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.

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.

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.

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.

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.

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.

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.

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

Synthetic beta-adrenergic agonists (BA) have broad biomedical and agricultural application for increasing lean body mass, yet a poor understanding of the biology underpinning these agents is limiting further drug discovery potential. Growing female pigs (77 ± 7 kg) were administered the BA, Ractopamine (20 ppm in feed), or the recombinant growth hormone (GH), Reporcin (10 mg/48 hrs injected) for 1, 3, 7, 13 (n = 10 per treatment, per time point) or 27 days (n = 15 per treatment). Using RNA-sequencing and inferred pathway analysis, we examined temporal changes to the Longissimus Dorsi skeletal muscle transcriptome (n = 3 per treatment, per time point) relative to a feed-only control cohort. Gene expression changes were affirmed by quantitative-PCR on all samples (n = 164). RNA-sequencing analysis revealed that BA treatment had greater effects than GH, and that asparagine synthetase (Asns) was the 5th most significantly increased gene by BA at day 3. ASNS protein expression was dramatically increased by BA treatment at day 7 (p < 0.05). The most significantly increased gene at day 3 was activating transcription factor 5 (Atf5), a transcription factor known to regulate ASNS gene expression. Gene and protein expression of Atf4, another known regulator of Asns expression, was not changed by BA treatment. Expression of more than 20 known Atf4 target genes were increased by BA treatment, suggesting that BA treatment induces an integrated stress response (ISR) in skeletal muscle of pigs. In support of this, mRNA expression of sestrin-2 (Sesn2) and cyclin-dependant kinase 1 alpha (Cdkn1a), two key stress-responsive genes and negative regulators of cellular growth, were also strongly increased from day 3 of BA treatment. Finally, tRNA charging was the most significantly enriched pathway induced by BA treatment, suggesting alterations to the translational capacity/efficiency of the muscle. BA-mediated changes to the skeletal muscle transcriptome are highly indicative of an integrated stress response (ISR), particularly genes relating to amino acid biosynthesis and protein translational capacity.

Author Correction: Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate.

The original version of this Article omitted the following from the Acknowledgements:'We also thank DBT-CREST BT/HRD/03/01/2002.'This has been corrected in both the PDF and HTML versions of the Article.

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.

Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate.

Root traits such as root angle and hair length influence resource acquisition particularly for immobile nutrients like phosphorus (P). Here, we attempted to modify root angle in rice by disrupting the OsAUX1 auxin influx transporter gene in an effort to improve rice P acquisition efficiency. We show by X-ray microCT imaging that root angle is altered in the osaux1 mutant, causing preferential foraging in the top soil where P normally accumulates, yet surprisingly, P acquisition efficiency does not improve. Through closer investigation, we reveal that OsAUX1 also promotes root hair elongation in response to P limitation. Reporter studies reveal that auxin response increases in the root hair zone in low P environments. We demonstrate that OsAUX1 functions to mobilize auxin from the root apex to the differentiation zone where this signal promotes hair elongation when roots encounter low external P. We conclude that auxin and OsAUX1 play key roles in promoting root foraging for P in rice.

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.

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.

Genome-wide mapping of transcriptional enhancer candidates using DNA and chromatin features in maize.

BACKGROUND: While most cells in multicellular organisms carry the same genetic information, in each cell type only a subset of genes is being transcribed. Such differentiation in gene expression depends, for a large part, on the activation and repression of regulatory sequences, including transcriptional enhancers. Transcriptional enhancers can be located tens of kilobases from their target genes, but display characteristic chromatin and DNA features, allowing their identification by genome-wide profiling. Here we show that integration of chromatin characteristics can be applied to predict distal enhancer candidates in Zea mays, thereby providing a basis for a better understanding of gene regulation in this important crop plant. RESULT: To predict transcriptional enhancers in the crop plant maize (Zea mays L. ssp. mays), we integrated available genome-wide DNA methylation data with newly generated maps for chromatin accessibility and histone 3 lysine 9 acetylation (H3K9ac) enrichment in young seedling and husk tissue. Approximately 1500 intergenic regions, displaying low DNA methylation, high chromatin accessibility and H3K9ac enrichment, were classified as enhancer candidates. Based on their chromatin profiles, candidate sequences can be classified into four subcategories. Tissue-specificity of enhancer candidates is defined based on the tissues in which they are identified and putative target genes are assigned based on tissue-specific expression patterns of flanking genes. CONCLUSIONS: Our method identifies three previously identified distal enhancers in maize, validating the new set of enhancer candidates and enlarging the toolbox for the functional characterization of gene regulation in the highly repetitive maize genome.

A Transcriptomic Comparison of Two Bambara Groundnut Landraces under Dehydration Stress.

The ability to grow crops under low-water conditions is a significant advantage in relation to global food security. Bambara groundnut is an underutilised crop grown by subsistence farmers in Africa and is known to survive in regions of water deficit. This study focuses on the analysis of the transcriptomic changes in two bambara groundnut landraces in response to dehydration stress. A cross-species hybridisation approach based on the Soybean Affymetrix GeneChip array has been employed. The differential gene expression analysis of a water-limited treatment, however, showed that the two landraces responded with almost completely different sets of genes. Hence, both landraces with very similar genotypes (as assessed by the hybridisation of genomic DNA onto the Soybean Affymetrix GeneChip) showed contrasting transcriptional behaviour in response to dehydration stress. In addition, both genotypes showed a high expression of dehydration-associated genes, even under water-sufficient conditions. Several gene regulators were identified as potentially important. Some are already known, such as WRKY40, but others may also be considered, namely PRR7, ATAUX2-11, CONSTANS-like 1, MYB60, AGL-83, and a Zinc-finger protein. These data provide a basis for drought trait research in the bambara groundnut, which will facilitate functional genomics studies. An analysis of this dataset has identified that both genotypes appear to be in a dehydration-ready state, even in the absence of dehydration stress, and may have adapted in different ways to achieve drought resistance. This will help in understanding the mechanisms underlying the ability of crops to produce viable yields under drought conditions. In addition, cross-species hybridisation to the soybean microarray has been shown to be informative for investigating the bambara groundnut transcriptome.

DNA Methylation and Chromatin Regulation during Fleshy Fruit Development and Ripening.

Fruit ripening is a developmental process that results in the leaf-like carpel organ of the flower becoming a mature ovary primed for dispersal of the seeds. Ripening in fleshy fruits involves a profound metabolic phase change that is under strict hormonal and genetic control. This work reviews recent developments in our understanding of the epigenetic regulation of fruit ripening. We start by describing the current state of the art about processes involved in histone post-translational modifications and the remodeling of chromatin structure and their impact on fruit development and ripening. However, the focus of the review is the consequences of changes in DNA methylation levels on the expression of ripening-related genes. This includes those changes that result in heritable phenotypic variation in the absence of DNA sequence alterations, and the mechanisms for their initiation and maintenance. The majority of the studies described in the literature involve work on tomato, but evidence is emerging that ripening in other fruit species may also be under epigenetic control. We discuss how epigenetic differences may provide new targets for breeding and crop improvement.

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.

Lipidomic analysis of plasma samples from women with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common disorder affecting between 5 and 18 % of females of reproductive age and can be diagnosed based on a combination of clinical, ultrasound and biochemical features, none of which on its own is diagnostic. A lipidomic approach using liquid chromatography coupled with accurate mass high-resolution mass-spectrometry (LC-HRMS) was used to investigate if there were any differences in plasma lipidomic profiles in women with PCOS compared with control women at different stages of menstrual cycle. Plasma samples from 40 women with PCOS and 40 controls aged between 18 and 40 years were analysed in combination with multivariate statistical analyses. Multivariate data analysis (LASSO regression and OPLS-DA) of the sample lipidomics datasets showed a weak prediction model for PCOS versus control samples from the follicular and mid-cycle phases of the menstrual cycle, but a stronger model (specificity 85 % and sensitivity 95 %) for PCOS versus the luteal phase menstrual cycle controls. The PCOS vs luteal phase model showed increased levels of plasma triglycerides and sphingomyelins and decreased levels of lysophosphatidylcholines and phosphatidylethanolamines in PCOS women compared with controls. Lipid biomarkers of PCOS were tentatively identified which may be useful in distinguishing PCOS from controls especially when performed during the menstrual cycle luteal phase.

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.