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Background DNA binding with one finger (Dof) transcription factors play important roles in plant growth and abiotic stress responses. Although genome-wide identification and analysis of the DOF transcription factor family has been reported in other species, no relevant studies have emerged in wheat. The aim of this study was to investigate the evolutionary and functional characteristics associated with plant growth and abiotic stress responses by genome-wide analysis of the wheat Dof transcription factor gene family. Results Using the recently released wheat genome database (IWGSC RefSeq v1.0), we identified 96 wheat Dof gene family members, which were phylogenetically clustered into five distinct subfamilies. Gene duplication analysis revealed a broad and heterogeneous distribution of TaDofs on the chromosome groups 1 to 7, and obvious tandem duplication genes were present on chromosomes 2 and 3.Members of the same gene subfamily had similar exon-intron structures, while members of different subfamilies had obvious differences. Functional divergence analysis indicated that type-II functional divergence played a major role in the differentiation of the TaDof gene family. Positive selection analysis revealed that the Dof gene family experienced different degrees of positive selection pressure during the process of evolution, and five significant positive selection sites (30A, 31 T, 33A, 102G and 104S) were identified. Additionally, nine groups of coevolving amino acid sites, which may play a key role in maintaining the structural and functional stability of Dof proteins, were identified. The results from the RNA-seq data and qRT-PCR analysis revealed that TaDof genes exhibited obvious expression preference or specificity in different organs and developmental stages, as well as in diverse abiotic stress responses. Most TaDof genes were significantly upregulated by heat, PEG and heavy metal stresses. Conclusions The genome-wide analysis and identification of wheat DOF transcription factor family and the discovery of important amino acid sites are expected to provide new insights into the structure, evolution and function of the plant Dof gene family.

Background The accessibility of sequencing technologies has enabled meta-transcriptomic studies to provide a deeper understanding of microbial ecology at the transcriptional level. Analyzing omics data involves multiple steps that require the use of various bioinformatics tools. With the increasing availability of public microbiome datasets, conducting meta-analyses can reveal new insights into microbiome activity. However, the reproducibility of data is often compromised due to variations in processing methods for sample omics data. Therefore, it is essential to develop efficient analytical workflows that ensure repeatability, reproducibility, and the traceability of results in microbiome research. Results We developed metaTP, a pipeline that integrates bioinformatics tools for analyzing meta-transcriptomic data comprehensively. The pipeline includes quality control, non-coding RNA removal, transcript expression quantification, differential gene expression analysis, functional annotation, and co-expression network analysis. To quantify mRNA expression, we rely on reference indexes built using protein-coding sequences, which help overcome the limitations of database analysis. Additionally, metaTP provides a function for calculating the topological properties of gene co-expression networks, offering an intuitive explanation for correlated gene sets in high-dimensional datasets. The use of metaTP is anticipated to support researchers in addressing microbiota-related biological inquiries and improving the accessibility and interpretation of microbiota RNA-Seq data. Conclusions We have created a conda package to integrate the tools into our pipeline, making it a flexible and versatile tool for handling meta-transcriptomic sequencing data. The metaTP pipeline is freely available at: https://github.com/nanbei45/metaTP .

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript’s expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Aquaporins (AQPs) are one diverse family of membrane channel proteins that play crucial regulatory roles in plant stress physiology. However, the heat stress responsiveness of AQP genes in soybean remains poorly understood. In this study, 75 non-redundant AQP encoding genes were identified in soybean. Multiple sequence alignments showed that all GmAQP proteins possessed the conserved regions, which contained 6 trans-membrane domains (TM1 to TM6). Different GmAQP members consisted of distinct Asn-Pro-Ala (NPA) motifs, aromatic/arginine (ar/R) selectivity filters and Froger’s positions (FPs). Phylogenetic analyses distinguished five sub-families within these GmAQPs: 24 GmPIPs, 24 GmTIPs, 17 GmNIPs, 8 GmSIPs, and 2 GmXIPs. Promoter cis-acting elements analyses revealed that distinct number and composition of heat stress and hormone responsive elements existed in different promoter regions of GmAQPs. QRT-PCR assays demonstrated that 12 candidate GmAQPs with relatively extensive expression in various tissues or high expression levels in root or leaf exhibited different expression changes under heat stress and hormone cues (abscisic acid (ABA), l-aminocyclopropane-l-carboxylic acid (ACC), salicylic acid (SA) and methyl jasmonate (MeJA)). Furthermore, the promoter activity of one previously functionally unknown AQP gene-GmTIP2;6 was investigated in transgenic Arabidopsis plants. The beta-glucuronidase (GUS) activity driven by the promoter of GmTIP2;6 was strongly induced in the heat- and ACC-treated transgenic plants and tended to be accumulated in the hypocotyls, vascular bundles, and leaf trichomes. These results will contribute to uncovering the potential functions and molecular mechanisms of soybean GmAQPs in mediating heat stress and hormone signal responses.

Background Riccia fluitans , an amphibious liverwort, exhibits a fascinating adaptation mechanism to transition between terrestrial and aquatic environments. Utilizing nanopore direct RNA sequencing, we try to capture the complex epitranscriptomic changes undergone in response to land-water transition. Results A significant finding is the identification of 45 differentially expressed genes (DEGs), with a split of 33 downregulated in terrestrial forms and 12 upregulated in aquatic forms, indicating a robust transcriptional response to environmental changes. Analysis of N6-methyladenosine (m6A) modifications revealed 173 m6A sites in aquatic and only 27 sites in the terrestrial forms, indicating a significant increase in methylation in the former, which could facilitate rapid adaptation to changing environments. The aquatic form showed a global elongation bias in poly(A) tails, which is associated with increased mRNA stability and efficient translation, enhancing the plant’s resilience to water stress. Significant differences in polyadenylation signals were observed between the two forms, with nine transcripts showing notable changes in tail length, suggesting an adaptive mechanism to modulate mRNA stability and translational efficiency in response to environmental conditions. This differential methylation and polyadenylation underline a sophisticated layer of post-transcriptional regulation, enabling Riccia fluitans to fine-tune gene expression in response to its living conditions. Conclusions These insights into transcriptome dynamics offer a deeper understanding of plant adaptation strategies at the molecular level, contributing to the broader knowledge of plant biology and evolution. These findings underscore the sophisticated post-transcriptional regulatory strategies Riccia fluitans employs to navigate the challenges of aquatic versus terrestrial living, highlighting the plant’s dynamic adaptation to environmental stresses and its utility as a model for studying adaptation mechanisms in amphibious plants.

Plant resistance against biotic stressors is significantly influenced by pathogenesis-related 1 (PR1) proteins. This study examines the systematic identification and characterization of PR1 family genes in sugarcane (Saccharum spontaneum Np-X) and the transcript expression of selected genes in two sugarcane cultivars (ROC22 and Zhongtang3) in response to Ustilago scitaminea pathogen infection. A total of 18 SsnpPR1 genes were identified at the whole-genome level and further categorized into four groups. Notably, tandem and segmental duplication occurrences were detected in one and five SsnpPR1 gene pairs, respectively. The SsnpPR1 genes exhibited diverse physio-chemical attributes and variations in introns/exons and conserved motifs. Notably, four SsnpPR1 (SsnpPR1.02/05/09/19) proteins displayed a strong protein–protein interaction network. The transcript expression of three SsnpPR1 (SsnpPR1.04/06/09) genes was upregulated by 1.2–2.6 folds in the resistant cultivar (Zhongtang3) but downregulated in the susceptible cultivar (ROC22) across different time points as compared to the control in response to pathogen infection. Additionally, SsnpPR1.11 was specifically upregulated by 1.2–3.5 folds at 24–72 h post inoculation (hpi) in ROC22, suggesting that this gene may play an important negative regulatory role in defense responses to pathogen infection. The genetic improvement of sugarcane can be facilitated by our results, which also establish the basis for additional functional characterization of SsnpPR1 genes in response to pathogenic stress.

Purpose Guidelines by professional organizations for assessing variant pathogenicity include the recommendation to utilize biologically relevant transcripts; however, there is variability in transcript selection by laboratories. Methods We describe three patients whose genomic results were incorrect, because alternative transcripts and tissue expression patterns were not considered by the commercial laboratories. Results In individual 1, a pathogenic coding variant in a brain-expressed isoform of CKDL5 was missed twice on sequencing, because the variant was intronic in the transcripts considered in analysis. In individual 2, a microdeletion affecting KMT2C was not reported on microarray, since deletions of proximal exons in this gene are seen in healthy individuals; however, this individual had a more distal deletion involving the brain-expressed KMT2C isoform, giving her a diagnosis of Kleefstra syndrome. Individual 3 was reported to have a pathogenic variant in exon 10 of OFD1 on exome, but had no typical features of the OFD1 -related disorders. Since exon 10 is spliced from the more biologically relevant transcripts of OFD1 , it was determined that he did not have an OFD1 disorder. Conclusion These examples illustrate the importance of considering alternative transcripts as a potential confounder when genetic results are negative or discordant with the phenotype.

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis. Methods To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models. Results From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2–202 and SPON2–203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development. Conclusion Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2–202, SPON2–203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers

Animals strongly rely on chemical senses to uncover the outside world and adjust their behaviour. Chemical signals are perceived by facial sensitive chemosensors that can be clustered into three families, namely the gustatory (TASR), olfactory (OR, TAAR) and pheromonal (VNR, FPR) receptors. Over recent decades, chemoreceptors were identified in non-facial parts of the body, including the brain. In order to map chemoreceptors within the encephalon, we performed a study based on four brain atlases. The transcript expression of selected members of the three chemoreceptor families and their canonical partners was analysed in major areas of healthy and demented human brains. Genes encoding all studied chemoreceptors are transcribed in the central nervous system, particularly in the limbic system. RNA of their canonical transduction partners (G proteins, ion channels) are also observed in all studied brain areas, reinforcing the suggestion that cerebral chemoreceptors are functional. In addition, we noticed that: (i) bitterness-associated receptors display an enriched expression, (ii) the brain is equipped to sense trace amines and pheromonal cues and (iii) chemoreceptor RNA expression varies with age, but not dementia or brain trauma. Extensive studies are now required to further understand how the brain makes sense of endogenous chemicals.

The entomopathogenic nematode Heterorhabditis bacteriophora , symbiotically associated with enterobacteria of the genus Photorhabdus , is a biological control agent against many insect pests. Dauer Juveniles (DJ) of this nematode are produced in industrial-scale bioreactors up to 100 m 3 in liquid culture processes lasting approximately 11 days. A high DJ yield (> 200,000 DJ·mL −1 ) determines the success of the process. To start the mass production, a DJ inoculum proceeding from a previous monoxenic culture is added to pre-cultured (24 h) Photorhabdus bacteria. Within minutes after contact with the bacteria, DJ are expected to perceive signals that trigger their further development (DJ recovery) to reproductive hermaphrodites. A rapid, synchronized, and high DJ recovery is a key factor for an efficient culture process. In case of low percentage of DJ recovery, the final DJ yield is drastically reduced, and the amount of non-desired stages (males and non-fertilized females) hinders the DJ harvest. In a preliminary work, a huge DJ recovery phenotypic variability in H. bacteriophora ethyl methanesulphonate (EMS) mutants was determined. In the present study, two EMS-mutant lines (M31 and M88) with high and low recovery phenotypes were analyzed concerning their differences in gene expression during the first hours of contact with Photorhabdus supernatant containing food signals triggering recovery. A snapshot (RNA-seq analysis) of their transcriptome was captured at 0.5, 1, 3 and 6 h after exposure. Transcripts (3060) with significant regulation changes were identified in the two lines. To analyze the RNA-seq data over time, we (1) divided the expression profiles into clusters of similar regulation, (2) identified over and under-represented gene ontology categories for each cluster, (3) identified Caenorhabditis elegans homologous genes with recovery-related function, and (4) combined the information with available single nucleotide polymorphism (SNP) data. We observed that the expression dynamics of the contrasting mutants (M31 and M88) differ the most within the first 3 h after Photorhabdus supernatant exposure, and during this time, genes related to changes in the DJ cuticle and molting are more active in the high-recovery line (M31). Comparing the gene expression of DJ exposed to the insect food signal in the haemolymph, genes related to host immunosuppressive factors were not found in DJ upon bacterial supernatant exposure. No link between the position of SNPs associated with high recovery and changes in gene expression was determined for genes with high differential expression. Concerning specific transcripts, nine H. bacteriophora gene models with differential expression are provided as candidate genes for further studies.