Gene Datasets Research Articles

Assessment of the genetic diversity of lymnaeid (Gastropoda: Pulmonata) snails and their infection status with trematode cercariae in different regions of Thailand

Lymnaeid snails are some of the most widespread snails and are the first intermediate host of trematode parasites that affect human and livestock health. A full understanding of the genetic relationship of hosts and parasites is of paramount importance for effective parasite management. The present study assessed the prevalence of trematode larvae in lymnaeid snails and examined the genetic diversity of these snails collected across Thailand. We collected 672 lymnaeid snails from 39 locations in 22 provinces of six regions in Thailand. Subsequently, cercarial infection in the snails was observed by using the shedding method. Lymnaeid snails released 5 types of trematode cercariae, namely, xiphidiocercariae, echinostome cercariae I, echinostome cercariae II, furcocercous cercariae, and strigea cercariae. The phylogenetic analysis based on ITS2 and 28S rDNA sequences revealed 5 cercaria types assigned to four trematode families, of which two belong to the group of human intestinal flukes. Combination of shell morphology and sequence analysis of the mitochondrial COI and 16S rDNA genes, the lymnaeid snails were classified into two species, Radix rubiginosa and Orientogalba viridis. Moreover, the combined dataset of mtDNA genes (COI + 16S rDNA) from R. rubiginosa and O. viridis revealed 32 and 15 different haplotypes, respectively, of which only a few haplotypes were infected with cercariae. The genetic diversity and genetic structure revealed that R. rubiginosa and O. viridis experienced a bottleneck phenomenon, and showed limited gene flow between populations. Population demographic history analyses revealed that R. rubiginosa and O. viridis experienced population reductions followed by recent population expansion. These findings may improve our understanding of parasite–lymnaeid evolutionary relationships, as well as the underlying molecular genetic basis, which is information that can be used for further effective control of the spread of trematode disease.

Inhibition of ferroptosis reverses heart failure with preserved ejection fraction in mice

BackgroundHeart failure with preserved ejection fraction (HFpEF) accounts for approximately 50% of heart failure cases. The molecular mechanisms by which HFpEF leads to impaired diastolic function of the heart have not been clarified, nor have the drugs that target the clinical symptoms of HFpEF patients.MethodsHFpEF chip data (GSE180065) was downloaded from the National Center for Biotechnology Information (NCBI) database. Differentially expressed genes (DEGs) were filtered by the limma package in R and processed for GO and KEGG pathway analyses. Then, ferroptosis-related genes in HFpEF were identified by taking the intersection between DEGs and ferroptosis-related genes. CytoHubba and MCODE were used to screen ferroptosis-related hub DEGs in the protein–protein interaction (PPI) network. Establishment of a mouse HFpEF model to validate the transcript levels of ferroptosis-related hub DEGs and ferroptosis-related phenotypes. Transcript levels of ferroptosis-related hub DEGs and HFpEF phenotypic changes in the hearts of HFpEF mice were further examined after the use of ferroptosis inhibitors.ResultsGO and KEGG enrichment analyses suggested that the DEGs in HFpEF were significantly enriched in ferroptosis-related pathways. A total of 24 ferroptosis-related DEGs were identified between the ferroptosis gene dataset and the DEGs. The established PPI network was further analyzed by CytoHubba and MCODE modules, and 11 ferroptosis-related hub DEGs in HFpEF were obtained. In animal experiments, HFpEF mice showed significant abnormal activation of ferroptosis. The expression trends of the 11 hub DEGs associated with ferroptosis, except for Cdh1, were consistent with the results of the bioinformatics analysis. Inhibition of ferroptosis alters the transcript levels of 11 ferroptosis-related hub DEGs and ameliorates HFpEF phenotypes.ConclusionsThe present study contributes to a deeper understanding of the specific mechanisms by which ferroptosis is involved in the development of HFpEF and suggests that inhibition of ferroptosis may mitigate the progression of HFpEF. In addition, eleven hub genes were recognized as potential drug binding targets.

First Report of Pitaya Virus X Infecting Lophocereus schottii f. mieckleyanus (Thin-Stemmed Totem Pole Cactus) in the United States.

Lophocereus is a genus of three species of columnar cacti native to Arizona and Mexico (Lodi, 2015). These cacti produce several tall, ascending, columnar stems that branch at the base in a candelabra-like arrangement. The most common species, L. schottii is known as the senita cactus. Several unusual knobby-stemmed spineless forms of senita cactus have been found in nature in Baja California, Mexico, which are collectively known as totem pole cacti. The thin-stemmed totem pole cactus, L. schottii f. mieckleyanus is an important part of landscapes in southern Arizona. Cacti are clonally propagated which makes viral infections of economic importance in the ornamental/nursery industry. In February 2023, virus-like symptoms, such as mosaic and chlorotic spots were observed on the stems of L. schottii f. mieckleyanus grown in a nursery in Phoenix, AZ, USA. Total RNA was extracted from two symptomatic cacti (YPHC-61 A & B) following the protocol by Tzanetakis et al. (2007), and cDNA was synthesized using the Superscript IV Reverse Transcriptase (Invitrogen, Vilnius, Lithuania). Reverse transcription polymerase chain reaction (RT-PCR) performed with cactus virus X specific primers (Kim et al. 2016) targeting the coat protein (CP) gene failed to generate any amplicon, while potexvirus-replicase primers, Potex 2RC and Potex 5 (van der Vlugt and Berendsen 2002) targeting RNA-dependent RNA polymerase (RdRp) gene amplified an expected amplicon of ~580 bp from both the samples. One of the amplicons was Sanger sequenced and showed 90.7% nucleotide (nt) identity with pitaya virus X (PiVX) in the GenBank (MN982522). Sequence was submitted in the GenBank under the accession number OR425049. PiVX is a new species of the genus Potexvirus and is named after its origin from pitaya (Hylocereus spp.). Further, RT-PCR was conducted with PiVX-specific primers, CP 110F/CP 604R targeting CP gene (Bae and Park 2022) and RdRp gene (RdRp F 5' GCGTGGGCCCTGGAAAA-3'/RdRp R 5' CTAAGATTCATCAATTCACCTCTCC-3') (this study). Amplicons of ~500 and 1100 bp were obtained using primers, CP 110F/CP 604R and RdRp F/RdRp R, respectively. A BLAST search revealed 90.5% nt identity to PiVX CP sequences (OM802135 and OM802134) and 87.3% nt identity to RdRp sequences (MN982523 and LC654699) in the GenBank. Sequences of isolates YPHC-61A and YPHC-61B were submitted in the GenBank under accession numbers, OQ915350 and PP182358 (CP gene) and OQ915351 and PP209539 (RdRp gene). Phylogenetic analysis based on the combined sequence datasets of CP and RdRp genes also grouped YPHC-61A and YPHC-61B with PiVX isolates and separated from other potexviruses species. For a bioassay of the virus, sap extract from symptomatic cactus was mechanically inoculated onto indicator plant species, i.e., beans, alfalfa, and melon. Ten days post- inoculation, chlorotic lesions were observed on beans and alfalfa plants, while melon and mock-inoculated plants did not show any symptoms. Similarly, L. schottii f. mieckleyanus plants grafted with infected cactus showed chlorotic spots after 30 days post grafting. Mechanically inoculated beans, alfalfa, and cactus plants were found to be positive for PiVX based on RT-PCR and Sanger sequencing. PiVX has earlier been detected on Notocactus leninghausii f. cristatus (Park et al. 2018) and dragon fruit (Selenicereus undatus) plants in South Korea (Bae and Park 2022). To our knowledge, this is the first report of PiVX on L. schottii f. mieckleyanus in the United States and worldwide.

Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach.

The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.

Feature selection for hybrid information systems based on fuzzy β covering and fuzzy evidence theory

Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information systems faces challenges. In addition, many existing feature selection methods are susceptible to noise, such as Fisher, LASSO, random forest, mutual information, rough-set-based methods, etc. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. Firstly, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy β covering with an anti-noise mechanism is established. Based on fuzzy belief and fuzzy plausibility, two robust feature selection algorithms for hybrid data are proposed in this framework. Experiments on 10 datasets of various types have shown that the proposed algorithms achieved the highest classification accuracy 11 times out of 20 experiments, significantly surpassing the performance of the other 6 state-of-the-art algorithms, achieved dimension reduction of 84.13% on seven UCI datasets and 99.90% on three large-scale gene datasets, and have a noise tolerance that is at least 6% higher than the other 6 state-of-the-art algorithms. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.

Analyzing the correlation between protein expression and sequence-related features of mRNA and protein in Escherichia coli K-12 MG1655 model.

It was necessary to have a tool that could predict the amount of protein and optimize the gene sequences to produce recombinant proteins efficiently. The Transim model published by Tuller et al. in 2018 can calculate the translation rate in E. coli using features on the mRNA sequence, achieving a Spearman correlation with the amount of protein per mRNA of 0.36 when tested on the dataset of operons' first genes in E. coli K-12 MG1655 genome. However, this Spearman correlation was not high, and the model did not fully consider the features of mRNA and protein sequences. Therefore, to enhance the prediction capability, our study firstly tried expanding the testing dataset, adding genes inside the operon, and using the microarray of the mRNA expression data set, thereby helping to improve the correlation of translation rate with the amount of protein with more than 0.42. Next, the applicability of 6 traditional machine learning models to calculate a "new translation rate" was examined using initiation rate and elongation rate as inputs. The result showed that the SVR algorithm had the most correlated new translation rates, with Spearman correlation improving to R = 0.6699 with protein level output and to R = 0.6536 with protein level per mRNA. Finally, the study investigated the degree of improvement when combining more features with the new translation rates. The results showed that the model's predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model's final translation rate to protein level was up to R = 0.6729. This demonstrated the model's capability to predict protein expression of a gene, rather than being limited to predicting expression by an mRNA and showed the model's potential for development into gene expression predicting tools.

Comparative transcriptomics of two Salvia subg. Perovskia species contribute towards molecular background of abietane-type diterpenoid biosynthesis

Tanshinones, are a group of diterpenoid red pigments present in Danshen – an important herbal drug of Traditional Chinese Medicine which is a dried root of Salvia miltiorrhiza Bunge. Some of the tanshinones are sought after as pharmacologically active natural products. To date, the biosynthetic pathway of tanshinones has been only partially elucidated. These compounds are also present in some of the other Salvia species, i.a. from subgenus Perovskia, such as S. abrotanoides (Kar.) Sytsma and S. yangii B.T. Drew. Despite of the close genetic relationship between these species, significant qualitative differences in their diterpenoid profile have been discovered. In this work, we have used the Liquid Chromatography–Mass Spectrometry analysis to follow the content of diterpenoids during the vegetation season, which confirmed our previous observations of a diverse diterpenoid profile. As metabolic differences are reflected in different transcript profile of a species or tissues, we used metabolomics-guided transcriptomic approach to select candidate genes, which expression possibly led to observed chemical differences. Using an RNA-sequencing technology we have sequenced and de novo assembled transcriptomes of leaves and roots of S. abrotanoides and S. yangii. As a result, 134,443 transcripts were annotated by UniProt and 56,693 of them were assigned as Viridiplantae. In order to seek for differences, the differential expression analysis was performed, which revealed that 463, 362, 922 and 835 genes indicated changes in expression in four comparisons. GO enrichment analysis and KEGG functional analysis of selected DEGs were performed. The homology and expression of two gene families, associated with downstream steps of tanshinone and carnosic acid biosynthesis were studied, namely: cytochromes P-450 and 2-oxoglutarate-dependend dioxygenases. Additionally, BLAST analysis revealed existence of 39 different transcripts related to abietane diterpenoid biosynthesis in transcriptomes of S. abrotanoides and S. yangii. We have used quantitative real-time RT-PCR analysis of selected candidate genes, to follow their expression levels over the vegetative season. A hypothesis of an existence of a multifunctional CYP76AH89 in transcriptomes of S. abrotanoides and S. yangii is discussed and potential roles of other CYP450 homologs are speculated. By using the comparative transcriptomic approach, we have generated a dataset of candidate genes which provides a valuable resource for further elucidation of tanshinone biosynthesis. In a long run, our investigation may lead to optimization of diterpenoid profile in S. abrotanoides and S. yangii, which may become an alternative source of tanshinones for further research on their bioactivity and pharmacological therapy.

Diversity and genetic structure of freshwater shark Wallago attu: an emerging species of commercial interest.

Pakistan has natural freshwater resources acting as a hotspot for diverse fish fauna. However, this aquatic fauna is declining at an alarming rate due to over-exploitation, habitat degradation, water pollution, climate change, and certain anthropogenic activities. The freshwater shark, Wallago attu, is a popular edible catfish inhabiting these freshwater ecosystems. Habitat degradation, overfishing, and human activities are heavily impacting the natural population of this species. So, sound knowledge about its population structure is necessary for its proper management in natural waters. The current study involves utilizing two mtDNA markers (COI, Cytb) to assess the genetic structure and differentiation among W. attu populations of Pakistani Rivers. Genetic variability analysis indicated a high haplotype (0.343 ± 0.046-0.870 ± 0.023) and low nucleotide diversity (0.0024 ± 0.012-0.0038 ± 0.018) among single and combined gene sequences, respectively. Overall, River Indus was populated with more diverse fauna of Wallago attu as compared to River Chenab and River Ravi. Population pairwise, Fst values (0.40-0.61) were found to be significantly different (p < 0.01) among three Riverine populations based upon combined gene sequences. The gene flow for the combined gene (COI + Cytb) dataset among three populations was less than 1.0. The transition/transversion bias value R (0.58) was calculated for testing of neutral evolution, and it declared low genetic polymorphism among natural riverine populations of Wallago attu. The current study's findings would be meaningful in planning the management and conservation of this economically important catfish in future.

Exploring the Molecular Mechanisms and Shared Gene Signatures Between Systemic Lupus Erythematosus and Bladder Urothelial Carcinoma.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with increased susceptibility to cancer, including bladder urothelial carcinoma (BLCA). This study investigates the shared molecular mechanisms and gene signatures between SLE and BLCA, shedding light on potential biomarkers and therapeutic targets. We compiled gene datasets related to SLE and BLCA from various databases and identified common genes. Differential gene expression analysis, protein-protein interaction networks, and hub gene identification were performed. We studied functional enrichment, immune infiltration, and transcription factor/miRNA regulation networks. We also explored gene-disease interactions and protein-chemical/drug networks. Hub gene expression levels and diagnostic values were validated in TCGA and GEO databases. Prognostic analysis was performed on the core gene MMP9 in the TCGA-BLCA database to study its prognostic value. Finally, the mRNA expression of MMP9 was verified in bladder cancer cell lines and BLCA patient blood. The diagnostic value of MMP9 for BLCA was verified by receiver operating characteristic(ROC) curve analysis of the expression of MMP9 in patients' blood. We identified 524 common genes between SLE and BLCA, enriched in pathways related to apoptosis and cytokine regulation. Immune infiltration analysis for two diseases. Transcription factors and microRNAs were implicated in regulating these common genes. The gene-disease network linked hub genes with various diseases, emphasizing their roles in autoimmune disease and cancer. Protein-chemical/drug networks highlighted potential treatment options. Finally, our study found that MMP9 is a potential therapeutic target with diagnostic and prognostic value and Immune-related biomarkers in patients with BLCA and SLE. Our study reveals shared molecular mechanisms, genetic signatures, and immune infiltrates between SLE and BLCA. MMP9 emerges as a potential diagnostic and prognostic biomarker in BLCA, warranting further investigation. These findings provide insights into the pathogenesis of SLE-associated BLCA and may guide future research and therapeutic strategies.

COMPARATIVE ASSESSMENT OF BIOBANK DATASETS FOR THE STUDY OF INFLAMMATORY BOWEL DISEASE

Abstract Lack of available multiomics datasets has slowed progress in understanding the relationship between genomic variants, gene expression, and disease progress in inflammatory bowel disease (IBD). Biobanks provide an opportunity to produce population-scale datasets to accelerate understanding of these diseases. Ovation’s IBD Omic Data is a multiomic dataset including whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) from disease and normal tissues, derived from clinical biopsies of patients with IBD. Omic data is supplemented with patient phenotypic data including diagnoses, therapies, procedures, and routine laboratory results. We report initial results of analysis of the first 212 patients from this cohort to identify genes differentially expressed between disease and normal colon tissue in patients with IBD, and to explore variants in these genes in both the Ovation IBD Omic Data and UK Biobank (UKBB) genomic data. To generate Ovation IBD Omic Data, DNA and RNA were extracted from fixed formalin paraffin embedded (FFPE) tissue samples of 212 IBD patients. Sequencing was conducted at a depth of 30X WGS. RNA-seq analysis was performed at a depth of 30M 1x50-bp reads per sample. Subsequent analysis was done via STAR aligner, FeatureCounts, and DESeq2. We curated a list of 54 common genes implicated in IBD from the literature as a benchmark. WGS data in the Variant Call Format (VCF) were ingested into the REVEAL platform along with phenotypic data variables. Values for minor allele frequencies (maf) were calculated and annotations were generated using the Ensembl Variant Effect Predictor (VEP). We examined the number of unique genomic variants in coding regions of the Ovation IBD Omic Data compared to those from the UKBB dataset for the benchmark genes. Despite the difference in sample population size, the Ovation data included a median of ~3,600 more unique variants per gene (~85% had more variants identified in a population of 212). Analysis of the global gene expression levels from Ovation’s IBD dataset showed 18 genes from the benchmark had significantly different expression in disease and normal tissue (p-value &amp;lt;0.001) across all IBD patients. We also analyzed the subset of samples from patients with CD (n=58). Notably, SLC4A4 and IFITM3 did not pass the original threshold in the full analysis (p-values were &amp;gt;0.001), however they were statistically significant when the analysis was limited to CD patients (p-value &amp;lt;0.0001). Comparing datasets of varying complexity shows that, despite a smaller sample size, specialized data derived from clinical samples, such as the Ovation IBD Omic Data, can provide additional insights for complex diseases like IBD. Further analysis will focus on the relationship between unique genomic variants identified, expression profiles and associated metadata to identify candidates of potential clinical relevance.

Taxonomic study and diversity of Postia s.lat. in Swat, Pakistan: addition of five brown rot Polypores to the country.

Brown rot Polypores are ecologically significant as they play a crucial role in maintaining the carbon cycle and contribute to humus formation in forest ecosystems through their lignocellulose degradation ability. It is important to note that some species can significantly impact timber, potentially causing decay in economically valuable wood. Many Asian countries including Pakistan are still under the exploratory phase and have undocumented species diversity in Polypore fungi. In the current study, collections representing five different species belonging to two families, Postiaceae and Adustoporiaceae, were subjected to detailed morphoanatomical and molecular analyses. A combined matrix of two gene datasets (ITS and nrLSU) was analyzed using three different phylogenetic methods viz. Maximum Parsimony (MP), Maximum Likelihood (ML), and Bayesian inference (BI). Our study presents descriptions of five previously undocumented brown rot Polypore species from the country including Fuscopostia fragilis (Fr.) B.K. Cui, L.L. Shen & Y.C. Dai, Amaropostia stiptica (Pers.) B.K. Cui, L.L. Shen & Y.C. Dai, Cyanosporus piceicola B.K. Cui, L.L. Shen & Y.C. Dai, Spongiporus balsameus (Peck) A. David, Rhodonia placenta (Fr.) Niemelä, K.H. Larss. & Schigel. Regarding the molecular data, nodes of our subject sequences were substantially supported and fell under their respective species clades with high ML bootstrap values (≥ 95), MP bootstrap ≥ 74 and BI probabilities ≥ 0.98. Findings of the study will not only contribute to our understanding of local Polypores species diversity but also enhance knowledge of geographical distribution in global context.

Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches

Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological conditions that have attracted significant attention due to the higher prevalence of EC in patients with PCOS. Even with this proven association, little is known about the complex molecular pathways that connect PCOS to an increased risk of EC. In order to address this, our study presents two main innovations. To provide a solid basis for our analysis, we have first created a dataset of genes linked to EC and PCOS. Second, we start by building fixed-size ensembles, and then we refine the configuration of a single clustering algorithm within the ensemble at each step of the hyperparameter optimization process. This optimization evaluates the potential performance of the ensemble as a whole, taking into consideration the interactions between each algorithm. All the models in the ensemble are individually optimized with the suitable hyperparameter optimization method, which allows us to tailor the strategy to the model’s needs. Our approach aims to improve the ensemble’s performance, significantly enhancing the accuracy and robustness of clustering outcomes. Through this approach, we aim to enhance our understanding of PCOS and EC, potentially leading to diagnostic and treatment breakthroughs.

Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients.

There is insufficient understanding on systemic interferon (IFN) responses during COVID-19 infection. Early reports indicated that interferon responses were suppressed by the coronavirus (SARS-CoV-2) and clinical trials of administration of various kinds of interferons had been disappointing. Expression of interferon-stimulated genes (ISGs) in peripheral blood (better known as interferon score) has been a well-established bioassay marker of systemic IFN responses in autoimmune diseases. Therefore, with archival samples of a cohort of COVID-19 patients collected before the availability of vaccination, we aimed to better understand this innate immune response by studying the IFN score and related ISGs expression in bulk and single cell RNAs sequencing expression datasets. In this study, we recruited 105 patients with COVID-19 and 30 healthy controls in Hong Kong. Clinical risk factors, disease course, and blood sampling times were recovered. Based on a set of five commonly used ISGs (IFIT1, IFIT2, IFI27, SIGLEC1, IFI44L), the IFN score was determined in blood leukocytes collected within 10 days after onset. The analysis was confined to those blood samples collected within 10 days after disease onset. Additional public datasets of bulk gene and single cell RNA sequencing of blood samples were used for the validation of IFN score results. Compared to the healthy controls, we showed that ISGs expression and IFN score were significantly increased during the first 10 days after COVID infection in majority of patients (71%). Among those low IFN responders, they were more commonly asymptomatic patients (71% vs 25%). 22 patients did not mount an overall significant IFN response and were classified as low IFN responders (IFN score < 1). However, early IFN score or ISGs level was not a prognostic biomarker and could not predict subsequent disease severity. Both IFI27 and SIGLEC1 were monocyte-predominant expressing ISGs and IFI27 were activated even among those low IFN responders as defined by IFN score. In conclusion, a substantial IFN response was documented in this cohort of COVID-19 patients who experience a natural infection before the vaccination era. Like innate immunity towards other virus, the ISGs activation was observed largely during the early course of infection (before day 10). Single-cell RNA sequencing data suggested monocytes were the cell-type that primarily accounted for the activation of two highly responsive ISGs (IFI44L and IFI27). As sampling time and age were two major confounders of ISG expression, they may account for contradicting observations among previous studies. On the other hand, the IFN score was not associated with the severity of the disease.

Complete mitochondrial genome of an oleaginous microalga Vischeria punctata (Eustigmatophyceae: Chlorobotryaceae) and phylogenetic analysis

Vischeria punctata, as first described by Vischer in 1945, is a member of the family Chlorobotryaceae, within the order Eustigmatales. This species is recognized for its potential as a source of biofuels and other high-value products. In the present investigation, the whole genome of V. punctata was sequenced utilizing the Illumina HiSeq 4000 platform, enabling the assembly and annotation of its complete mitochondrial genome. The resulting circular genome spans 41,528 base pairs (bp) with a guanine-cytosine (GC) content of 27.3%. This genome encompasses 36 protein-coding genes, alongside 28 transfer RNA (tRNA), and three ribosomal RNA (rRNA) genes. The evolutionary trajectory of V. punctata was further explored by constructing a phylogenetic tree derived from the mitochondrial 33 gene dataset of 16 Ochrophyta species. Comparative analysis reveals that V. punctata bears closer ties to Vischeria sp. CAUP Q202 than to Vischeria stellata strain SAG 33.83, suggesting shared evolutionary pathways and phenotypic traits. This investigation constitutes the inaugural study into the mitochondrial evolution and phylogenetic patterning of the mitogenome in V. punctata. The outcomes from this research bolster our understanding of the genetic diversity and evolutionary processes within the class Eustigmatophyceae. In particular, the mitochondrial genome of V. punctata serves as a valuable resource in elucidating these aspects.

Morphological and molecular characterization of Halamphoravantushpaensis (Bacillariophyceae, Amphipleuraceae), a new diatom species widely dispersed on the shores of the soda Lake Van (Türkiye).

In this study, we describe Halamphoravantushpaensis sp. nov., a newly identified diatom species found in the highly alkaline Lake Van in Eastern Turkey (Türkiye). This new species is characterized morphologically by light and scanning electron microscopy, performed on both wild and cultivated samples. Two monoclonal cultures were submitted to a genome-skimming approach, giving access to the complete sequence of their nuclear rRNA cluster of genes, mitochondrial and plastid genomes. Both strains were highly similar from the genomic point of view, with few mutations noted, although in organellar genomes some of them concerned protein coding genes and were non-silent. Also, the group II intron in the mitochondrial cox1 gene was found to display a relatively high number of polymorphisms. The plastome also distinguishes itself from other Halamphora spp. by the extension of its inverted repeat at the expense of the two single copy regions of the genome. Maximum likelihood molecular phylogeny inferred from a concatenated three genes dataset (18S, psbC and rbcL) positions this species within the K clade, which is known to contain hypersaline to freshwater species.

Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.

Gene expression data are used to discover meaningful hidden information in gene datasets. Cancer and other disorders may be diagnosed based on differences in gene expression profiles, and this information can be gleaned by gene sequencing. Thanks to the tremendous power of artificial intelligence (AI), healthcare has become a significant user of deep learning (DL) for predicting cancer diseases and categorizing gene expression. Gene expression Microarrays have been proved effective in predicting cancer diseases and categorizing gene expression. Gene expression datasets contain only limited samples, but the features of cancer are diverse and complex. To overcome their dimensionality, gene expression datasets must be enhanced. By learning and analyzing features of input data, it is possible to extract features, as multidimensional arrays, from the data. Synthetic samples are needed to strengthen the range of information. DL strategies may be used when gene expression data are used to diagnose and classify cancer diseases.

Detecting the Tumor Prognostic Factors From the YTH Domain Family Through Integrative Pan-Cancer Analysis.

Emerging evidence suggests that N6-methyladenosine (m6A) methylation plays a critical role in cancers through various mechanisms. This work aims to reveal the essential role of m6A methylation "readers" in regulation of cancer prognosis at the pan-cancer level. Herein, we focused on one special protein family of the "readers" of m6A methylation, YT521-B homology (YTH) domain family genes, which were observed to be frequently dysregulated in tumor tissues and closely associated with cancer prognosis. Then, a comprehensive analysis of modulation in cancer prognosis was conducted by integrating RNA sequencing (RNAseq) datasets of YTH family genes and clinical information at the pan-cancer level. YTH family genes were significantly differentially expressed in most of the cancers, particularly increased in Gastrointestinal cancers, and decreased in Endocrine and Urologic cancers. In addition, they were observed to be associated with overall survival (OS) and disease-specific survival (DSS) with various extent, especially in lower grade glioma (LGG), thyroid cancer (THCA), liver hepatocellular carcinoma (LIHC) and kidney clear cell carcinoma (KIRC), so were some "writers" (METLL3, METLL14, WTAP) and "erasers" (FTO, ALKBH5). Further survival analysis illustrated that YTH family genes specifically YTHScore constructed by combining 5 YTH family genes, as well as RWEScore calculated by combining genes from "readers"-"writers"-"erasers" could dramatically distinguish tumor prognosis in 4 representative cancers. As expected, YTHScore presented an equally comparable prognostic classification with RWEScore. Finally, analysis of immune signatures and clinical characteristics implied that, the activity of the innate immune, diagnostic age, clinical stage, Tumor-Node-Metastasis (TNM) stage and immune types, might play specific roles in modulating tumor prognosis. The study demonstrated that YTH family genes had the potential to predict tumor prognosis, in which the YTHScore illustrated equal ability to predict tumor prognosis compared to RWEScore, thus providing insights into prognostic biomarkers and therapeutic targets at the pan-cancer level.

Comprehensive analysis of PPP4C's impact on prognosis, immune microenvironment, and immunotherapy response in lung adenocarcinoma using single-cell sequencing and multi-omics.

Elevated PPP4C expression has been associated with poor prognostic implications for patients suffering from lung adenocarcinoma (LUAD). The extent to which PPP4C affects immune cell infiltration in LUAD, as well as the importance of associated genes in clinical scenarios, still requires thorough investigation. In our investigation, we leveraged both single-cell and comprehensive RNA sequencing data, sourced from LUAD patients, in our analysis. This study also integrated datasets of immune-related genes from InnateDB into the framework. Our expansive evaluation employed various analytical techniques; these included pinpointing differentially expressed genes, constructing WGCNA, implementing Cox proportional hazards models. We utilized these methods to investigate the gene expression profiles of PPP4C within the context of LUAD and to clarify its potential prognostic value for patients. Subsequent steps involved validating the observed enhancement of PPP4C expression in LUAD samples through a series of experimental approaches. The array comprised immunohistochemistry staining, Western blotting, quantitative PCR, and a collection of cell-based assays aimed at evaluating the influence of PPP4C on the proliferative and migratory activities of LUAD cells. In lung cancer, elevated expression levels of PPP4C were observed, correlating with poorer patient prognoses. Validation of increased PPP4C levels in LUAD specimens was achieved using immunohistochemical techniques. Experimental investigations have substantiated the role of PPP4C in facilitating cellular proliferation and migration in LUAD contexts. Furthermore, an association was identified between the expression of PPP4C and the infiltration of immune cells in these tumors. A prognostic framework, incorporating PPP4C and immune-related genes, was developed and recognized as an autonomous predictor of survival in individuals afflicted with LUAD. This prognostic tool has demonstrated considerable efficacy in forecasting patient survival and their response to immunotherapeutic interventions. The involvement of PPP4C in LUAD is deeply intertwined with the tumor's immune microenvironment. PPP4C's over-expression is associated with negative clinical outcomes, promoting both tumor proliferation and spread. A prognostic framework based on PPP4C levels may effectively predict patient prognoses in LUAD, as well as the efficacy of immunotherapy strategy. This research sheds light on the mechanisms of immune interaction in LUAD and proposes a new strategy for treatment.

Identification of Biomarkers Associated with Oxidative Stress in Aortic Dissection Based on Bulk Transcriptome Analyses.

The aim of this study is to investigate the underlying molecular mechanism of oxidative stress (OS) involved in aortic dissection (AD). Datasets of AD and OS-related genes were obtained from the Gene Expression Omnibus (GEO) and the GeneCards database, respectively. Differential expression analysis and weighted gene correlation network analysis (WGCNA) were employed to screen genes. After enrichment analysis, a protein-protein interaction (PPI) network was constructed, and machine learning algorithms were used to determine signature genes. Comprehensive bioinformatics analyses on the signature genes were executed, and a clinical prediction model was established and evaluated. External datasets, in vitro experiment, and Mendelian randomization (MR) analysis were applied to validation. We identified CCL2, ITGB4, MYC, SOCS3, SPP1 and TEK as OS-related signature genes in AD. The area under the ROC curve of all the signature genes was greater than 0.75. The clinical prediction model based on the signature genes showed satisfactory diagnostic efficacy in both training and validation cohorts. In validation cohort and in vitro experiment, CCL2, MYC, SPP1 and TEK were further validated. However, the MR results showed no causal association between the expression of the signature genes and AD. This study demonstrated that OS participates in and affects the progression of AD. Six biomarkers associated with OS could be perceived as crucial targets for the diagnosis and treatment of AD.

IL1A regulates the inflammation in gout through the Toll-like receptors pathway.

Objective: Gout is a dangerous metabolic condition related to monosodium urate (MSU). Our aim is to study the molecular mechanisms underlying gout and to identify potential clinical biomarkers by bioinformatics analysis and experimental validation. Methods: In this study, we retrieved the overlapping genes between GSE199950-Differential Expressed Genes (DEGs) dataset and key module in Weighted Gene Co-Expression Network Analysis (WGCNA) on GSE199950. These genes were then analyzed by protein-protein interaction (PPI) network, expression and Gene Set Enrichment Analysis to identify the hub gene related to gout. Then, the gene was investigated by peripheral blood mononuclear cells (PBMCs), immunoassay and cell experiments like western blotting to uncover its underlying mechanism in gout cells. Results: From the turquoise module and 83 DEGs, we identified 62 overlapping genes, only 11 genes had mutual interactions in PPI network and these genes were highly expressed in MSU-treated samples. Then, it was found that the IL1A (interleukin 1 alpha) was the only one gene related to Toll-like receptor signaling pathway that was associated with the occurrence of gout. Thus, IL1A was determined as the hub gene in this study. In immunoassay, IL1A was significantly positively correlated with B cells and negatively correlated with macrophages. Moreover, IL1A is highly expressed in gout patients,it has a good clinical diagnostic value. Finally, the results of in vitro experiments showed that after knocking down IL1A, the expressions of pro-inflammatory cytokines and Toll-like receptor signaling pathway-related proteins (TLR2, TLR4, MyD88) were all reduced. Conclusion: It is confirmed that IL1A is a promoting gene in gout with a good diagnostic value, and specifically it affects the inflammation in gout through Toll-like receptor pathway. Our research offers fresh perspectives on the pathophysiology of gout and valuable directions for future diagnosis and treatment.