Dynamic Gene Expression Patterns Research Articles

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objectiveSystemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns. MethodologyThis paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method. ResultsThe proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies–Bouldin clustering index and a ∼60% increase in the classification specificity score. ConclusionA novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

3D genomic alterations during development of skeletal muscle in chicken1

The development of skeletal muscle are complicated processes involving genes responsible for proper muscle morphology, contractility, cell proliferation, differentiation, interactions, migration, and death. The three-dimensional chromatin architecture of skeletal muscle development has not been studied intensively although dynamic transcriptional regulation during differentiation of muscle cells is one of the most deeply studied processes. The RNA-seq was used to analyze the transcriptome pattern during chicken muscle development across 12 stages. Hi-C was used to build a chromatin architectures during four representative stages. ChIP-seq was conducted to identify enhancers in these four stages, which are occupied by histone H3K27ac and H3K4me3 peaks. Results show that large-scale genome architecture changes are mostly unidirectional, and coupled by complex on/off dynamic patterns of gene expression. Specifically, we observed 258.30 Mb of the genome undergoing A/B compartment switching. Notable alterations (316.57 Mb) of interaction frequencies within TADs were observed. Substantial aging-associated genes exhibited ascending connectivity with the compartment transition from repressive to active status during muscle development. Some muscle-related gene promoters that interacted with active enhancers during development, and some myopathy/aging-associated genes that were activated in aging muscle were founded. These results provide key insights into skeletal muscle development in vivo, and offer a valuable resource that allows in-depth functional characterization of candidate genes.

Trajectory Inference Highlights Dynamic Expression of T-Cell Development Genes in Pediatric T-Cell Acute Lymphoblastic Leukemia

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is a rare cancer with a diverse set of known drivers. Although genomic mutations driving T-ALL have been extensively characterized, these molecular subtypes do not inform patient treatment selection. In order to improve the utility of genomic data for pediatric T-ALL patients, further study is required to understand the molecular hallmarks and behaviors of T-ALL cells. One tool to investigate the behavior of subpopulations of cells is trajectory inference, which uses single-cell resolution data to infer putative relationships between cell populations, providing insight into cancer cell evolution. This method has the potential to identify subsets of cells that may respond particularly well or poorly to a given treatment based on their molecular profile, providing additional resolution in the effort to characterize T-ALL subtypes. In this study, we used the 10x Chromium system to perform single-cell RNA sequencing (scRNAseq) of blood and/or bone marrow samples (n = 12) collected at diagnosis from eight pediatric T-ALL patients (Figure A). We generated sequencing data for a total of 21,116 cells and performed standard scRNAseq processing and normalization using Seurat 4.0.2 in R 4.0.3. Subsequently, we filtered down to cancer cell cluster(s) for each patient sample and used Slingshot 2.4.0 to identify cluster similarity and tradeSeq 1.10.0 to fit a generalized additive model for each putative trajectory and identify genes whose expression changed across each trajectory. Each sample had 1-5 predicted trajectories for a total of 24 trajectories across the dataset. Each trajectory had 85-958 genes changing expression along it (total 3482 genes across the dataset). Nearly 40% of these genes only showed expression changes along a single trajectory, suggesting a high degree of heterogeneity not only across patients but also within individual patient samples. 6.5% of genes in this analysis show expression changes across at least 10/24 trajectories. These include genes known to be drivers of T-ALL, such as BCL11B and ETV6, demonstrating the ability of trajectory inference to capture known facets of T-ALL biology. Genes known to be involved in T-cell commitment ( RUNX1, TCF7) and cellular self-renewal ( MEIS1, MEF2C) showed changes in expression across a subset of trajectories as well. Other genes of interest including proto-oncogenes such as JUNB also show changes in expression across some trajectories but not others, even within a single patient (Figure B). The data presented here demonstrate the value of trajectory inference methods to explore dynamic patterns of gene expression in scRNAseq data from pediatric T-ALL patients. By identifying putative trajectories within individual patients and comparing gene expression across these trajectories, we are able to more clearly resolve unique biological processes occurring in leukemic cells and potentially giving rise to treatment-resistant populations. Further interrogation of the genes identified in this analysis, correlation with known molecular subtypes, and extension to larger cohorts will improve our understanding of the molecular interactions driving disease progression, and ultimately improve treatment selection and prognosis for pediatric T-ALL patients.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

Single-cell profiling of peripheral blood and muscle cells reveals inflammatory features of juvenile dermatomyositis.

Introduction: Juvenile dermatomyositis (JDM) is a rare yet serious childhood systemic autoimmune condition that primarily causes skin rashes and inflammatory myopathy of the proximal muscles. Although the associated immune response involves the innate and adaptive arms, a detailed analysis of the pertinent immune cells remains to be performed. This study aims to investigate the dynamic changes of cell type, cell composition and transcriptional profiles in peripheral blood and muscle tissues, and in order to clarify the involvement of immune cells in the pathogenesis of JDM and provide a theoretical reference for JDM. Methods: Single-cell RNA sequencing combined with bioinformatic analyses were used to investigate the dynamic changes in cell composition and transcriptional profiles. Results: Analysis of 45,859 cells revealed nine and seven distinct cell subsets in the peripheral blood and muscle tissues respectively. IFITM2+ and CYP4F3+ monocytes were largely produced, and CD74+ smooth muscle cells (SMCs) and CCL19+ fibroblasts were identified as inflammatory-related cell subtypes in JDM patients, exhibiting patient-specific cell population heterogeneity.The dynamic gene expression patterns presented an enhanced type I interferon response in peripheral blood monocytes and T-cells, and SMCs and fibroblasts in muscle of untreated JDM patients. EGR1 and IRF7 may play central roles in the inflammation in both CD74+ SMCs and CCL19+ fibroblasts. Moreover, inflammatory-related monocytes could regulate T-cells, and the interaction between immune cells and SMCs or fibroblasts in muscle was enhanced under the inflammatory state. Conclusions: Immune dysregulation is one of the key pathogenic factors of JDM, and type I interferon responses are significantly enhanced in peripheral blood Monos and T cells as well as SMCs and fibroblasts. EGR1 and IRF7 may play central roles in the inflammation and are considered as potential therapeutic targets for JDM.

Transcription factors regulating vasculogenesis and angiogenesis.

Transcription factors (TFs) play a crucial role in regulating the dynamic and precise patterns of gene expression required for the initial specification of endothelial cells (ECs), and during endothelial growth and differentiation. While sharing many core features, ECs can be highly heterogeneous. Differential gene expression between ECs is essential to pattern the hierarchical vascular network into arteries, veins and capillaries, to drive angiogenic growth of new vessels, and to direct specialization in response to local signals. Unlike many other cell types, ECs have no single master regulator, instead relying on differing combinations of a necessarily limited repertoire of TFs to achieve tight spatial and temporal activation and repression of gene expression. Here, we will discuss the cohort of TFs known to be involved in directing gene expression during different stages of mammalian vasculogenesis and angiogenesis, with a primary focus on development.

Dynamic patterns of gene expression and regulatory variation in the maize seed coat

BackgroundSeed size is an important factor contributing to maize yield, but its molecular mechanism remains unclear. The seed coat, which serves as one of the three components of the maize grain, determines seed size to a certain extent. The seed coat also shares the maternal genotype and is an ideal material for studying heterosis.ResultsIn this study, the self-pollinated seeds of the maize hybrid Yudan888 and its parental lines were continuously collected from 0 day after pollination (DAP) to 15 DAP for phenotyping, cytological observation and RNA-seq. The phenotypic data showed that 3 DAP and 8 DAP are the best time points to study maize seed coat heterosis. Cytological observations indicated that maize seed coat heterosis might be the result of the coordination between cell number and cell size. Furthermore, the RNA-seq results showed that the nonadditive genes changed significantly between 3 and 8 DAP. However, the number of genes expressed additively was not significantly different. Our findings suggest that seed coat heterosis in hybrid is the result of nonadditive expression caused by dynamic changes in genes at different time points during seed expansion and seed coat development. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment indicated that genes related to DNA replication, cell cycle regulation, circadian rhythms and metabolite accumulation contributed significantly to hybrid seed coat heterosis.ConclusionMaize seed coat phenotyping allowed us to infer that 3 DAP and 8 DAP are important time points in the study of seed coat heterosis. Our findings provide evidence for genes involved in DNA replication, cell cycle regulation, circadian rhythms and metabolite accumulation in hybrid with high or low parental expression as major contributors to hybrid seed coat heterosis.

Single-cell transcriptomics reveals zinc and copper ions homeostasis in epicardial adipose tissue of heart failure.

Epicardial adipose tissue (EAT) is a unique visceral fat reservoir that shares an immune microenvironment without a distinct boundary with myocardium. Increasingly, visceral fat has been studied as a secondary immune organ, and EAT is no exception in this regard. Cellular subsets of EAT are associated with disease development. In heart failure (HF) patients, however, the immune characteristics of EAT have rarely been studied, especially those non-immune cells related to the immune microenvironment. Herein, an analysis of seven EAT samples by single-cell RNA sequencing (scRNA-Seq) is presented here, including 1 neonate, 1 infant, 1 child, 2 adults with heart failure (Adults-HF) and 2 adult heart transplant donors as non-heart failure control (Adults-Non HF). Analysis of 51730 high-quality cells revealed eleven major cell types in EAT. For the first time, the pseudo-temporal reconstruction technique was employed to plot the cell trajectories of various major cell types (such as T lymphocytes, fibroblasts, endothelial cells, monocytes, and smooth muscle cells) in EAT across different developmental stages, achieving a single-cell resolution. The dynamic gene expression patterns of major cell types presented the immune characteristics of metabolism disorder of zinc and copper ions, and downregulated immune-related pathways in EAT of adult patients with HF. These data provide insights regarding HF immune dysregulation at the cellular level.

Arginase 1 Expression by Macrophages Promotes Cryptococcus neoformans Proliferation and Invasion into Brain Microvascular Endothelial Cells.

Cryptococcal meningoencephalitis caused by Cryptococcus neoformans infection is the most common cause of death in HIV/AIDS patients. Macrophages are pivotal for the regulation of immune responses to cryptococcal infection by either playing protective function or facilitating fungal dissemination. However, the mechanisms underlying macrophage responses to C. neoformans remain unclear. To analyze the transcriptomic changes and identify the pathogenic factors of macrophages, we performed a comparative transcriptomic analysis of alveolar macrophage responses during C. neoformans infection. Alveolar macrophages isolated from C. neoformans-infected mice showed dynamic gene expression patterns, with expression change from a protective M1 (classically activated)-like to a pathogenic M2 (alternatively activated)-like phenotype. Arg1, the gene encoding the enzyme arginase 1, was found as the most upregulated gene in alveolar macrophages during the chronic infection phase. The in vitro inhibition of arginase activity resulted in a reduction of cryptococcal phagocytosis, intracellular growth, and proliferation, coupled with an altered macrophage response from pathogenic M2 to a protective M1 phenotype. In an in vitro model of the blood-brain barrier, macrophage-derived arginase was found to be required for C. neoformans invasion of brain microvascular endothelium. Further analysis of the degree of virulence indicated a positive correlation between arginase 1 expression in macrophages and cryptococcal brain dissemination in vivo. Thus, our data suggest that a dynamic macrophage activation that involves arginase expression may contribute to the cryptococcal disease by promoting cryptococcal growth, proliferation, and the invasion to the brain endothelium.

MGCEP 1.0: A Genetic-Engineered Marine-Derived Chassis Cell for a Scaled Heterologous Expression Platform of Microbial Bioactive Metabolites.

Marine microorganisms produce a variety of bioactive secondary metabolites, which represent a significant source of novel antibiotics. Heterologous expression is a valuable tool for discovering marine microbial secondary metabolites; however, marine-derived chassis cell is very scarce. Here, we build an efficient plug-and-play marine-derived gene clusters expression platform 1.0 (MGCEP 1.0) by the systematic engineering of the deep-sea-derived Streptomyces atratus SCSIO ZH16. For a proof of concept, four families of microbial bioactive metabolite biosynthetic gene clusters (BGCs), including alkaloids, aminonucleosides, nonribosomal peptides, and polyketides, were efficiently expressed in this platform. Moreover, 19 compounds, including two new angucycline antibiotics, were produced in MGCEP 1.0. Dynamic patterns of global biosynthetic gene expression in MGCEP 1.0 with or without a heterologous gene cluster were revealed at the transcriptome level. The platform MGCEP 1.0 provides new possibilities for expressing microbial secondary metabolites, especially of marine origin.

Dynamic transcriptome and LC-MS/MS analysis revealed the important roles of taurine and glutamine metabolism in response to environmental salinity changes in gills of rainbow trout (Oncorhynchus mykiss)

Recently, the frequent salinity fluctuation has become a growing threat to fishes. However, the dynamic patterns of gene expression in response to salinity changes remain largely unexplored. In the present study, 18 RNA-Seq datasets were generated from gills of rainbow trout at different salinities, including 0 ‰, 6 ‰, 12 ‰, 18 ‰, 24 ‰ and 30 ‰. Based on the strict thresholds, we have identified 63, 1411, 2096, 1031 and 1041 differentially expressed genes in gills of rainbow trout through pairwise comparisons. Additionally, weighted gene co-expression network analysis was performed to construct 18 independent modules with distinct expression patterns. Of them, green and tan modules were found to be tightly related to salinity changes, several hub genes of which are known as the important regulators in taurine and glutamine metabolism. To further investigate their potential roles in response to salinity changes, taurine, glutamine, and their metabolism-related glutamic acid and α-ketoglutaric acid were accurately quantitated using liquid chromatography-tandem mass spectrometry analysis. Results clearly showed that their concentrations were closely associated with salinity changes. These findings suggested that taurine and glutamine play important roles in response to salinity changes in gills of rainbow trout, providing new insights into the molecular mechanism of fishes in salinity adaptation.

Validation of superior reference genes in mouse submandibular glands under developmental and functional regeneration states.

Saliva is crucial for lubricating the mouth and aiding in food digestion. However, the occurrence of oral dysfunction, such as xerostomia, dysphagia or oral infection can markedly reduce the quality of life of affected individuals. The major salivary glands include the submandibular gland (SMG), and sublingual and parotid glands; they are the larger glands in mammals that produce the majority of the saliva. The SMG serves as an effective model for the study of branching morphogenesis and functional regeneration. In order to better understand the key dynamic gene expression patterns during salivary gland development and functional regeneration, it is crucial to search for a panel of reliable reference genes. The present study thus aimed to identify superior reference genes to normalize gene expression data in the SMG under states of development and functional regeneration. First, the developmental SMG samples were harvested from mice in the embryonic and post-natal periods. Functional regeneration samples from a ductal ligation/de-ligation model were obtained at several stages. A total of 12 reference genes (Actb, Actg1, Ubc, Uba1, Uba52, Ube2c, Tuba1a, Tuba1b, Tubb5, H2afy, H2afx and Gapdh) from 430 candidates involving tubulin, histone, actin, ubiquitin and GAPDH family members were screened via transcriptome sequencing (RNA-seq) analysis. RT-qPCR (SYBR-Green) and western blot analysis were then used to semi-quantitatively assess gene and protein expression. The stability of expression was evaluated using the ΔCq, geNorm, BestKeeper, NormFinder and RefFinder methods and software. Actg1 exhibited the highest stability in the SMG developmental stage, while Tubb5 was recommended as the most stable reference gene for the SMG regenerative stage. In summary, the present study provides evidence-based selections for superior reference genes in the SMG during the stages of development and functional regeneration.

Revealing the impacts on shaping scutate scales in goose skin

Feather follicles and scales are two types of skin appendages distributed on different parts of avian skin. The morphogenesis and development of scales in waterfowl remain largely unknown. Here, we used H&E staining, ISH and RNA sequencing to reveal the morphological and molecular variations at the early development of scutate scales in goose shank skin. Transcriptome analysis produced 1824 differentially expressed genes (DEGs) regulating the induction of scales and further enriched gene function in cell adhesion and Wnt signaling pathway, etc. A total of 8 candidate genes (ALDOC, CSRP2, KRT15, KRT75, LGALS1, S100A6, OGN and SFRP2) were further detected by RT-qPCR to show upregulated (6 genes) and downregulated (2 genes) from pre-placodal to placode stage during the induction of goose scales. The localization of 7 candidate genes (ALDOC, CSRP2, CD109, KRT15, KRT75, S100A6, and OGN) by ISH suggests the potential roles for dermal and epidermal development during the induction of scutate scales. The dynamic molecular changes and specific gene expression patterns revealed in this report provide general knowledge of scale development in waterfowl as well as skin appendage diversity.

Construction of the axolotl cell landscape using combinatorial hybridization sequencing at single-cell resolution

The Mexican axolotl (Ambystoma mexicanum) is a well-established tetrapod model for regeneration and developmental studies. Remarkably, neotenic axolotls may undergo metamorphosis, a process that triggers many dramatic changes in diverse organs, accompanied by gradually decline of their regeneration capacity and lifespan. However, the molecular regulation and cellular changes in neotenic and metamorphosed axolotls are still poorly investigated. Here, we develop a single-cell sequencing method based on combinatorial hybridization to generate a tissue-based transcriptomic landscape of the neotenic and metamorphosed axolotls. We perform gene expression profiling of over 1 million single cells across 19 tissues to construct the first adult axolotl cell landscape. Comparison of single-cell transcriptomes between the tissues of neotenic and metamorphosed axolotls reveal the heterogeneity of non-immune parenchymal cells in different tissues and established their regulatory network. Furthermore, we describe dynamic gene expression patterns during limb development in neotenic axolotls. This system-level single-cell analysis of molecular characteristics in neotenic and metamorphosed axolotls, serves as a resource to explore the molecular identity of the axolotl and facilitates better understanding of metamorphosis.

Disorders of histone methylation: Molecular basis and clinical syndromes.

Epigenetic modifications of DNA and histone tails are essential for gene expression regulation. They play an essential role in neurodevelopment as nervous system development is a complex process requiring a dynamic pattern of gene expression. Histone methylation is one of the vital epigenetic regulators and mostly occurs on lysine residues of histones H3 and H4. Histone methylation is catalyzed by two sets of enzymes: histone lysine methyltransferases (KMTs) and histone lysine demethylases (KDMs). KMT2 enzymes form a distinct multi-subunit complex known as COMPASS to enhance their catalytic activity and diversify their biologic functions. Several neurodevelopmental syndromes result from defects in histone methylation which can be caused by deficiencies in histone methyltransferases and demethylases, loss of the histone methyltransferase activator TASP1, or derangements in COMPASS formation. In this review article, the molecular mechanism of histone methylation is discussed followed by summarizing clinical syndromes caused by monogenic defects in histone methylation.

Gpx3 and Egr1 Are Involved in Regulating the Differentiation Fate of Cardiac Fibroblasts under Pressure Overload.

Objectives Although myocardial fibrosis is a common pathophysiological process associated with many heart diseases, the molecular mechanisms regulating the development of fibrosis have not been fully determined. Recently, single cell RNA sequencing (scRNA-seq) analysis has been used to examine cellular fate and function during cellular differentiation and has contributed to elucidating the mechanisms of various diseases. The main purpose of this study was to characterize the fate of cardiac fibroblasts (CFs) and the dynamic gene expression patterns in a model of cardiac pressure overload using scRNA-seq analysis. Methods The public scRNA-seq dataset of the transverse aortic coarctation (TAC) model in mice was downloaded from the GEO database, GSE155882. First, we performed quality control, dimensionality reduction, clustering, and annotation of the data through the Seurat R package (v4.0.5). Then, we constructed the pseudotime trajectory of cell development and identified key regulatory genes using the Monocle R package (v2.22.0). Different cell fates and groups were fully characterized by Gene Set Enrichment Analysis (GSEA) analysis and Transcription factor (TF) activity analysis. Finally, we used Cytoscape (3.9.1) to extensively examine the gene regulatory network related to cell fate. Results Pseudotime analysis showed that CFs differentiated into two distinct cell fates, one of which produced activated myofibroblasts, and the other which produced protective cells that were associated with reduced fibrosis levels, increased antioxidative stress responses, and the ability to promote angiogenesis. In the TAC model, activated CFs were significantly upregulated, while protective cells were downregulated. Treatment with the bromodomain inhibitor JQ1 reversed this change and improved fibrosis. Analysis of dynamic gene expression revealed that Gpx3 was significantly upregulated during cell differentiation into protective cells. Gpx3 expression was affected by JQ1 treatment. Furthermore, Gpx3 expression levels were negatively correlated with the different levels of fibrosis observed in the various treatment groups. Finally, we found that transcription factors Jun, Fos, Atf3, and Egr1 were upregulated in protective cells, especially Egr1 was predicted to be involved in the regulation of genes related to antioxidant stress and angiogenesis, suggesting a role in promoting differentiation into this cell phenotype. Conclusions The scRNA-seq analysis was used to characterize the dynamic changes associated with fibroblast differentiation and identified Gpx3 as a factor that might be involved in the regulation of myocardial fibrosis under cardiac pressure overload. These findings will help to further understanding of the mechanism of fibrosis and provide potential intervention targets.

Generating dynamic gene expression patterns without the need for regulatory circuits.

Synthetic biology has successfully advanced our ability to design and implement complex, time-varying genetic circuits to control the expression of recombinant proteins. However, these circuits typically require the production of regulatory genes whose only purpose is to coordinate expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products while nevertheless encoding complex expression dynamics. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic gene expression patterns. We discover these genetic solutions by computationally evolving genomes to reproduce desired gene expression time-course data. Our approach shows that non-trivial patterns can be evolved, including patterns where the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and comparable expression patterns can be achieved via different genetic architectures. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates.

Heat stress response in Chinese cabbage (Brassica rapa L.) revealed by transcriptome and physiological analysis.

High temperatures have a serious impact on the quality and yield of cold-loving Chinese cabbage, which has evolved to have a unique set of stress mechanisms. To explore the relationship between these mechanisms and the heat-tolerance of Chinese cabbage, the physiological indicators of the heat-tolerant ‘268’ line and heat-sensitive ‘334’ line were measured. Under heat stress, the proline (Pro), soluble sugar (SS), and superoxide dismutase (SOD) indexes of the ‘268’ line increased significantly. When additionally using transcriptome analysis, we found that the identified 3,360 DEGs were abundantly enriched in many metabolic pathways including ‘plant hormone signal transduction’, ‘carbon metabolism’, and ‘glycolysis/gluconeogenesis’. Dynamic gene expression patterns showed that HKL1 in Cluster 15 may be a key factor in the regulation of sugar homeostasis. The interaction network screened four ABA-related genes in Cluster 15, suggesting that high temperatures lead to changes in hormonal signaling, especially an increase in ABA signaling. Compared with the ‘334’ line, the expressions of Prx50, Prx52, Prx54, SOD1, and SOD2 in the ‘268’ line were significantly upregulated, and these genes were actively involved in the reactive oxygen species (ROS) scavenging process. In summary, our results revealed the relationship between plant heat tolerance, physiology, and biochemistry and may also provide ideas for the future development of high-quality and heat-tolerant Chinese cabbage germplasm resources.

The analysis of dynamic gene expression patterns in peripheral blood of multiple sclerosis patients indicates possible diagnostic and prognostic biomarkers

IntroductionAmong numerous invasive procedures for the research of biomarkers, blood-based indicators are regarded as marginally non-invasive procedures in the diagnosis and prognosis of demyelinating disorders, including multiple sclerosis (MS). In this study, we looked into the blood-derived gene expression profiles of patients with multiple sclerosis to investigate their clinical traits and linked them with dysregulated gene expressions to establish diagnostic and prognostic indicators. MethodsWe included 51 patients with relapsing-remitting MS (RRMS, n = 31), clinically isolated syndrome (CIS, n = 12), primary progressive MS (PPMS, n = 8) and a control group (n = 51). Using correlational analysis, the transcriptional patterns of chosen gene panels were examined and subsequently related with disease duration and the expanded disease disability score (EDSS). In addition, principal component analysis, univariate regression, and logistic regression analysis were employed to highlight distinct profiles of genes and prognosticate the excellent biomarkers of this illness. ResultsOur findings demonstrated that neurofilament light (NEFL), tumor necrosis factor α (TNF-α), Tau, and clusterin (CLU) were revealed to be increased in recruited patients, whereas the presenilin-1 (PSEN1) and cell-surface glycoprotein-44 (CD44) were downregulated. Principal Component Analysis revealed distinct patterns between the MS and control groups. Correlation analysis indicated co-dependent dysregulated genes and their differential expression with clinical findings. Furthermore, logistic regression demonstrated that Clusterin (AUC=0.940), NEFL (AUC=0.775), TNF-α (AUC=0.817), Tau (AUC=0.749), PSEN1 (AUC=0.6913), and CD44 (AUC=0.832) had diagnostic relevance. Following the univariate linear regression, a significant regression equation was found between EDSS and IGF-1 (R2 adj = 0.10844; p= 0.0060), APP (R2 adj = 0.1107; p= 0.0098), and PSEN1 (R2 adj = 0.1266; p=0.0102). ConclusionThis study exhibits dynamic gene expression patterns that represent the significance of specified genes that are prospective diagnostic and prognostic biomarkers for multiple sclerosis.