Academic literature on the topic 'Bioinformatics and computational biology not elsewhere classified'

Abstract Motivation Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets. Results In this work, we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified. Availability and implementation The implementation of VPF-Class can be downloaded from https://github.com/biocom-uib/vpf-tools. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Motivation Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction. Results This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes. Availability and implementation https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI. Supplementary information Supplementary data are available at Bioinformatics online.

Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric cancer were analyzed, screened and mined through modeling and network modeling to find valuable data associated with multi-drug resistance of gastric cancer. Methods: First, data sets were preprocessed from three aspects: data processing, data annotation and classification, and functional clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory network was constructed by mining interactions among the genes. This paper computed the values of each node in each classified primary gene regulatory network and ranked these nodes according to their scores. On the basis of this, the appropriate core node was selected and the corresponding core network was developed. Results and Conclusion:: Finally, core network modules were analyzed, which were mined. After the correlation analysis, the result showed that the constructed network module had 20 core genes. This module contained valuable data associated with multi-drug resistance in gastric cancer.

Abstract Summary Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease. Availability and implementation https://www.genomedetective.com/app/typingtool/cov Contact koen@emweb.be or deoliveira@ukzn.ac.za Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. Results We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Motivation More than half of the human proteome contains the proline-rich motif, PxxP. This motif has a high propensity for adopting a left-handed polyproline II (PPII) helix and can potentially bind SH3 domains. SH3 domains are generally grouped into two classes, based on whether the PPII binds in a positive (N-to-C terminal) or negative (C-to-N terminal) orientation. Since the discovery of this structural motif, over six decades ago, a systematic understanding of its binding remains poor and the consensus amino acid sequence that binds SH3 domains is still ill defined. Results Here, we show that the PPII interaction with SH3 domains is governed by the helix backbone and its prolines, and their rotation angle around the PPII helical axis. Based on a geometric analysis of 131 experimentally solved SH3 domains in complex with PPIIs, we observed a rotary translation along the helical screw axis, and separated them by 120° into three categories we name α (0–120°), β (120–240°) and γ (240–360°). Furthermore, we found that PPII helices are distinguished by a shifting PxxP motif preceded by positively charged residues which act as a structural reading frame and dictates the organization of SH3 domains; however, there is no one single consensus motif for all classified PPIIs. Our results demonstrate a remarkable apparatus of a lock with a rotating and translating key with no known equivalent machinery in molecular biology. We anticipate our model to be a starting point for deciphering the PPII code, which can unlock an exponential growth in our understanding of the relationship between protein structure and function. Availability and implementation We have implemented the proposed methods in the R software environment and in an R package freely available at https://github.com/Grantlab/bio3d. Supplementary information Supplementary data are available at Bioinformatics online.

Background: The comparison of the protein sequences is an important research filed in bioinformatics. Many alignment-free methods have been proposed. Objective: In order to mining the more information of the protein sequence, this study focus on a new alignment-free method based on physiochemical properties of amino acids. Methods: Average physiochemical value (Apv) has been defined. For a given protein sequence, a 2D curve was outlined based on Apv and position of the amino acid, and there is not loop and intersection on the curve. According to the curve, the similarity/dissimilarity of the protein sequences can be analyzed. Results and Conclusion: Two groups of protein sequences are taken as examples to illustrate the new methods, the protein sequences can be classified correctly, and the results are highly correlated with that of ClustalW. The new method is simple and effective.

Background: Blattella germanica is a widespread urban invader insect that can spread numerous types of human pathogens, including bacteria, fungi, and protozoa. Despite the medical significance of B. germanica, the genetic diversity of this species has not been investigated across its wide geographical distribution in China. Objective: In this study, the genetic variation of B. germanica was evaluated in central China. Methods: Fragments of the mitochondrial cytochrome c oxidase subunit I (COI) gene and the 16S rRNA gene were amplified in 36 B. germanica isolates from 7 regions. The sequence data for COI and 16S rRNA genes were analyzed using bioinformatics methods. Results: In total, 13 haplotypes were found among the concatenated sequences. Each sampled population, and the total population, had high haplotype diversity (Hd) that was accompanied by low nucleotide diversity (Pi). Molecular genetic variation analysis indicated that 84.33% of the genetic variation derived from intra-region sequences. Phylogenetic analysis indicated that the B. germanica isolates from central China should be classified as a single population. Demographic analysis rejected the hypothesis of sudden population expansion of the B. germanica population. Conclusion: The 36 isolates of B. germanica sampled in this study had high genetic variation and belonged to the same species. They should be classified as a single population. The mismatch distribution analysis and BSP analysis did not support a demographic population expansion of the B. germanica population, which provided useful knowledge for monitoring changes in parasite populations for future control strategies.

The current coronavirus disease 2019 (COVID-19) outbreak is alarmingly escalating and raises challenges in finding efficient compounds for treatment. Repurposing phytochemicals in herbs is an ideal and economical approach for screening potential herbal components against COVID-19. Andrographis paniculata, also known as Chuan Xin Lian, has traditionally been used as an anti-inflammatory and antibacterial herb for centuries and has recently been classified as a promising herbal remedy for adjuvant therapy in treating respiratory diseases. This study aimed to screen Chuan Xin Lian’s bioactive components and elicit the potential pharmacological mechanisms and plausible pathways for treating COVID-19 using network pharmacology combined with molecular docking. The results found terpenoid (andrographolide) and flavonoid (luteolin, quercetin, kaempferol, and wogonin) derivatives had remarkable potential against COVID-19 and sequelae owing to their high degrees in the component-target-pathway network and strong binding capacities in docking scores. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the PI3K-AKT signaling pathway might be the most vital molecular pathway in the pathophysiology of COVID-19 and long-term sequelae whereby therapeutic strategies can intervene.

Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions. Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.

Background Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes. Methods Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference). Results We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups. Conclusions We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

Environmental DNA (eDNA) is being used increasingly for biomonitoring of communities (e.g., microbes, macroinvertebrates, fish species) across terrestrial and aquatic ecosystems. Developing methods that combine eDNA approaches with metagenomic barcoded amplicon sequencing (eDNA-metabarcoding) are now providing a powerful noninvasive and cost-effective means for comprehensively surveying biodiversity in a wide range of habitats. Invasive species have a substantial impact on the ecology and economics of the Great Lakes region, and eDNAmetabarcoding methods have recently been applied in monitoring non-native, as well as native, fish populations in the freshwater systems there. In this research, we validated an eDNAmetabarcoding approach that uses established platforms, the MiFish/MitoFish pipeline, for fish community monitoring on Lake Michigan. For validation, we compared survey results from our eDNA-metabarcoding approach to those obtained using traditional surveys (e.g., electrofishing and seining). We also sampled a closed 180,000-gallon freshwater fish tank system to see how well our methods characterized a known native fish population that resided in the tank. Finally, we applied the approach to monitoring invasive and native fish populations in southern Lake Michigan at a site that is currently undergoing restoration to improve the aquatic habitats.. We were able to reliably capture the fish community structure of the native fish tank as well as those of open waters on the lake using our methods. Diversity patterns detected at the restoration site using our eDNA-metabarcoding approach accurately reflected those of the historical record, which have taken many years to establish by conventional means. Overall, this study suggests eDNAmetabarcoding is an efficient, credible, and powerful approach to biomonitoring.

Worldwide, as many as 1 billion people suffer from neurological disorders. Fundamentally, neurological disorders are caused by dysregulation of biochemical signaling within neurons, leading to deficits in learning and memory formation. To identify better preventative and therapeutic strategies for patients of neurological disorders, we require a better understanding of how biochemical signaling is regulated within neurons.

Biochemical signaling at the connections between neurons, called synapses, regulates dynamic shifts in a synapse’s size and connective strength. Called synaptic plasticity, these shifts are initiated by calcium ion (Ca²⁺) flux into message-receiving structures called dendritic spines. Within dendritic spines, Ca²⁺ binds sensor proteins such as calmodulin (CaM). Importantly, Ca²⁺/CaM may bind and activate a wide variety of proteins, which subsequently facilitate signaling pathways regulating the dendritic spine’s size and connective strength.

In this thesis, I use computational models to characterize molecular mechanisms regulating Ca²⁺-dependent protein signaling within the dendritic spine. Specifically, I explore how Ca²⁺/CaM differentially activates binding partners and how these binding partners transduce signals downstream. For this, I present deterministic models of Ca²⁺, CaM, and CaM-dependent proteins, and in analyzing model output I demonstrate in-part that competition for CaM-binding alone may be sufficient to set the Ca²⁺ frequency-dependence of protein activation. Subsequently, I adapt my deterministic models into particle-based, spatial-stochastic frameworks to quantify how spatial effects influence model output, showing evidence that spatial gradients of Ca²⁺/CaM may set spatial gradients of activated proteins downstream. Additionally, I incorporate into my models the most detailed model to-date of Ca²⁺/CaM-dependent protein kinase II (CaMKII), a multi-subunit protein essential to synaptic plasticity. With this detailed model of CaMKII, my analysis suggests that the many subunits of CaMKII provide avidity effects that significantly increase the protein’s effective affinity for binding partners, particularly Ca²⁺/CaM. Altogether, this thesis provides a detailed analysis of Ca²⁺-dependent signaling within dendritic spines, characterizing molecular mechanisms that may be useful for the development of novel therapeutics for patients of neurological disorders.

Regulation of metabolism in mammalian cells is achieved through a complex interplay between cellular signaling, metabolic reactions, and transcriptional changes. The modeling of metabolic fluxes in a cell requires the knowledge of all these mechanisms, some of which may be unknown. A cybernetic approach provides a framework to model these complex interactions through the implicit accounting of such regulatory mechanisms, assuming a biological “goal”. The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena ranging from complex substrate uptake patterns and dynamic metabolic flux distributions to the behavior of gene knockout strains. The premise underlying the cybernetic framework is that the regulatory processes affecting metabolism can be mathematically formulated as a cybernetic objective through variables that constrain the network to achieve a specified biological “goal”.

Cybernetic theory builds on the perspective that regulation is organized towards achieving goals relevant to an organism’s survival or displaying a specific phenotype in response to a stimulus. While cybernetic models have been established by prior work carried out in bacterial systems, we show its applicability to more complex biological systems with a predefined goal. We have modeled eicosanoid, a well-characterized set of inflammatory lipids derived from arachidonic acid, metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA, a chemical analogue of Lipopolysaccharide found on the surface of bacterial cells) and adenosine triphosphate (ATP, a danger signal released in response to surrounding cell death) using cybernetic control variables. Here, the cybernetic goal is inflammation; the hallmark of inflammation is the expression of cytokines which act as autocrine signals to stimulate a pro-inflammatory response. Tumor necrosis factor (TNF)-α is an exemplary pro-inflammatory marker and can be designated as a cybernetic objective for modeling eicosanoid—prostaglandin (PG) and leukotriene (LK)—metabolism. Transcriptomic and lipidomic data for eicosanoid biosynthesis and conversion were obtained from the LIPID Maps database. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation using the treatment ATP stimulation. We then validated our model by predicting an independent data set, the PG response of KLA primed ATP stimulated BMDM cells.

The process of inflammation is mediated by the production of multiple cytokines, chemokines, and lipid mediators each of which contribute to specific individual objectives. For such complex processes in mammalian systems, a cybernetic objective based on a single protein/component may not be sufficient to capture all the biological processes thereby necessitating the use of multiple objectives. The choice of the objective function has been made by intuitive considerations in this thesis. If objectives are conjectured, an argument can be made for numerous alternatives. Since regulatory effects are estimated from unregulated kinetics, one encounters the risk of multiplicity in this regard giving rise to multiple models. The best model is of course that which is able to predict a comprehensive set of perturbations. Here, we have extended our above model to also capture the dynamics of LKs. We have used migration as a biological goal for LK using the chemoattractant CCL2 as a key representative molecule describing cell activation leading to an inflammatory response where a goal composed of multiple cybernetic objectives is warranted. Alternative model objectives included relating both branches of the eicosanoid metabolic network to the inflammatory cytokine TNF-α, as well as the simple maximization of all metabolic products such that each equally contributes to the inflammatory system outcome. We were again able to show that all three cybernetic objectives describing the LK and PG branches for eicosanoid metabolism capture the complex regulation and provide a reliable description of eicosanoid formation. We performed simulated drug and gene perturbation analyses on the system to identify differences between the models and propose additional experiments to select the best cybernetic model.

The advantage to using cybernetic modeling is in its ability to capture system behavior without the same level of detail required for these interactions as standard kinetic modeling. Given the complexity of mammalian systems, the cybernetic goal for mammalian cells may not be based solely on survival or growth but on specific context dependent cellular responses. In this thesis, we have laid the groundwork for the application of cybernetic modeling in complex mammalian systems through a specific example case of eicosanoid metabolism in BMDM cells, illustrated the case for multiple objectives, and highlighted the extensibility of the cybernetic framework to other complex biological systems.

Prostate cancer is the second leading cause of cancer death among men in the United States. The androgen receptor (AR) antagonist enzalutamide is an FDA-approved drug for treatment of patients with late-stage prostate cancer and is currently under clinical study for early-stage prostate cancer treatment. After a short positive response period to enzalutamide, tumors will develop drug resistance. In these studies, we uncovered that NOTCH signaling and DNA methylation are a deregulated in enzalutamide-resistant cells. NOTCH2 and c-MYC gene expression positively correlated with AR expression in samples from patients with hormone refractory disease in which AR expression levels correspond to those typically observed in enzalutamide-resistance. The expression of Notch signaling components was upregulated in enzalutamide-resistant cells suggesting the activation of the pathway. Inhibition of this pathway in vitro and in vivo promoted an increase in the sensitivity to enzalutamide with an impact on AR expression. On the other hand, DNMT activity and DNMT3B expression were upregulated in resistant lines. Enzalutamide induced the expression of DNMT3A and DNMT3B in prostate cancer cells with a potential role for p53 and pRB in this process. The overexpression of DNMT3B3, a DNMT3B variant, promoted an enzalutamide-resistant phenotype in C4-2 cells. DNA methylation inhibition, using low-concentration decitabine, and DNMT3B knockdown induced a re-sensitization of resistant prostate cancer cells and tumors to enzalutamide. Decitabine treatment in enzalutamide-resistant induced a decrease in the expression of AR-V7 and changes in genes from the apoptosis, DNA repair and mRNA splicing pathways. Decitabine plus enzalutamide treatment of 22RV1 xenografts induced a decrease in tumor weight, KI-67 and AR-V7 expression and an increase in Cleaved-Caspase3 levels. All the above suggest that Notch signaling and DNA methylation pathways are deregulated after enzalutamide resistance onset, and targeting these pathways restores the sensitivity to enzalutamide.

The Mediator (Med) complex comprises about 30 subunits and is a transcriptional co-regulator in eukaryotic systems. The core Mediator complex, consisting of the head, middle and tail modules, functions as a bridge between transcription factors and basal transcription machinery, whereas the CDK8 kinase module can attenuate Mediator’s ability to function as either a co-activator or co-repressor. Many Arabidopsis Mediator subunit has been functionally characterized, which reveals critical roles of Mediator in many aspects of plant growth and development, responses to biotic and abiotic stimuli, and metabolic homeostasis. Traditional genetic and biochemical approaches laid the foundation for our understanding of Mediator function, but recent transcriptomic and metabolomic studies have provided deeper insights into how specific subunits cooperate in the regulation of plant metabolism. In Chapter 1, we highlight recent developments in the investigation of Mediator and plant metabolism, with emphasis on the large-scale biology studies of med mutants.

We previously found that MED5, an Arabidopsis Mediator tail subunit, is required for maintaining phenylpropanoid homeostasis. A semi-dominant mutation (reduced epidermal fluorescence 4-3, ref4-3) that causes a single amino acid substitution in MED5b functions as a strong suppressor of the pathway, leading to decreased soluble phenylpropanoid accumulation, reduced lignin content and dwarfism. In contrast, loss of MED5a and MED5b (med5) results in increased levels of phenylpropanoids. In Chapter 2, we present our finding that ref4-3 requires CDK8, a Mediator kinase module subunit, to repress plant growth even though the repression of phenylpropanoid metabolism in ref4-3 is CDK8-independent. Transcriptome profiling revealed that salicylic acid (SA) biosynthesis genes are up-regulated in a CDK8-dependent manner in ref4-3, resulting in hyper-accumulation of SA and up-regulation of SA response genes. Both growth repression and hyper-accumulation of SA in ref4-3 require CDK8 with intact kinase activity, but these SA phenotypes are not connected with dwarfing. In contrast, mRNA-sequencing (RNA-seq) analysis revealed the up-regulation of a DNA J protein-encoding gene in ref4-3, the elimination of which partially suppresses dwarfing. Together, our study reveals genetic interactions between Mediator tail and kinase module subunits and enhances our understanding of dwarfing in phenylpropanoid pathway mutants.

In Chapter 3, we characterize other phenotypes of med5 and ref4-3, and find that in addition to the up-regulated phenylpropanoid metabolism, med5 show other interesting phenotypes including hypocotyl and petiole elongation as well as accelerated flowering, all of which are known collectively as the shade avoidance syndrome (SAS), suggesting that MED5 antagonize shade avoidance in wild-type plants. In contrast, the constitutive ref4-3 mutant protein inhibits the process, and the stunted growth of ref4-3 mutants is substantially alleviated by the light treatment that triggers SAS. Moreover, ref4-3 mimics the loss-of-function med5 mutants in maintaining abscisic acid (ABA) levels under both normal and drought growth conditions. The phenotypic characterization of med5 mutants extend our understanding of the role of Mediator in SAS and ABA signaling, providing further insight into the physiological and metabolic responses that require MED5.

In Chapter 4, we explore the function of MED5 and CDK8 in gene expression regulation by investigating the effect of mutations in Mediator including med5, ref4-3, cdk8-1 and ref4-3 cdk8-1 on genome-wide Pol II distribution. We find that loss of MED5 results in loss of Pol II occupancy at many target genes. In contrast, many genes show enriched Pol II levels in ref4-3, some of which overlap with those showing reduced Pol II occupancy in med5. In addition, Pol II occupancy is significantly reduced when CDK8 is disrupted in ref4-3. Our results help to narrow down the direct gene targets of MED5 and identify genes that may be closely related to the growth deficiency observed in ref4-3 plants, providing a critical foundation to elucidate the molecular function of Mediator in transcription regulation.