased risk score with clinicopathological factors. Three ARGs (GABARAPL1, NBR1, and PINK1) were selected to build a prognostic risk score model. The EFS in the low-risk group was significantly better than that in the high-risk group in both the training and validation cohorts. A nomogram incorporating the prognostic risk score, age, and International Neuroblastoma Staging System stage showed a favorable predictive ability for EFS rates according to the area under the ROC curve at 3 years (AUC = 0.787) and 5 years (AUC = 0.787). The nomogram demonstrated good discrimination and calibration. Our risk score model for the 3 ARGs can be used as an independent prognostic factor in patients with NB and MYCN gene amplification. The model can accurately predict the 3- and 5-year survival rates.

Motivation: The advancement of high-throughput PPI profiling techniques results in generating a large amount of PPI data. The alignment of the PPI networks uncovers the relationship between the species that can help understand the biological systems. The comparative study reveals the conserved biological interactions of the proteins across the species. It can also help study the biological pathways and signal networks of the cells. Although several network alignment algorithms are developed to study and compare the PPI data, the development of the aligner that aligns the PPI networks with high biological similarity and coverage is still challenging. Results: This paper presents a novel global network alignment algorithm, BioAlign, that incorporates a significant amount of biological information. Existing studies use global sequence and/or 3D-structure similarity to align the PPI networks. In contrast, BioAlign uses the local sequence similarity, predicted secondary structure motifs, and remote homology in addition to global sequence and 3D-structure similarity. The extra sources of biological information help BioAlign to align the proteins with high biological similarity. BioAlign produces significantly better results in terms of AFS and Coverage (6-32 and 7-34 with respect to MF and BP, respectively) than the existing algorithms. BioAlign aligns a much larger number of proteins that have high biological similarities as compared to the existing aligners. BioAlign helps in studying the functionally similar protein pairs across the species.

Objective: To construct a competitive endogenous RNA (ceRNA) regulatory network derived from exosomes of human breast cancer (BC) by using the exoRbase database, to explore the possible pathogenesis of BC, and to develop new targets for future diagnosis and treatment. Methods: The exosomal gene sequencing data of BC patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were analyzed by using R language. Use Targetscan and miRanda database to jointly predict and differentially express miRNA (microRNA), miRNA combined with mRNA. The miRcode database was used to predict the miRNA combined with differentially expressed lncRNA, and the starBase database was used to predict the miRNA combined with circRNA in the difference table. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Enrichment analysis and visualization of KEGG were carried out using KOBAS. Hub gene was determined by Cytohubba plug-in. Results: Forty-two differentially expressed mRNA, 43 differentially expressed circRNA, and 26 differentially expressed lncRNA were screened out. The ceRNA network was constructed by using Cytoscape software, including 19 mRNA nodes, 2 lncRNA nodes, 8 circRNA nodes, and 41 miRNA nodes. KEGG enrichment analysis showed that differentially expressed mRNA in the regulatory network mainly enriched the p53 signaling pathway. Find the key Hub gene PTEN. Conclusion: The ceRNA regulatory network in blood exosomes of BC patients has been successfully constructed in this study, which provides an exact target for the diagnosis and treatment of BC.

The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.

Background: Intrauterine growth retardation (IUGR) affects approximately 10% to 15% of all pregnancies worldwide. IUGR is not only associated with stillbirth and newborn death, but also the delay of cognition in childhood and the promotion of metabolic and vascular disorders in adulthood. Figuring out the mechanism of IUGR is rather meaningful and valuable. Methods: Datasets related to IUGR were searched in the Gene Expression Omnibus website. Principal component analysis (PCA) was used for normalization. Differential expressed genes (DEGs) were screened out using the ggpot2 tool. DEGs were used to conduct Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses, and protein-protein interaction (PPI) analysis. IUGR related genes were searched in the OMIM website to look for the intersection with the DEGs. The DEGs were analyzed for tissue-specific expression by the online resource BioGPS. The results were displayed through volcano map, Venn map, box plot, heat map, and GSEA enrichment plots drawn by R language packages. Results: Eleven DEGs were screened out of 2 datasets. One hundred ninety-five genes related to IUGR in OMIM were retrieved. EGR2 was the only intersection gene that was found in both groups. Genes associated with placental tissue expression include COL17A1, HSD11B1, and LGALS14. Molecular functions of the DEGs are related to the oxidoreductase activity. The following 4 signaling pathways, reactome signaling by interleukins, reactome collagen degradation, Naba secreted factors, and PID NFAT tfpathway, were enriched by GSEA. Two critical modules comprising 5 up-regulated genes (LEP, PRL, TAC3, MMP14, and ADAMTS4) and 4 down-regulated genes (TIMP4, FOS, CCK, and KISS1) were identified by PPI analysis. Finally, we identified 6 genes (PRL, LGALS14, EGR2, TAC3, LEP, and KISS1) that are potentially relevant to the pathophysiology of IUGR. Conclusion: The candidate down-regulated genes LGALS14 and KISS1, as well as the up-regulated genes PRL, EGR2, TAC3, and LEP, were found to be closely related to IUGR by bioinformatics analysis. These hub genes are related to hypoxia and oxidoreductase activities in placental development. We provide useful and novel information to explore the potential mechanism of IUGR and make efforts to the prevention of IUGR.

Background: Coding and non-coding short tandem repeats (STRs) facilitate a great diversity of phenotypic traits. The imbalance of mononucleotide A-repeats around transcription start sites (TSSs) was found in 3 mammals: H. sapiens, M. musculus , and R. norvegicus . Principal Findings: We found that the imbalance pattern originated in some vertebrates. A similar pattern was observed in mammals and birds, but not in amphibians and reptiles. We proposed that the enriched A-repeats upstream of TSSs is a novel hallmark of endotherms or warm-blooded animals. Gene ontology analysis indicates that the primary function of upstream A-repeats involves metabolism, cellular transportation, and sensory perception (smell and chemical stimulus) through housekeeping genes. Conclusions: Upstream A-repeats may play a regulatory role in the metabolic process of endothermic animals.

Introduction: In an effort to combat SARS-CoV-2 through multi-subunit vaccine design, during studies using whole genome and immunome, ORF10, located at the 3′ end of the genome, displayed unique features. It showed no homology to any known protein in other organisms, including SARS-CoV. It was observed that its nucleotide sequence is 100% identical in the SARS-CoV-2 genomes sourced worldwide, even in the recent-most VoCs and VoIs of B.1.1.529 (Omicron), B.1.617 (Delta), B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma) lineages, implicating its constant nature throughout the evolution of deadly variants. Aim: The structure and function of SARS-CoV-2 ORF10 and the role it may play in the viral evolution is yet to be understood clearly. The aim of this study is to predict its structure, function, and understand evolutionary dynamics on the basis of mutations and likely heightened immune responses in the immunopathogenesis of this deadly virus. Methods: Sequence analysis, ab-initio structure modeling and an understanding of the impact of likely substitutions in key regions of protein was carried out. Analyses of viral T cell epitopes and primary anchor residue mutations was done to understand the role it may play in the evolution as a molecule with likely enhanced immune response and consequent immunopathogenesis. Results: Few amino acid substitution mutations are observed, most probably due to the ribosomal frameshifting, and these mutations may not be detrimental to its functioning. As ORF10 is observed to be an expressed protein, ab-initio structure modeling shows that it comprises mainly an α-helical region and maybe an ER-targeted membrane mini-protein. Analyzing the whole proteome, it is observed that ORF10 presents amongst the highest number of likely promiscuous and immunogenic CTL epitopes, specifically 11 out of 30 promiscuous ones and 9 out of these 11, immunogenic CTL epitopes. Reactive T cells to these epitopes have been uncovered in independent studies. Majority of these epitopes are located on the α-helix region of its structure, and the substitution mutations of primary anchor residues in these epitopes do not affect immunogenicity. Its conserved nucleotide sequence throughout the evolution and diversification of virus into several variants is a puzzle yet to be solved. Conclusions: On the basis of its sequence, structure, and epitope mapping, it is concluded that it may function like those mini-proteins used to boost immune responses in medical applications. Due to the complete nucleotide sequence conservation even a few years after SARS-CoV-2 genome was first sequenced, it poses a unique puzzle to be solved, in view of the evolutionary dynamics of variants emerging in the populations worldwide.

Accurate diagnosis of chronic, non-healing wounds is challenging and time-consuming because it can be caused by a variety of etiologies. This brief report presents an unusual case of a chronic wound lasting for 10 months investigated by deep metagenomic sequencing. Epstein-Barr virus (EBV) was identified in the wound and subsequently validated by in situ hybridization. Histopathologic examination eventually revealed that the non-healing wound was due to an EBV-associated NK/T cell lymphoma. By identifying mutations across the viral genome, the virus was classified as Type I EBV and clustered with others of geographic proximity. Our results suggest that metagenomic shotgun sequencing can not only rapidly and accurately identify the presence of underlying pathogens but also provide strain-level resolution for the surveillance of viral epidemiology.

CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that the diversity and frequency of these pathways differ by the CRISPR-Cas type. The interplay between CRISPR-Cas systems and DNA repair pathways is essential for the acquisition of new spacers in CRISPR arrays. We conduct simulation studies to demonstrate that the efficiency of these DNA repair pathways may be inferred from the time-series patterns in the RNA structure of CRISPR repeats. This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to consider multifaceted interactions between different genes and systems, to design effective CRISPR-based antimicrobials that can specifically target drug-resistant bacteria in natural microbial communities.

B12D family proteins are transmembrane proteins that contain the B12D domain involved in membrane trafficking. Plants comprise several members of the B12D family, but these members’ numbers and specific functions are not determined. This study aims to identify and characterize the members of B12D protein family in plants. Phytozome database was retrieved for B12D proteins from 14 species. The total 66 B12D proteins were analyzed in silico for gene structure, motifs, gene expression, duplication events, and phylogenetics. In general, B12D proteins are between 86 and 98 aa in length, have 2 or 3 exons, and comprise a single transmembrane helix. Motif prediction and multiple sequence alignment show strong conservation among B12D proteins of 11 flowering plants species. Despite that, the phylogenetic tree revealed a distinct cluster of 16 B12D proteins that have high conservation across flowering plants. Motif prediction revealed 41 aa motif conserved in 58 of the analyzed B12D proteins similar to the bZIP motif, confirming that in the predicted biological process and molecular function, B12D proteins are DNA-binding proteins. Cis -regulatory elements screening in putative B12D promoters found various responsive elements for light, abscisic acid, methyl jasmonate, cytokinin, drought, and heat. Despite that, there is specific elements for cold stress, cell cycle, circadian, auxin, salicylic acid, and gibberellic acid in the promoter of a few B12D genes indicating for functional diversification for B12D family members. The digital expression shows that B12D genes of Glycine max have similar expression patterns consistent with their clustering in the phylogenetic tree. However, the expression of B12D genes of Hordeum vulgure appears inconsistent with their clustering in the tree. Despite the strong conservation of the B12D proteins of Viridiplantae, gene association analysis, promoter analysis, and digital expression indicate different roles for the members of the B12D family during plant developmental stages.