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Abstract Background Lichens, traditionally considered as a simple partnership primarily between mycobiont and photobiont, are, in reality, complex holobionts comprised of a multitude of microorganisms. Lichen mycobiome represents fungal community residing within lichen thalli. While it is acknowledged that factors like the host lichen species and environmental conditions influence the structure of the lichen mycobiome, the existing research remains insufficient. To investigate which factor, host genus or location, has a greater impact on the lichen mycobiome, we conducted a comparative analysis of mycobiomes within Parmelia and Peltigera collected from both Turkey and South Korea, using high-throughput sequencing based on internal transcribed spacer region amplification. Results Overall, the lichen mycobiome was dominated by Capnodiales (Dothideomycetes), regardless of host or location. At the order level, the taxonomic composition was not significantly different according to lichen genus host or geographical distance. Hierarchical clustering of the top 100 abundant ASVs did not clearly indicate whether the lichen mycobiome was more influenced by host genus or location. Analyses of community similarity and partitioning variables revealed that the structure of the lichen mycobiome is more significantly influenced by location than by host genus. When analyzing the core mycobiome by host genus, the Peltigera mycobiome contained more ASV members than the Parmelia mycobiome. These two core mycobiomes also share common fungal strains, including basidiomycete yeast. Additionally, we used chi-squared tests to identify host genus-specialists and location-specialists. Conclusions By comparing lichen mycobiomes of the same genera across different countries, our study advances our comprehension of these microbial communities. Our study elucidates that, although host species play a contributory role, geographic distance exerts a more pronounced impact on the structure of lichen mycobiome. We have made foundational contributions to understanding the lichen mycobiome occupying ecologically crucial niches. We anticipate that broader global-scale investigations into the fungal community structures will provide more detailed insights into fungal residents within lichens.

The multisystemic effects of COVID-19 may continue for a longer time period following the acute phase, depending on the severity of the disease. However, long-term systemic transcriptomic changes associated with COVID-19 disease and the impact of disease severity are not fully understood. We aimed to investigate the impact of COVID-19 and its severity on transcriptomic alterations in peripheral blood mononuclear cells (PBMCs) following 1 year of the disease. PBMCs were isolated from the peripheral blood of healthy control donors who did not have COVID-19 (C; n = 13), from COVID-19 patients without pneumonia (NP; n = 11), and from COVID-19 patients with severe pneumonia (SP; n = 10) after 1-year of follow-up. Following RNA isolation from PBMCs, high-quality RNAs were sequenced after creating a library. Differentially expressed genes (DEGs) and differentially expressed long non-coding RNAs (DElncRNAs) were identified using Benjamini–Hochberg correction and they were analysed for hierarchical clustering and principal component analysis (PCA). Intergroup comparisons (C vs. NP, C vs. SP, and NP vs. SP) of DEGs and DElncRNAs were performed and hub genes were determined. Functional enrichment analyses of DEGs and DElncRNAs were made using Metascape (v3.5.20240101) and the first version of NCPATH. The RNA sequencing analysis revealed 4843 DEGs and 1056 DElncRNAs in “C vs. NP”, 1651 DEGs and 577 DElncRNAs in “C vs. SP”, and 954 DEGs and 148 DElncRNAs in “NP vs. SP”, with 291 DEGs and 70 DElncRNAs shared across all groups, respectively. We identified 14 hub genes from 291 DEGs, with functional enrichment analysis showing upregulated DEGs mainly linked to inflammation and osteoclast differentiation and downregulated DEGs to viral infections and immune responses. The analysis showed that 291 common and 14 hub genes were associated with pneumonia and that these genes could be regulated by the transcription factors JUN and NFκB1 carrying the NFκB binding site. We also revealed unique immune cell signatures across DEG categories indicating that the upregulated DEGs were associated with neutrophils and monocytes, while downregulated DEGs were associated with CD4 memory effector T cells. The comparative transcriptomic analysis of NP and SP groups with 52 gene signatures suggestive of IPF risk showed a lower risk of IPF in the SP group than the NP patients. Our findings suggest that COVID-19 may cause long term pathologies by modulating the expression of various DEGs, DeLncRNAs, and hub genes at the cellular level.

The NCBI SRA database is constantly expanding due to the large amount of genomic and transcriptomic data from various organisms generated by next-generation sequencing, and re-searchers worldwide regularly deposit new data into the database. This high-coverage genomic and transcriptomic information can be re-evaluated regardless of the original research subject. The database-deposited NGS data can offer valuable insights into the genomes of organelles, particularly for non-model organisms. Here, we developed an automated bioinformatics workflow called “OrgaMiner”, designed to unveil high-quality mitochondrial and chloroplast genomes by data mining the NCBI SRA database. OrgaMiner, a Python-based pipeline, automatically orchestrates various tools to extract, assemble, and annotate organelle genomes for non-model organisms without available organelle genome sequences but with data in the NCBI SRA. To test the usability and feasibility of the pipeline, “mollusca” was selected as a keyword, and 76 new mitochondrial genomes were de novo assembled and annotated automatically without writing one single code. The applicability of the pipeline can be expanded to identify organelles in diverse invertebrate, vertebrate, and plant species by simply specifying the taxonomic name. OrgaMiner provides an easy-to-use, end-to-end solution for biologists mainly working with taxonomy and population genetics.

Crocus istanbulensis (B.Mathew) Rukšāns is one of the most endangered Crocus species in the world and has an extremely limited distribution range in Istanbul. Our recent field work indicates that no more than one hundred individuals remain in the wild. In the present study, we used genome skimming to determine the complete chloroplast (cp) genome sequences of six C. istanbulensis individuals collected from the locus classicus. The cp genome of C. istanbulensis has 151,199 base pairs (bp), with a large single-copy (LSC) (81,197 bp), small single copy (SSC) (17,524 bp) and two inverted repeat (IR) regions of 26,236 bp each. The cp genome contains 132 genes, of which 86 are protein-coding (PCGs), 8 are rRNA and 38 are tRNA genes. Most of the repeats are found in intergenic spacers of Crocus species. Mononucleotide repeats were most abundant, accounting for over 80% of total repeats. The cp genome contained four palindrome repeats and one forward repeat. Comparative analyses among other Iridaceae species identified one inversion in the terminal positions of LSC region and three different gene (psbA, rps3 and rpl22) arrangements in C. istanbulensis that were not reported previously. To measure selective pressure in the exons of chloroplast coding sequences, we performed a sequence analysis of plastome-encoded genes. A total of seven genes (accD, rpoC2, psbK, rps12, ccsA, clpP and ycf2) were detected under positive selection in the cp genome. Alignment-free sequence comparison showed an extremely low sequence diversity across naturally occurring C. istanbulensis specimens. All six sequenced individuals shared the same cp haplotype. In summary, this study will aid further research on the molecular evolution and development of ex situ conservation strategies of C. istanbulensis.

Background Extraintestinal pathogenic Escherichia coli (ExPEC) is an important bacterium and responsible for many bloodstream infections, including urinary tract infections and even fatal bacteremia. The aim of this research was to investigate whether ExPEC strains isolated from Turkish blood cultures have a relationship between 16S rRNA based phylogenetic clusters and antibiotic resistance profiles, virulence factors or clonal lineages. Methods Phenotypically identified ExPEC blood culture isolates (n = 104) were included in this study. The 16S rRNA partial sequence analysis was performed for genotypic identification of ExPEC isolates. Antibiotic susceptibility and Extended-Spectrum β-Lactamase testing of isolates were performed. Phylogenetic classification (A, B1, B2 and D), Multi Locus Sequence Typing analysis and virulence-associated genes were investigated. Results Based on 16S rRNA partial sequence analysis, 97 out of 104 (93.26%) ExPEC isolates were confirmed as E. coli. Ampicillin (74.22%) and cefuroxime axetil (65.97%) resistances had the highest frequencies among the ExPEC isolates. In terms of phylogenetic classification of ExPEC, D (38.14%, 37/97) was the most prevalent group after A (29.89%, 29/97), B2 (20.61%, 20/97), and B1 (11.34%, 11/97). The sequence types of the 20 ExPEC isolates belonging to the B2 phylogenetic group were analyzed by Multi Locus Sequence Typing. Ten isolates out of 20 (50.0%) were identified as ST131. The other STs were ST95 (n = 1), ST14 (n = 1), ST10 (n = 1), ST69 (n = 1), ST1722 (n = 2), ST141 (n = 1), ST88 (n = 1), ST80 (n = 1), and ST998 (n = 1). Of the ST131 strains, six (60%, 6/10) represented serogroup O25. The most common virulence factor genes were serum resistance factor gene, traT (55.7%) aerobactin siderophore receptor and yersiniabactin encoding genes iutA (45.3%) and fyuA (50.5%), respectively. In addition, PAI (41.2%), iroN (23.7%), hlyA (15.4%), kpsII (13.4%), ompT (13.4%), papG (12.4%), iss (9.3%), cnf1 (7.2%), ibeA (2.06%), and sfaS (2.06%) genes were present in the ExPEC isolates. Conclusion The 16S rRNA-based phylogenetic relationship tree analysis showed that a large cluster was present among 97 ExPEC isolates along with related reference strains. There were 21 main clusters with 32 closely related subclusters. Based on our findings, different clonal lineages of ExPEC can display different antibiotic susceptibilities and virulence properties. We also concluded that virulence factors were not distributed depending on phylogenetic groups (A, B1, B2, and D). The ExPEC isolates belonging to the same phylogenetic group and sequence type could display different resistance and virulence characteristics.

RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom’s precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

Hyperhydricity is a morphophysiological disorder of plants in tissue culture characterized morphologically by the presence of translucent, thick, curled, and fragile leaves as a result of excessive water intake. Since clonal propagation is a major in vitro technique for multiplying plants vegetatively, the emergence of hyperhydricity-related symptoms causes significant economic losses to agriculture and horticulture. Although numerous efforts have been hitherto devoted to the morphological and anatomical responses of plants to hyperhydricity, the underlying molecular mechanism remains largely unknown. Here, a genome-wide transcriptome analysis was performed to identify differentially expressed genes in hyperhydric and nonhyperhydric leaves of peach [ (L.) Batsch]. The RNA sequencing (RNA-Seq) analysis showed that the expression of >300 transcripts was altered between control and hyperhydric leaf cells. The top 30 differentially expressed transcripts (DETs) were related to the posttranscriptional regulators of organelle gene expression and photosynthesis, cellular elimination, plant cuticle development, and abiotic stress response processes. The expression of 10 DETs was also conformed by quantitative real-time polymerase chain reaction (RT-qPCR) in hyperhydric and nonhyperhydric leaves. As a complex biological process, hyperhydricity alters the expression of various transcripts including transcription factor (), RNA binding protein (pentatricopeptide, ), transporter protein (), and . Thus, this genome-wide transcriptome profiling study may help elucidate the molecular mechanism of hyperhydricity.

bZIP proteins are one of the largest transcriptional regulators playing crucial roles in plant development, physiological processes, and biotic/abiotic stress responses. Despite the availability of recently published draft genome sequence of Cucumis sativus, no comprehensive investigation of these family members has been presented for cucumber. We have identified 64 bZIP transcription factor-encoding genes in the cucumber genome. Based on structural features of their encoded proteins, CsbZIP genes could be classified into 6 groups. Cucumber bZIP genes were expanded mainly by segmental duplication rather than tandem duplication. Although segmental duplication rate of the CsbZIP genes was lower than that of Arabidopsis, rice and sorghum, it was observed as a common expansion mechanism. Some orthologous relationships and chromosomal rearrangements were observed according to comparative mapping analysis with other species. Genome-wide expression analysis of bZIP genes indicated that 64 CsbZIP genes were differentially expressed in at least one of the ten sampled tissues. A total of 4 CsbZIP genes displayed higher expression values in leaf, flowers and root tissues. The in silico micro-RNA (miRNA) and target transcript analyses identified that a total of 21 CsbZIP genes were targeted by 38 plant miRNAs. CsbZIP20 and CsbZIP22 are the most targeted by miR165 and miR166 family members, respectively. We also analyzed the expression of ten CsbZIP genes in the root and leaf tissues of drought-stressed cucumber using quantitative RT-PCR. All of the selected CsbZIP genes were measured as increased in root tissue at 24th h upon PEG treatment. Contrarily, the down-regulation was observed in leaf tissues of all analyzed CsbZIP genes. CsbZIP12 and CsbZIP44 genes showed gradual induction of expression in root tissues during time points. This genome-wide identification and expression profiling provides new opportunities for cloning and functional analyses, which may be used in further studies for improving stress tolerance in plants.

Boron stress is an environmental factor affecting plant development and production. Recently, microRNAs (miRNAs) have been found to be involved in several plant processes such as growth regulation and stress responses. In this study, miRNAs associated with boron stress were identified and characterized in barley. miRNA profiles were also comparatively analyzed between root and leave samples. A total of 31 known and 3 new miRNAs were identified in barley; 25 of them were found to respond to boron treatment. Several miRNAs were expressed in a tissue specific manner; for example, miR156d, miR171a, miR397, and miR444a were only detected in leaves. Additionally, a total of 934 barley transcripts were found to be specifically targeted and degraded by miRNAs. In silico analysis of miRNA target genes demonstrated that many miRNA targets are conserved transcription factors such as Squamosa promoter-binding protein, Auxin response factor (ARF), and the MYB transcription factor family. A majority of these targets were responsible for plant growth and response to environmental changes. We also propose that some of the miRNAs in barley such as miRNA408 might play critical roles against boron exposure. In conclusion, barley may use several pathways and cellular processes targeted by miRNAs to cope with boron stress.

Peach (Prunus persica L.) is one of the most important worldwide fresh fruits. Since fruit growth largely depends on adequate water supply, drought stress is considered as the most important abiotic stress limiting fleshy fruit production and quality in peach. Plant responses to drought stress are regulated both at transcriptional and post-transcriptional level. As post-transcriptional gene regulators, miRNAs (miRNAs) are small (19-25 nucleotides in length), endogenous, non-coding RNAs. Recent studies indicate that miRNAs are involved in plant responses to drought. Therefore, Illumina deep sequencing technology was used for genome-wide identification of miRNAs and their expression profile in response to drought in peach. In this study, four sRNA libraries were constructed from leaf control (LC), leaf stress (LS), root control (RC) and root stress (RS) samples. We identified a total of 531, 471, 535 and 487 known mature miRNAs in LC, LS, RC and RS libraries, respectively. The expression level of 262 (104 up-regulated, 158 down-regulated) of the 453 miRNAs changed significantly in leaf tissue, whereas 368 (221 up-regulated, 147 down-regulated) of the 465 miRNAs had expression levels that changed significantly in root tissue upon drought stress. Additionally, a total of 197, 221, 238 and 265 novel miRNA precursor candidates were identified from LC, LS, RC and RS libraries, respectively. Target transcripts (137 for LC, 133 for LS, 148 for RC and 153 for RS) generated significant Gene Ontology (GO) terms related to DNA binding and catalytic activities. Genome-wide miRNA expression analysis of peach by deep sequencing approach helped to expand our understanding of miRNA function in response to drought stress in peach and Rosaceae. A set of differentially expressed miRNAs could pave the way for developing new strategies to alleviate the adverse effects of drought stress on plant growth and development.

We investigated the magnetic properties of Cr3+ (J  <  0) ion-modified DNA (M-DNA) nanowire (1000 base) at room temperature under a uniform magnetic field (∼100 Oe) for different doping concentrations. A Monte Carlo simulation method-based Metropolis algorithm is used to figure out the thermodynamic quantities of nanowire formed by Cr M-DNA followed by analysing the dependency of the ferromagnetic behaviour of the M-DNA to dopant concentration. It is understood that ion density/base and ion density/helical of Cr3+ ions can be a tuning parameter, herewith the dopant ratio has an actual importance on the magnetic characterization of M-DNA nanowire (3%–20%). We propose the source of magnetism as an exchange interaction between Cr and DNA helical atoms indicated in the Heisenberg Hamiltonian. [ABSTRACT FROM AUTHOR]