The CD4 Versus CD8 T Cell Fate Decision: A Multiomics-Informed Perspective.

The choice of developing thymocytes to become CD8+ cytotoxic or CD4+ helper T cells has been intensely studied, but many of the underlying mechanisms remain to be elucidated. Recent multiomics approaches have provided much higher resolution analysis of gene expression in developing thymocytes than was previously achievable, thereby offering a fresh perspective on this question. Focusing on our recent studies using CITE-seq (cellular indexing of transcriptomes and epitopes) analyses of mouse thymocytes, we present a detailed timeline of RNA and protein expression changes during CD8 versus CD4 T cell differentiation. We also revisit our current understanding of the links between T cell receptor signaling and expression of the lineage-defining transcription factors ThPOK and RUNX3. Finally, we propose a sequential selection model to explain the tight linkage between MHC-I versus MHC-II recognition and T cell lineage choice. This model incorporates key aspects of previously proposed kinetic signaling, instructive, and stochastic/selection models.

Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2.

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.

Molecular and Spatial Signatures of Mouse Embryonic Endothelial Cells at Single-Cell Resolution.

BACKGROUND: Mature endothelial cells (ECs) are heterogeneous, with subtypes defined by tissue origin and position within the vascular bed (ie, artery, capillary, vein, and lymphatic). How this heterogeneity is established during the development of the vascular system, especially arteriovenous specification of ECs, remains incompletely characterized. METHODS: We used droplet-based single-cell RNA sequencing and multiplexed error-robust fluorescence in situ hybridization to define EC and EC progenitor subtypes from E9.5, E12.5, and E15.5 mouse embryos. We used trajectory inference to analyze the specification of arterial ECs (aECs) and venous ECs (vECs) from EC progenitors. Network analysis identified candidate transcriptional regulators of arteriovenous differentiation, which we tested by CRISPR (clustered regularly interspaced short palindromic repeats) loss of function in human-induced pluripotent stem cells undergoing directed differentiation to aECs or vECs (human-induced pluripotent stem cell-aECs or human-induced pluripotent stem cell-vECs). RESULTS: From the single-cell transcriptomes of 7682 E9.5 to E15.5 ECs, we identified 19 EC subtypes, including Etv2+Bnip3+ EC progenitors. Spatial transcriptomic analysis of 15 448 ECs provided orthogonal validation of these EC subtypes and established their spatial distribution. Most embryonic ECs were grouped by their vascular-bed types, while ECs from the brain, heart, liver, and lung were grouped by their tissue origins. Arterial (Eln, Dkk2, Vegfc, and Egfl8), venous (Fam174b and Clec14a), and capillary (Kcne3) marker genes were identified. Compared with aECs, embryonic vECs and capillary ECs shared fewer markers than their adult counterparts. Early capillary ECs with venous characteristics functioned as a branch point for differentiation of aEC and vEC lineages. CONCLUSIONS: Our results provide a spatiotemporal map of embryonic EC heterogeneity at single-cell resolution and demonstrate that the diversity of ECs in the embryo arises from both tissue origin and vascular-bed position. Developing aECs and vECs share common venous-featured capillary precursors and are regulated by distinct transcriptional regulatory networks.

Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis.

Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.

Directed Differentiation of Human Induced Pluripotent Stem Cells to Heart Valve Cells.

BACKGROUND: A main obstacle in current valvular heart disease research is the lack of high-quality homogeneous functional heart valve cells. Human induced pluripotent stem cells (hiPSCs)-derived heart valve cells may help with this dilemma. However, there are no well-established protocols to induce hiPSCs to differentiate into functional heart valve cells, and the networks that mediate the differentiation have not been fully elucidated. METHODS: To generate heart valve cells from hiPSCs, we sequentially activated the Wnt, BMP4, VEGF (vascular endothelial growth factor), and NFATc1 signaling pathways using CHIR-99021, BMP4, VEGF-165, and forskolin, respectively. The transcriptional and functional similarity of hiPSC-derived heart valve cells compared with primary heart valve cells were characterized. Longitudinal single-cell RNA sequencing was used to uncover the trajectory, switch genes, pathways, and transcription factors of the differentiation. RESULTS: An efficient protocol was developed to induce hiPSCs to differentiate into functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells. After 6-day differentiation and CD144 magnetic bead sorting, ≈70% CD144+ cells and 30% CD144- cells were obtained. On the basis of single-cell RNA sequencing data, the CD144+ cells and CD144- cells were found to be highly similar to primary heart valve endothelial cells and primary heart valve interstitial cells in gene expression profile. Furthermore, CD144+ cells had the typical function of primary heart valve endothelial cells, including tube formation, uptake of low-density lipoprotein, generation of endothelial nitric oxide synthase, and response to shear stress. Meanwhile, CD144- cells could secret collagen and matrix metalloproteinases, and differentiate into osteogenic or adipogenic lineages like primary heart valve interstitial cells. Therefore, we identified CD144+ cells and CD144- cells as hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells, respectively. Using single-cell RNA sequencing analysis, we demonstrated that the trajectory of heart valve cell differentiation was consistent with embryonic valve development. We identified the main switch genes (NOTCH1, HEY1, and MEF2C), signaling pathways (TGF-ß, Wnt, and NOTCH), and transcription factors (MSX1, SP5, and MECOM) that mediated the differentiation. Finally, we found that hiPSC-derived valve interstitial-like cells might derive from hiPSC-derived valve endothelial-like cells undergoing endocardial-mesenchymal transition. CONCLUSIONS: In summary, this is the first study to report an efficient strategy to generate functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells from hiPSCs, as well as to elucidate the differentiation trajectory and transcriptional dynamics of hiPSCs differentiated into heart valve cells.

Interferon-γ inducible factor 16 (IFI16) restricts adeno-associated virus type 2 (AAV2) transduction in an immune-modulatory independent way.

We determined the transcription profile of adeno-associated virus type 2 (AAV2)-infected primary human fibroblasts. Subsequent analysis revealed that cells respond to AAV infection through changes in several significantly affected pathways, including cell cycle regulation, chromatin modulation, and innate immune responses. Various assays were performed to validate selected differentially expressed genes and to confirm not only the quality but also the robustness of the raw data. One of the genes upregulated in AAV2-infected cells was interferon-γ inducible factor 16 (IFI16). IFI16 is known as a multifunctional cytosolic and nuclear innate immune sensor for double-stranded as well as single-stranded DNA, exerting its effects through various mechanisms, such as interferon response, epigenetic modifications, or transcriptional regulation. IFI16 thereby constitutes a restriction factor for many different viruses among them, as shown here, AAV2 and thereof derived vectors. Indeed, the post-transcriptional silencing of IFI16 significantly increased AAV2 transduction efficiency, independent of the structure of the virus/vector genome. We also show that IFI16 exerts its inhibitory effect on AAV2 transduction in an immune-modulatory independent way by interfering with Sp1-dependent transactivation of wild-type AAV2 and AAV2 vector promoters. IMPORTANCE: Adeno-associated virus (AAV) vectors are among the most frequently used viral vectors for gene therapy. The lack of pathogenicity of the parental virus, the long-term persistence as episomes in non-proliferating cells, and the availability of a variety of AAV serotypes differing in their cellular tropism are advantageous features of this biological nanoparticle. To deepen our understanding of virus-host interactions, especially in terms of antiviral responses, we present here the first transcriptome analysis of AAV serotype 2 (AAV2)-infected human primary fibroblasts. Our findings indicate that interferon-γ inducible factor 16 acts as an antiviral factor in AAV2 infection and AAV2 vector-mediated cell transduction in an immune-modulatory independent way by interrupting the Sp1-dependent gene expression from viral or vector genomes.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Division-Independent Differentiation of Muscle Stem Cells During a Growth Stimulus.

Adult muscle stem cells (MuSCs) are known to replicate upon activation before differentiating and fusing to regenerate myofibers. It is unclear whether MuSC differentiation is intrinsically linked to cell division, which has implications for stem cell population maintenance. We use single-cell RNA-sequencing to identify transcriptionally diverse subpopulations of MuSCs after 5 days of a growth stimulus in adult muscle. Trajectory inference in combination with a novel mouse model for tracking MuSC-derived myonuclei and in vivo labeling of DNA replication revealed an MuSC population that exhibited division-independent differentiation and fusion. These findings demonstrate that in response to a growth stimulus in the presence of intact myofibers, MuSC division is not obligatory.

Single-Cell Transcriptome Reveals Potential Mechanisms for Coronary Artery Lesions in Kawasaki Disease.

BACKGROUND: Coronary artery lesions (CALs) are the most common and major complication of Kawasaki disease (KD) in developed countries. However, the underlying immunologic mechanisms of CAL development in KD remain unclear. METHODS: Here, we conducted single-cell transcriptome analyses of 212 210 peripheral blood mononuclear cells collected from a cross-sectional cohort of 16 children, including 4 patients with KD with CALs, 5 patients with KD without CALs, 4 healthy controls, and 3 febrile controls. RESULTS: KD altered the proportion of peripheral blood mononuclear cells, including an increasing trend in inflammatory cells (megakaryocytes and monocytes) and a decreasing trend in lymphocytes (eg, CD4+ T, CD8+ T, mucosal-associated invariant T, natural killer, and γδ T cells), highlighting the potential presence of lymphopenia phenomenon in KD. Our data indicated the presence of inflammatory cytokine storm in patients with KD with CALs, caused by systemic upregulation of TNFSF13B (tumor necrosis factor superfamily member 13b), CXCL16 (C-X-C motif chemokine ligand 16), TNFSF10 (tumor necrosis factor superfamily member 10), and IL1RN (interleukin 1 receptor antagonist), mainly produced by monocytes (especially for the Mono_CD14-CD16 cluster) and megakaryocytes. We also found that myeloid cells of patients with KD, particularly in those with CALs, might play a role in vascular injury (eg, increased MMP [matrix metalloproteinase] 9, MMP17, and MMP25) and immune cell recruitment. The immune landscape of patients with KD with CALs was featured by lower exhaustion levels in natural killer cells, a high cytotoxic state in the CD8_Pro cluster, and activation of the complement system in monocytes. Additionally, the activation of B cells was more pronounced in the early stage of KD. CONCLUSIONS: Collectively, this study provides a comprehensive understanding of the roles of various immune cells and inflammatory cytokine storms in the development of CALs in KD and offers a valuable resource for identifying novel therapeutic targets for patients with KD with CALs.

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops.

BACKGROUND: Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions. RESULTS: Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production. CONCLUSIONS: This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Unravelling the immune signature of herpes zoster: Insights into pathophysiology and the HLA risk profile.

The varicella-zoster virus (VZV) infects over 95% of the population. VZV reactivation causes herpes zoster (HZ), known as shingles, primarily affecting the elderly and immunocompromised individuals. However, HZ can also occur in otherwise healthy individuals. We analyzed the immune signature and risk profile in HZ patients using a genome-wide association study across different UK Biobank HZ cohorts. Additionally, we conducted one of the largest HZ HLA association studies to date, coupled with transcriptomic analysis of pathways underlying HZ susceptibility. Our findings highlight the significance of the MHC locus for HZ development, identifying five protective and four risk HLA alleles. This demonstrates that HZ susceptibility is largely governed by variations in the MHC. Furthermore, functional analyses revealed the upregulation of type I interferon and adaptive immune responses. These findings provide fresh molecular insights into the pathophysiology and the activation of innate and adaptive immune responses triggered by symptomatic VZV reactivation.

RoPE: A robust profile likelihood method for differential gene expression analysis.

Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.

Targeting Epsins to Inhibit Fibroblast Growth Factor Signaling While Potentiating Transforming Growth Factor-ß Signaling Constrains Endothelial-to-Mesenchymal Transition in Atherosclerosis.

BACKGROUND: Epsin endocytic adaptor proteins are implicated in the progression of atherosclerosis; however, the underlying molecular mechanisms have not yet been fully defined. In this study, we determined how epsins enhance endothelial-to-mesenchymal transition (EndoMT) in atherosclerosis and assessed the efficacy of a therapeutic peptide in a preclinical model of this disease. METHODS: Using single-cell RNA sequencing combined with molecular, cellular, and biochemical analyses, we investigated the role of epsins in stimulating EndoMT using knockout in Apoe-/- and lineage tracing/proprotein convertase subtilisin/kexin type 9 serine protease mutant viral-induced atherosclerotic mouse models. The therapeutic efficacy of a synthetic peptide targeting atherosclerotic plaques was then assessed in Apoe-/- mice. RESULTS: Single-cell RNA sequencing and lineage tracing revealed that epsins 1 and 2 promote EndoMT and that the loss of endothelial epsins inhibits EndoMT marker expression and transforming growth factor-ß signaling in vitro and in atherosclerotic mice, which is associated with smaller lesions in the Apoe-/- mouse model. Mechanistically, the loss of endothelial cell epsins results in increased fibroblast growth factor receptor-1 expression, which inhibits transforming growth factor-ß signaling and EndoMT. Epsins directly bind ubiquitinated fibroblast growth factor receptor-1 through their ubiquitin-interacting motif, which results in endocytosis and degradation of this receptor complex. Consequently, administration of a synthetic ubiquitin-interacting motif-containing peptide atheroma ubiquitin-interacting motif peptide inhibitor significantly attenuates EndoMT and progression of atherosclerosis. CONCLUSIONS: We conclude that epsins potentiate EndoMT during atherogenesis by increasing transforming growth factor-ß signaling through fibroblast growth factor receptor-1 internalization and degradation. Inhibition of EndoMT by reducing epsin-fibroblast growth factor receptor-1 interaction with a therapeutic peptide may represent a novel treatment strategy for atherosclerosis.

Genome-wide identification of SWEET genes reveals their roles during seed development in peanuts.

Sugar Will Eventually be Exported Transporter (SWEET) proteins are highly conserved in various organisms and play crucial roles in sugar transport processes. However, SWEET proteins in peanuts, an essential leguminous crop worldwide, remain lacking in systematic characterization. Here, we identified 94 SWEET genes encoding the conservative MtN3/saliva domains in three peanut species, including 47 in Arachis hypogea, 23 in Arachis duranensis, and 24 in Arachis ipaensis. We observed significant variations in the exon-intron structure of these genes, while the motifs and domain structures remained highly conserved. Phylogenetic analysis enabled us to categorize the predicted 286 SWEET proteins from eleven species into seven distinct groups. Whole genome duplication/segment duplication and tandem duplication were the primary mechanisms contributing to the expansion of the total number of SWEET genes. In addition, an investigation of cis-elements in the potential promoter regions and expression profiles across 22 samples uncovered the diverse expression patterns of AhSWEET genes in peanuts. AhSWEET24, with the highest expression level in seeds from A. hypogaea Tifrunner, was observed to be localized on both the plasma membrane and endoplasmic reticulum membrane. Moreover, qRT-PCR results suggested that twelve seed-expressed AhSWEET genes were important in the regulation of seed development across four different peanut varieties. Together, our results provide a foundational basis for future investigations into the functions of SWEET genes in peanuts, especially in the process of seed development.

Genome-wide identification of JAZ gene family members in autotetraploid cultivated alfalfa (Medicago sativa subsp. sativa) and expression analysis under salt stress.

BACKGROUND: Jasmonate ZIM-domain (JAZ) proteins, which act as negative regulators in the jasmonic acid (JA) signalling pathway, have significant implications for plant development and response to abiotic stress. RESULTS: Through a comprehensive genome-wide analysis, a total of 20 members of the JAZ gene family specific to alfalfa were identified in its genome. Phylogenetic analysis divided these 20 MsJAZ genes into five subgroups. Gene structure analysis, protein motif analysis, and 3D protein structure analysis revealed that alfalfa JAZ genes in the same evolutionary branch share similar exon‒intron, motif, and 3D structure compositions. Eight segmental duplication events were identified among these 20 MsJAZ genes through collinearity analysis. Among the 32 chromosomes of the autotetraploid cultivated alfalfa, there were 20 MsJAZ genes distributed on 17 chromosomes. Extensive stress-related cis-acting elements were detected in the upstream sequences of MsJAZ genes, suggesting that their response to stress has an underlying function. Furthermore, the expression levels of MsJAZ genes were examined across various tissues and under the influence of salt stress conditions, revealing tissue-specific expression and regulation by salt stress. Through RT‒qPCR experiments, it was discovered that the relative expression levels of these six MsJAZ genes increased under salt stress. CONCLUSIONS: In summary, our study represents the first comprehensive identification and analysis of the JAZ gene family in alfalfa. These results provide important information for exploring the mechanism of JAZ genes in alfalfa salt tolerance and identifying candidate genes for improving the salt tolerance of autotetraploid cultivated alfalfa via genetic engineering in the future.

Genome-wide identification, characterization, and expression analysis of MIPS family genes in legume species.

BACKGROUND: Evolutionarily conserved in plants, the enzyme D-myo-inositol-3-phosphate synthase (MIPS; EC 5.5.1.4) regulates the initial, rate-limiting reaction in the phytic acid biosynthetic pathway. They are reported to be transcriptional regulators involved in various physiological functions in the plants, growth, and biotic/abiotic stress responses. Even though the genomes of most legumes are fully sequenced and available, an all-inclusive study of the MIPS family members in legumes is still ongoing. RESULTS: We found 24 MIPS genes in ten legumes: Arachis hypogea, Cicer arietinum, Cajanus cajan, Glycine max, Lablab purpureus, Medicago truncatula, Pisum sativum, Phaseolus vulgaris, Trifolium pratense and Vigna unguiculata. The total number of MIPS genes found in each species ranged from two to three. The MIPS genes were classified into five clades based on their evolutionary relationships with Arabidopsis genes. The structural patterns of intron/exon and the protein motifs that were conserved in each gene were highly group-specific. In legumes, MIPS genes were inconsistently distributed across their genomes. A comparison of genomes and gene sequences showed that this family was subjected to purifying selection and the gene expansion in MIPS family in legumes was mainly caused by segmental duplication. Through quantitative PCR, expression patterns of MIPS in response to various abiotic stresses, in the vegetative tissues of various legumes were studied. Expression pattern shows that MIPS genes control the development and differentiation of various organs, and have significant responses to salinity and drought stress. CONCLUSION: The MIPS genes in the genomes of legumes have been identified, characterized and their expression was analysed. The findings pave way for understanding their molecular functions and evolution, and lead to identify the putative MIPS genes associated with different cell and tissue development.

Genome-wide identification and expression analyses of CYP450 genes in sweet potato (Ipomoea batatas L.).

BACKGROUND: Cytochrome P450 monooxygenases (CYP450s) play a crucial role in various biochemical reactions involved in the synthesis of antioxidants, pigments, structural polymers, and defense-related compounds in plants. As sweet potato (Ipomoea batatas L.) holds significant economic importance, a comprehensive analysis of CYP450 genes in this plant species can offer valuable insights into the evolutionary relationships and functional characteristics of these genes. RESULTS: In this study, we successfully identified and categorized 95 CYP450 genes from the sweet potato genome into 5 families and 31 subfamilies. The predicted subcellular localization results indicate that CYP450s are distributed in the cell membrane system. The promoter region of the IbCYP450 genes contains various cis-acting elements related to plant hormones and stress responses. In addition, ten conserved motifs (Motif1-Motif10) have been identified in the IbCYP450 family proteins, with 5 genes lacking introns and only one exon. We observed extensive duplication events within the CYP450 gene family, which may account for its expansion. The gene duplication analysis results showed the presence of 15 pairs of genes with tandem repeats. Interaction network analysis reveals that IbCYP450 families can interact with multiple target genes and there are protein-protein interactions within the family. Transcription factor interaction analysis suggests that IbCYP450 families interact with multiple transcription factors. Furthermore, gene expression analysis revealed tissue-specific expression patterns of CYP450 genes in sweet potatoes, as well as their response to abiotic stress and plant hormones. Notably, quantitative real-time polymerase chain reaction (qRT‒PCR) analysis indicated the involvement of CYP450 genes in the defense response against nonbiological stresses in sweet potatoes. CONCLUSIONS: These findings provide a foundation for further investigations aiming to elucidate the biological functions of CYP450 genes in sweet potatoes.

Genome-wide analysis of filamentous temperature-sensitive H protease (ftsH) gene family in soybean.

BACKGROUND: The filamentous temperature-sensitive H protease (ftsH) gene family belongs to the ATP-dependent zinc metalloproteins, and ftsH genes play critical roles in plant chloroplast development and photosynthesis. RESULTS: In this study, we performed genome-wide identification and a systematic analysis of soybean ftsH genes. A total of 18 GmftsH genes were identified. The subcellular localization was predicted to be mainly in cell membranes and chloroplasts, and the gene structures, conserved motifs, evolutionary relationships, and expression patterns were comprehensively analyzed. Phylogenetic analysis of the ftsH gene family from soybean and various other species revealed six distinct clades, all of which showed a close relationship to Arabidopsis thaliana. The members of the GmftsH gene family were distributed on 13 soybean chromosomes, with intron numbers ranging from 3 to 15, 13 pairs of repetitive segment. The covariance between these genes and related genes in different species of Oryza sativa, Zea mays, and Arabidopsis thaliana was further investigated. The transcript expression data revealed that the genes of this family showed different expression patterns in three parts, the root, stem, and leaf, and most of the genes were highly expressed in the leaves of the soybean plants. Fluorescence-based real-time quantitative PCR (qRT-PCR) showed that the expression level of GmftsH genes varied under different stress treatments. Specifically, the genes within this family exhibited various induction levels in response to stress conditions of 4℃, 20% PEG-6000, and 100 mmol/L NaCl. These findings suggest that the GmftsH gene family may play a crucial role in the abiotic stress response in soybeans. It was also found that the GmftsH7 gene was localized on the cell membrane, and its expression was significantly upregulated under 4 ℃ treatment. In summary, by conducting a genome-wide analysis of the GmftsH gene family, a strong theoretical basis is established for future studies on the functionality of GmftsH genes. CONCLUSIONS: This research can potentially serve as a guide for enhancing the stress tolerance characteristics of soybean.