Mechanistic insight into the role of AUXIN RESISTANCE4 in trafficking of AUXIN1 and LIKE AUX1-2.

AUXIN RESISTANCE4 (AXR4) regulates the trafficking of auxin influx carrier AUXIN1 (AUX1), a plasma-membrane protein that predominantly localizes to the endoplasmic reticulum (ER) in the absence of AXR4. In Arabidopsis (Arabidopsis thaliana), AUX1 is a member of a small multigene family comprising 4 highly conserved genes-AUX1, LIKE-AUX1 (LAX1), LAX2, and LAX3. We report here that LAX2 also requires AXR4 for correct localization to the plasma membrane. AXR4 is a plant-specific protein and contains a weakly conserved α/ß hydrolase fold domain that is found in several classes of lipid hydrolases and transferases. We have previously proposed that AXR4 may either act as (i) a post-translational modifying enzyme through its α/ß hydrolase fold domain or (ii) an ER accessory protein, which is a special class of ER protein that regulates targeting of their cognate partner proteins. Here, we show that AXR4 is unlikely to act as a post-translational modifying enzyme as mutations in several highly conserved amino acids in the α/ß hydrolase fold domain can be tolerated and active site residues are missing. We also show that AUX1 and AXR4 physically interact with each other and that AXR4 reduces aggregation of AUX1 in a dose-dependent fashion. Our results suggest that AXR4 acts as an ER accessory protein. A better understanding of AXR4-mediated trafficking of auxin transporters in crop plants will be crucial for improving root traits (designer roots) for better acquisition of water and nutrients for sustainable and resilient agriculture.

Coenzyme A Restriction as a Factor Underlying Pre-Eclampsia with Polycystic Ovary Syndrome as a Risk Factor.

Pre-eclampsia is the most common pregnancy complication affecting 1 in 20 pregnancies, characterized by high blood pressure and signs of organ damage, most often to the liver and kidneys. Metabolic network analysis of published lipidomic data points to a shortage of Coenzyme A (CoA). Gene expression profile data reveal alterations to many areas of metabolism and, crucially, to conflicting cellular regulatory mechanisms arising from the overproduction of signalling lipids driven by CoA limitation. Adverse feedback loops appear, forming sphingosine-1-phosphate (a cause of hypertension, hypoxia and inflammation), cytotoxic isoketovaleric acid (inducing acidosis and organ damage) and a thrombogenic lysophosphatidyl serine. These also induce mitochondrial and oxidative stress, leading to untimely apoptosis, which is possibly the cause of CoA restriction. This work provides a molecular basis for the signs of pre-eclampsia, why polycystic ovary syndrome is a risk factor and what might be done to treat and reduce the risk of disease.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

MOTIVATION: Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle. RESULTS: As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions. AVAILABILITY AND IMPLEMENTATION: The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progress towards platform chemical production using Clostridium autoethanogenum.

Since 2013, there has been an explosion in the number of research articles published on Clostridium autoethanogenum, an acetogen capable of producing platform chemicals such as ethanol and 2,3-butanediol from greenhouse gases. However, no review focusing solely on C. autoethanogenum has appeared in the literature. This review outlines the research conducted into this organism in three broad categories (Enzymology, Genetics, and Systems Biology) and suggestions for future research are offered.

The logic of the floral transition: Reverse-engineering the switch controlling the identity of lateral organs.

Much laboratory work has been carried out to determine the gene regulatory network (GRN) that results in plant cells becoming flowers instead of leaves. However, this also involves the spatial distribution of different cell types, and poses the question of whether alternative networks could produce the same set of observed results. This issue has been addressed here through a survey of the published intercellular distribution of expressed regulatory genes and techniques both developed and applied to Boolean network models. This has uncovered a large number of models which are compatible with the currently available data. An exhaustive exploration had some success but proved to be unfeasible due to the massive number of alternative models, so genetic programming algorithms have also been employed. This approach allows exploration on the basis of both data-fitting criteria and parsimony of the regulatory processes, ruling out biologically unrealistic mechanisms. One of the conclusions is that, despite the multiplicity of acceptable models, an overall structure dominates, with differences mostly in alternative fine-grained regulatory interactions. The overall structure confirms the known interactions, including some that were not present in the training set, showing that current data are sufficient to determine the overall structure of the GRN. The model stresses the importance of relative spatial location, through explicit references to this aspect. This approach also provides a quantitative indication of how likely some regulatory interactions might be, and can be applied to the study of other developmental transitions.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data.

BACKGROUND: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. RESULTS AND DISCUSSION: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large 'omics' datasets are increasingly being used in the area of rheumatology. CONCLUSIONS: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

Whole genome sequence and manual annotation of Clostridium autoethanogenum, an industrially relevant bacterium.

BACKGROUND: Clostridium autoethanogenum is an acetogenic bacterium capable of producing high value commodity chemicals and biofuels from the C1 gases present in synthesis gas. This common industrial waste gas can act as the sole energy and carbon source for the bacterium that converts the low value gaseous components into cellular building blocks and industrially relevant products via the action of the reductive acetyl-CoA (Wood-Ljungdahl) pathway. Current research efforts are focused on the enhancement and extension of product formation in this organism via synthetic biology approaches. However, crucial to metabolic modelling and directed pathway engineering is a reliable and comprehensively annotated genome sequence. RESULTS: We performed next generation sequencing using Illumina MiSeq technology on the DSM10061 strain of Clostridium autoethanogenum and observed 243 single nucleotide discrepancies when compared to the published finished sequence (NCBI: GCA_000484505.1), with 59.1 % present in coding regions. These variations were confirmed by Sanger sequencing and subsequent analysis suggested that the discrepancies were sequencing errors in the published genome not true single nucleotide polymorphisms. This was corroborated by the observation that over 90 % occurred within homopolymer regions of greater than 4 nucleotides in length. It was also observed that many genes containing these sequencing errors were annotated in the published closed genome as encoding proteins containing frameshift mutations (18 instances) or were annotated despite the coding frame containing stop codons, which if genuine, would severely hinder the organism's ability to survive. Furthermore, we have completed a comprehensive manual curation to reduce errors in the annotation that occur through serial use of automated annotation pipelines in related species. As a result, different functions were assigned to gene products or previous functional annotations rejected because of missing evidence in various occasions. CONCLUSIONS: We present a revised manually curated full genome sequence for Clostridium autoethanogenum DSM10061, which provides reliable information for genome-scale models that rely heavily on the accuracy of annotation, and represents an important step towards the manipulation and metabolic modelling of this industrially relevant acetogen.

Modulation of Arabidopsis and monocot root architecture by CLAVATA3/EMBRYO SURROUNDING REGION 26 peptide.

Plant roots are important for a wide range of processes, including nutrient and water uptake, anchoring and mechanical support, storage functions, and as the major interface with the soil environment. Several small signalling peptides and receptor kinases have been shown to affect primary root growth, but very little is known about their role in lateral root development. In this context, the CLE family, a group of small signalling peptides that has been shown to affect a wide range of developmental processes, were the focus of this study. Here, the expression pattern during lateral root initiation for several CLE family members is explored and to what extent CLE1, CLE4, CLE7, CLE26, and CLE27, which show specific expression patterns in the root, are involved in regulating root architecture in Arabidopsis thaliana is assessed. Using chemically synthesized peptide variants, it was found that CLE26 plays an important role in regulating A. thaliana root architecture and interacts with auxin signalling. In addition, through alanine scanning and in silico structural modelling, key residues in the CLE26 peptide sequence that affect its activity are pinpointed. Finally, some interesting similarities and differences regarding the role of CLE26 in regulating monocot root architecture are presented.

Tackling drought stress: receptor-like kinases present new approaches.

Global climate change and a growing population require tackling the reduction in arable land and improving biomass production and seed yield per area under varying conditions. One of these conditions is suboptimal water availability. Here, we review some of the classical approaches to dealing with plant response to drought stress and we evaluate how research on RECEPTOR-LIKE KINASES (RLKs) can contribute to improving plant performance under drought stress. RLKs are considered as key regulators of plant architecture and growth behavior, but they also function in defense and stress responses. The available literature and analyses of available transcript profiling data indeed suggest that RLKs can play an important role in optimizing plant responses to drought stress. In addition, RLK pathways are ideal targets for nontransgenic approaches, such as synthetic molecules, providing a novel strategy to manipulate their activity and supporting translational studies from model species, such as Arabidopsis thaliana, to economically useful crops.

Network inference analysis identifies an APRR2-like gene linked to pigment accumulation in tomato and pepper fruits.

Carotenoids represent some of the most important secondary metabolites in the human diet, and tomato (Solanum lycopersicum) is a rich source of these health-promoting compounds. In this work, a novel and fruit-related regulator of pigment accumulation in tomato has been identified by artificial neural network inference analysis and its function validated in transgenic plants. A tomato fruit gene regulatory network was generated using artificial neural network inference analysis and transcription factor gene expression profiles derived from fruits sampled at various points during development and ripening. One of the transcription factor gene expression profiles with a sequence related to an Arabidopsis (Arabidopsis thaliana) ARABIDOPSIS PSEUDO RESPONSE REGULATOR2-LIKE gene (APRR2-Like) was up-regulated at the breaker stage in wild-type tomato fruits and, when overexpressed in transgenic lines, increased plastid number, area, and pigment content, enhancing the levels of chlorophyll in immature unripe fruits and carotenoids in red ripe fruits. Analysis of the transcriptome of transgenic lines overexpressing the tomato APPR2-Like gene revealed up-regulation of several ripening-related genes in the overexpression lines, providing a link between the expression of this tomato gene and the ripening process. A putative ortholog of the tomato APPR2-Like gene in sweet pepper (Capsicum annuum) was associated with pigment accumulation in fruit tissues. We conclude that the function of this gene is conserved across taxa and that it encodes a protein that has an important role in ripening.

Validation of proteomic biomarkers previously found to be differentially expressed in women with polycystic ovary syndrome: a cross-sectional study.

OBJECTIVES: The aim of this study was to independently validate proteomic biomarkers previously reported to be differentially expressed in women with Polycystic Ovary Syndrome (PCOS) compared with controls. This study focused on plasma proteomic biomarkers. METHODS: This was a cross-sectional study at the University of Nottingham, in which samples from 30 PCOS and 30 control women were analysed by Western blotting. RESULTS: Mean abundance ratios from Western blots of plasma total haptoglobin and haptoglobin beta proteins were 1.25 (CI 1.11-1.4) and 1.24 (CI 1.04-1.44). The mean abundance ratio from the blots of alpha-2 macroglobulin was however 1.05 (CI, 1-1.1). The mean PCOS/control BMI ratio was 1.18 (CI 1.17-1.20). There was no correlation between PCOS/control BMI ratio and alpha-2 macroglobulin, total haptoglobin and haptoglobin beta abundance ratios. There was also no correlation between PCOS/control insulin ratio and alpha-2 macroglobulin, total haptoglobin and haptoglobin beta abundance ratios. CONCLUSIONS: Total haptoglobin and haptoglobin beta chain protein abundance was found to be elevated in women with PCOS compared with controls. We were unable to confirm decreased alpha-2 macroglobulin levels as reported in a previous study. Independent validation studies are required to validate early promising proteomic biomarkers in PCOS.

Transcriptomic analysis identifies four novel receptors potentially linking endometrial cancer with polycystic ovary syndrome and generates a transcriptomic atlas.

Polycystic Ovary Syndrome (PCOS) is associated with a 3 to 4-fold increased risk of endometrial cancer (EC), but molecular mechanisms are unclear. Upregulation of the IGF1 gene in PCOS endometrium may increase EC risk, but this is uncertain. We aimed to investigate links between EC and PCOS, by analysing publicly available transcriptomic data. The NCBI Gene Expression Omnibus was used to identify relevant studies. Differentially expressed genes (DEGs) were identified and analysed using Metascape to identify pathways of interest. PCOS DEGs that encode proteins secreted into blood were identified using the Human Protein Atlas blood protein database. EC DEGs that are cellular receptors were identified using EcoTyper. These were intersected to identify which EC receptors interact with PCOS secreted proteins. Seven receptors were identified in EC but only PTPRF, ITGA2, ITGA3 and ITGB4 genes were expressed on epithelial cells. Pathway enrichment of these genes showed that the major and common pathway involved was that of the PI3K-AKT signalling pathway which was consistent with a link between PCOS and EC. However, IGF1 was down regulated in PCOS and EC. These findings hold significant promise for improving our understanding of mechanistic pathways leading to EC in PCOS.

Unraveling the evolution of auxin signaling.

Auxin signaling is central to plant growth and development, yet hardly anything is known about its evolutionary origin. While the presence of key players in auxin signaling has been analyzed in various land plant species, similar analyses in the green algal lineages are lacking. Here, we survey the key players in auxin biology in the available genomes of Chlorophyta species. We found that the genetic potential for auxin biosynthesis and AUXIN1 (AUX1)/LIKE AUX1- and P-GLYCOPROTEIN/ATP-BINDING CASSETTE subfamily B-dependent transport is already present in several single-celled and colony-forming Chlorophyta species. In addition, our analysis of expressed sequence tag libraries from Coleochaete orbicularis and Spirogyra pratensis, green algae of the Streptophyta clade that are evolutionarily closer to the land plants than those of the Chlorophyta clade, revealed the presence of partial AUXIN RESPONSE FACTORs and/or AUXIN/INDOLE-3-ACETIC ACID proteins (the key factors in auxin signaling) and PIN-FORMED-like proteins (the best-characterized auxin-efflux carriers). While the identification of these possible AUXIN RESPONSE FACTOR- and AUXIN/INDOLE-3-ACETIC ACID precursors and putative PIN-FORMED orthologs calls for a deeper investigation of their evolution after sequencing more intermediate genomes, it emphasizes that the canonical auxin response machinery and auxin transport mechanisms were, at least in part, already present before plants "moved" to land habitats.

Polycystic Ovarian Syndrome (PCOS): Does the Challenge End at Conception?

Polycystic ovary syndrome (PCOS) is a prevalent condition that not only has the potential to impede conception but also represents the most common endocrine dysfunction in fertile women. It is considered a heterogeneous and multifaceted disorder, with multiple reproductive and metabolic phenotypes which differently affect the early- and long-term syndrome's risks. Undoubtedly, the impact of PCOS on infertility has attracted most of the attention of healthcare providers and investigators. However, there is growing evidence that even after conception is achieved, PCOS predisposes the parturient to several adverse pregnancy outcomes including a high risk of pregnancy-induced hypertension, spontaneous abortion, gestational diabetes, preeclampsia, and preterm birth, which increase the risks of stillbirth and neonatal death. Fetal growth abnormalities may also be more common, but the relationship is less well defined. This narrative review aims to summarize current knowledge regarding these conditions as they interplay with PCOS and concludes that although there appears to be an increase in these complications during the pregnancy of women with PCOS, there is a need for further research to clarify the possible confounding impact of obesity. Implications for clinical practice and future research are outlined.

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis.

BACKGROUND: Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems. RESULTS: We have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters. CONCLUSIONS: Relationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

Occurrence of a quadruplex motif in a unique insert within exon C of the bovine estrogen receptor alpha gene (ESR1).

The 5' end of exon C of the bovine estrogen receptor alpha gene (bov-ESR1) includes a unique G-rich insert, not found in other closely related mammalian genes, which lies close to both a double E-box transcription factor binding site and the site of a single nucleotide (G/A) polymorphism. Biophysical studies, using CD and UV absorbance measurements, show that this 22 base insert leads to the formation of a family of stable G-quadruplex structures which are unaffected by the G/A polymorphism. Multiplex PCR shows that the region including the G-quadruplex is transcribed into RNA, and studies with a synthetic RNA transcript sequence demonstrated formation of a highly stable parallel-folded quadruplex structure. Luciferase reporter constructs demonstrate that the G-rich sequence reduces rates of translation when present in the 5'-UTR of mRNA transcripts. Mutations (GGG to AAA) that destabilize the quadruplex lead to a 15-fold enhancement of translational efficiency, suggesting that a possible biological role of the insert in exon C of the bov-ESR1 is to regulate translation of this exon.

Arabidopsis antibody resources for functional studies in plants.

Here we report creation of a unique and a very valuable resource for Plant Scientific community worldwide. In this era of post-genomics and modelling of multi-cellular systems using an integrative systems biology approach, better understanding of protein localization at sub-cellular, cellular and tissue levels is likely to result in better understanding of their function and role in cell and tissue dynamics, protein-protein interactions and protein regulatory networks. We have raised 94 antibodies against key Arabidopsis root proteins, using either small peptides or recombinant proteins. The success rate with the peptide antibodies was very low. We show that affinity purification of antibodies massively improved the detection rate. Of 70 protein antibodies, 38 (55%) antibodies could detect a signal with high confidence and 22 of these antibodies are of immunocytochemistry grade. The targets include key proteins involved in hormone synthesis, transport and perception, membrane trafficking related proteins and several sub cellular marker proteins. These antibodies are available from the Nottingham Arabidopsis Stock Centre.

Repression of translation of human estrogen receptor alpha by G-quadruplex formation.

Tissue-specific expression of the human estrogen receptor alpha gene (ESR1) is achieved through multiple promoter sequences resulting in various mRNA transcripts encoding a common protein but differing in their 5'-untranslated region (5'-UTR). Many cancers are estrogen-sensitive with neoplastic growth stimulated through the estrogen receptor, a transcription factor that regulates developmental genes. We demonstrate that the human ESR1 gene is rich in potential quadruplex-forming sequences with 3 of 20 identified within exonic regions. In particular, we show using CD, UV, and NMR spectroscopy that a stable DNA G-quadruplex motif is formed within the exon C gene sequence. This motif, which PCR shows is transcribed in normal and neoplastic endometrium and in MCF-7 cells, forms a stable RNA quadruplex demonstrable by CD and UV analysis. Cloning the exon C G-quadruplex sequence upstream of a luciferase reporter gene caused a 6-fold reduction of enzymatic activity compared to a mutant sequence. We conclude that the exon C G-quadruplex motif is present in the 5'-UTR of the mRNA transcript, where it modulates the efficiency of translation.

Author Correction: The Beta-adrenergic agonist, Ractopamine, increases skeletal muscle expression of Asparagine Synthetase as part of an integrated stress response gene program.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Analysis of Gene Regulatory Networks of Maize in Response to Nitrogen.

Nitrogen (N) fertilizer has a major influence on the yield and quality. Understanding and optimising the response of crop plants to nitrogen fertilizer usage is of central importance in enhancing food security and agricultural sustainability. In this study, the analysis of gene regulatory networks reveals multiple genes and biological processes in response to N. Two microarray studies have been used to infer components of the nitrogen-response network. Since they used different array technologies, a map linking the two probe sets to the maize B73 reference genome has been generated to allow comparison. Putative Arabidopsis homologues of maize genes were used to query the Biological General Repository for Interaction Datasets (BioGRID) network, which yielded the potential involvement of three transcription factors (TFs) (GLK5, MADS64 and bZIP108) and a Calcium-dependent protein kinase. An Artificial Neural Network was used to identify influential genes and retrieved bZIP108 and WRKY36 as significant TFs in both microarray studies, along with genes for Asparagine Synthetase, a dual-specific protein kinase and a protein phosphatase. The output from one study also suggested roles for microRNA (miRNA) 399b and Nin-like Protein 15 (NLP15). Co-expression-network analysis of TFs with closely related profiles to known Nitrate-responsive genes identified GLK5, GLK8 and NLP15 as candidate regulators of genes repressed under low Nitrogen conditions, while bZIP108 might play a role in gene activation.