An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence.

BACKGROUND: The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work-streams required for tissue-specific (skin) and immunostain-specific annotation has not been extensively studied compared with the development of AI algorithms. OBJECTIVES: The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI-assisted analysis pipeline. METHODS: A total of 45 slides containing 3-5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells. RESULTS: We validated two DL models using 10-fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells. CONCLUSIONS: The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.

Light-Up Split Broccoli Aptamer as a Versatile Tool for RNA Assembly Monitoring in Cell-Free TX-TL Systems, Hybrid RNA/DNA Origami Tagging and DNA Biosensing.

Binary light-up aptamers are intriguing and emerging tools with potential in different fields. Herein, we demonstrate the versatility of a split Broccoli aptamer system able to turn on the fluorescence signal only in the presence of a complementary sequence. First, an RNA three-way junction harbouring the split system is assembled in an E. coli-based cell-free TX-TL system where the folding of the functional aptamer is demonstrated. Then, the same strategy is introduced into a 'bio-orthogonal' hybrid RNA/DNA rectangle origami characterized by atomic force microscopy: the activation of the split system through the origami self-assembly is demonstrated. Finally, our system is successfully used to detect the femtomoles of a Campylobacter spp. DNA target sequence. Potential applications of our system include the real-time monitoring of the self-assembly of nucleic-acid-based devices in vivo and of the intracellular delivery of therapeutic nanostructures, as well as the in vitro and in vivo detection of different DNA/RNA targets.

Protein interactions within and between two F-type type IV secretion systems.

Bacterial type IV secretion systems (T4SSs) can mediate conjugation. The T4SS from Neisseria gonorrhoeae possesses the unique ability to mediate DNA secretion into the extracellular environment. The N. gonorrhoeae T4SS can be grouped with F-type conjugative T4SSs based on homology. We tested 17 proteins important for DNA secretion by N. gonorrhoeae for protein interactions. The BACTH-TM bacterial two-hybrid system was successfully used to study periplasmic interactions. By determining if the same interactions were observed for F-plasmid T4SS proteins and when one interaction partner was replaced by the corresponding protein from the other T4SS, we aimed to identify features associated with the unique function of the N. gonorrhoeae T4SS as well as generic features of F-type T4SSs. For both systems, we observed already described interactions shared by homologs from other T4SSs as well as new and described interactions between F-type T4SS-specific proteins. Furthermore, we demonstrate, for the first-time, interactions between proteins with homology to the conserved T4SS outer membrane core proteins and F-type-specific proteins and we confirmed two of them by co-purification. The F-type-specific protein TraHN was found to localize to the outer membrane and the presence of significant amounts of TraHN in the outer membrane requires TraGN .

NIHBA: a network interdiction approach for metabolic engineering design.

MOTIVATION: Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION: Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic Responses to Coaggregation between Streptococcus gordonii and Streptococcus oralis.

Cell-cell adhesion between oral bacteria plays a key role in the development of polymicrobial communities such as dental plaque. Oral streptococci such as Streptococcus gordonii and Streptococcus oralis are important early colonizers of dental plaque and bind to a wide range of different oral microorganisms, forming multispecies clumps or "coaggregates." S. gordonii actively responds to coaggregation by regulating gene expression. To further understand these responses, we assessed gene regulation in S. gordonii and S. oralis following coaggregation in 25% human saliva. Coaggregates were formed by mixing, and after 30 min, RNA was extracted for dual transcriptome sequencing (RNA-Seq) analysis. In S. oralis, 18 genes (6 upregulated and 12 downregulated) were regulated by coaggregation. Significantly downregulated genes encoded functions such as amino acid and antibiotic biosynthesis, ribosome, and central carbon metabolism. In total, 28 genes were differentially regulated in Streptococcus gordonii (25 upregulated and 3 downregulated). Many genes associated with transporters and a two-component (NisK/SpaK) regulatory system were upregulated following coaggregation. Our comparative analyses of S. gordonii-S. oralis with different previously published S. gordonii pairings (S. gordonii-Fusobacterium nucleatum and S. gordonii-Veillonella parvula) suggest that the gene regulation is specific to each pairing, and responses do not appear to be conserved. This ability to distinguish between neighboring bacteria may be important for S. gordonii to adapt appropriately during the development of complex biofilms such as dental plaque. IMPORTANCE Dental plaque is responsible for two of the most prevalent diseases in humans, dental caries and periodontitis. Controlling the formation of dental plaque and preventing the transition from oral health to disease requires a detailed understanding of microbial colonization and biofilm development. Streptococci are among the most common colonizers of dental plaque. This study identifies key genes that are regulated when oral streptococci bind to one another, as they do in the early stages of dental plaque formation. We show that specific genes are regulated in two different oral streptococci following the formation of mixed-species aggregates. The specific responses of S. gordonii to coaggregation with S. oralis are different from those to coaggregation with other oral bacteria. Targeting the key genes that are upregulated during interspecies interactions may be a powerful approach to control the development of biofilm and maintain oral health.

Developing a simple method to enhance the generation of cone and rod photoreceptors in pluripotent stem cell-derived retinal organoids.

Cell replacement therapy is a promising treatment for irreversible retinal cell death in diverse diseases such as Stargardt's disease, age-related macular degeneration, and retinitis pigmentosa. The final impact of all retinal dystrophies is the loss of photoreceptors; hence, there is a pressing need for research into replacement. Seminal work has shown that a simple three-dimensional culture system enables differentiation of human pluripotent stem cells to retinal organoids containing large numbers of photoreceptors developing alongside retinal neurons and Müller glia cells in a laminated structure that resembles the native retina. Despite these promising developments, current protocols show different efficiencies across pluripotent stem cells and result in retinal organoids with a mixture of photoreceptor cells at varying maturation states, along with nonphotoreceptor cell types. In this study, we investigated the impact of stage-specific addition of retinoic acid (RA), 9-cis-retinal, 11-cis-retinal, levodopa (l-DOPA), triiodothyronine (T3), and γ-secretase inhibitor ((2S)-N-[(3,5-Difluorophenyl)acetyl]-l-alanyl-2-phenyl]glycine1,1-dimethylethyl ester2L [DAPT]) in the generation of cone and rod photoreceptors. Our results indicate that addition of RA + T3 during days 90 to 120 of differentiation enhanced the generation of rod and S-cone photoreceptor formation, while the combined addition of DAPT from days 28 to 42 with RA during days 30 to 120 of differentiation led to enhanced generation of L/M-cones at the expense of rods. l-DOPA when added together with RA during days 90 to 120 of differentiation also promoted the emergence of S-cones at the expense of rod photoreceptors. Collectively, these data represent an advance in our ability to direct generation of rod and cone photoreceptors in vitro.

Easybiotics: a GUI for 3D physical modelling of multi-species bacterial populations.

MOTIVATION: 3D physical modelling is a powerful computational technique that allows for the simulation of complex systems such as consortia of mixed bacterial species. The complexities in physical modelling reside in the knowledge intensive model building process and the computational expense in calculating their numerical solutions. These models can offer insights into microbiology, both in understanding natural systems and as design tools for developing novel synthetic bacterial systems. Developing a robust synthetic system typically requires multiple iterations around the specify→design→build→test cycle to meet specifications. This process is laborious and expensive for both the computational and laboratory aspects, hence any improvement in any of the workflow steps would be welcomed. We have previously introduced Simbiotics, a powerful and flexible platform for designing and analyzing 3D simulations of mixed species bacterial populations. Simbiotics requires programming experience to use which creates barriers to entry for use of the tool. RESULTS: In the spirit of enabling biologists who may not have programming skills to install and utilize Simbiotics, we present in this application note Easybiotics, a user-friendly graphical user interface for Simbiotics. Users may design, simulate and analyze models from within the graphical user interface, with features such as live graph plotting and parameter sweeps. Easybiotics provides full access to all of Simbiotics simulation features, such as cell growth, motility and gene regulation. AVAILABILITY AND IMPLEMENTATION: Easybiotics and Simbiotics are free to use under the GPL3.0 licence, and can be found at: http://ico2s.org/software/simbiotics.html. We also provide readily downloadable virtual machine sandboxes to facilitate rapid installation.

Automatic selection of verification tools for efficient analysis of biochemical models.

Motivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the 'usability barrier' enabling biologists to have access to this powerful computational technology. Availability and implementation: SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Synthesis and cell-free cloning of DNA libraries using programmable microfluidics.

Microfluidics may revolutionize our ability to write synthetic DNA by addressing several fundamental limitations associated with generating novel genetic constructs. Here we report the first de novo synthesis and cell-free cloning of custom DNA libraries in sub-microliter reaction droplets using programmable digital microfluidics. Specifically, we developed Programmable Order Polymerization (POP), Microfluidic Combinatorial Assembly of DNA (M-CAD) and Microfluidic In-vitro Cloning (MIC) and applied them to de novo synthesis, combinatorial assembly and cell-free cloning of genes, respectively. Proof-of-concept for these methods was demonstrated by programming an autonomous microfluidic system to construct and clone libraries of yeast ribosome binding sites and bacterial Azurine, which were then retrieved in individual droplets and validated. The ability to rapidly and robustly generate designer DNA molecules in an autonomous manner should have wide application in biological research and development.

CSBB: synthetic biology research at Newcastle University.

The Centre for Synthetic Biology and the Bioeconomy (CSBB) brings together a far-reaching multidisciplinary community across all Newcastle University's faculties - Medical Sciences, Science, Agriculture and Engineering, and Humanities, Arts and Social Sciences. The CSBB focuses on many different areas of Synthetic Biology, including bioprocessing, computational design and in vivo computation, as well as improving understanding of basic molecular machinery. Such breadth is supported by major national and international research funding, a range of industrial partners in the North East of England and beyond, as well as a large number of doctoral and post-doctoral researchers. The CSBB trains the next generation of scientists through a 1-year MSc in Synthetic Biology.

A thermodynamic switch modulates abscisic acid receptor sensitivity.

Abscisic acid (ABA) is a key hormone regulating plant growth, development and the response to biotic and abiotic stress. ABA binding to pyrabactin resistance (PYR)/PYR1-like (PYL)/Regulatory Component of Abscisic acid Receptor (RCAR) intracellular receptors promotes the formation of stable complexes with certain protein phosphatases type 2C (PP2Cs), leading to the activation of ABA signalling. The PYR/PYL/RCAR family contains 14 genes in Arabidopsis and is currently the largest plant hormone receptor family known; however, it is unclear what functional differentiation exists among receptors. Here, we identify two distinct classes of receptors, dimeric and monomeric, with different intrinsic affinities for ABA and whose differential properties are determined by the oligomeric state of their apo forms. Moreover, we find a residue in PYR1, H60, that is variable between family members and plays a key role in determining oligomeric state. In silico modelling of the ABA activation pathway reveals that monomeric receptors have a competitive advantage for binding to ABA and PP2Cs. This work illustrates how receptor oligomerization can modulate hormonal responses and more generally, the sensitivity of a ligand-dependent signalling system.

JEPETTO: a Cytoscape plugin for gene set enrichment and topological analysis based on interaction networks.

SUMMARY: JEPETTO (Java Enrichment of Pathways Extended To TOpology) is a Cytoscape 3.x plugin performing integrative human gene set analysis. It identifies functional associations between genes and known cellular pathways, and processes using protein interaction networks and topological analysis. The plugin integrates information from three separate web servers we published previously, specializing in enrichment analysis, pathways expansion and topological matching. This integration substantially simplifies the analysis of user gene sets and the interpretation of the results. We demonstrate the utility of the JEPETTO plugin on a set of misregulated genes associated with Alzheimer's disease. AVAILABILITY: Source code and binaries are freely available for download at http://apps.cytoscape.org/apps/jepetto, implemented in Java and multi-platform. Installable directly via Cytoscape plugin manager. Released under the GNU General Public Licence.

Transcriptional dynamics of two seed compartments with opposing roles in Arabidopsis seed germination.

Seed germination is a critical stage in the plant life cycle and the first step toward successful plant establishment. Therefore, understanding germination is of important ecological and agronomical relevance. Previous research revealed that different seed compartments (testa, endosperm, and embryo) control germination, but little is known about the underlying spatial and temporal transcriptome changes that lead to seed germination. We analyzed genome-wide expression in germinating Arabidopsis (Arabidopsis thaliana) seeds with both temporal and spatial detail and provide Web-accessible visualizations of the data reported (vseed.nottingham.ac.uk). We show the potential of this high-resolution data set for the construction of meaningful coexpression networks, which provide insight into the genetic control of germination. The data set reveals two transcriptional phases during germination that are separated by testa rupture. The first phase is marked by large transcriptome changes as the seed switches from a dry, quiescent state to a hydrated and active state. At the end of this first transcriptional phase, the number of differentially expressed genes between consecutive time points drops. This increases again at testa rupture, the start of the second transcriptional phase. Transcriptome data indicate a role for mechano-induced signaling at this stage and subsequently highlight the fates of the endosperm and radicle: senescence and growth, respectively. Finally, using a phylotranscriptomic approach, we show that expression levels of evolutionarily young genes drop during the first transcriptional phase and increase during the second phase. Evolutionarily old genes show an opposite pattern, suggesting a more conserved transcriptome prior to the completion of germination.

Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions.

Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes.

EnrichNet: network-based gene set enrichment analysis.

MOTIVATION: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. RESULTS: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. AVAILABILITY: EnrichNet is freely available at http://www.enrichnet.org. CONTACT: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features.

MOTIVATION: The prediction of a protein's contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation. RESULTS: The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions. AVAILABILITY: http://icos.cs.nott.ac.uk/servers/psp.html. CONTACT: natalio.krasnogor@nottingham.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Homebrew Photolithography for the Rapid and Low-Cost, "Do It Yourself" Prototyping of Microfluidic Devices.

Photolithography is the foundational process at the root of micro-electromechanical (MEMS) and microfluidic systems manufacture. The process is descendant from the semiconductor industry, originating from printed circuit board and microprocessor fabrication, itself historically performed in a cleanroom environment utilizing expensive, specialist microfabrication equipment. Consequently, these conditions prove cost-prohibitive and pose a large barrier to entry. We present a novel homebrew, "do-it-yourself" method for performing photolithography to produce master mold wafers using only household appliances and homemade equipment at the bench side, outside of a cleanroom, producing a range of designs including spiral, serpentine, rectangular, and circulatory. Our homebrew processes result in the production of microfluidic channels with feature resolution of ∼85 µm width and 50 µm height utilizing inkjet-printed photomasks on transparency film to expose dry-film photoresist. From start to finish, the entire process takes under <90 min and costs <£300. With SU8 epoxy negative photoresist and a chrome photomask, our low-cost UV exposure apparatus and homemade spincoater could be used to produce PDMS devices containing large arrays of identical microwells measuring 4.4 µm in diameter. We show that our homebrew method produces both rectangular and spiral microfluidic channels with better results than can be achieved by SLA 3D printing by comparison, and amenable to bonding into multilayer functional microfluidic devices. As these methods are fundamental to microfluidics manufacture, we envision that this work will be of value to researchers across a broad range of disciplines, such as those working in resource-constrained countries or conditions, with many and widely varying applications.

Verifiable biology.

The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.