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Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign., This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. S.J. acknowledges funding from BBSRC Mitigation Fund RG16134-18. I.M.O. and J.R.B. acknowledge funding from MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). J.R.B. acknowledges funding from MCIN/AEI/10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS). Y.W. acknowledges funding from the National Natural Science Foundation of China (grant no. 61976225). N.K. is funded by a Royal Academy of Engineering Chair in Emerging Technology award.

Potential applications are increasingly shown in the literature enabled by the advent of scaffolded DNA origami [1]. We employ a data-driven approach, applying machine learning to a curated a database collected from Nucleic Acid Origami literature. We expect to aid the design of well-formed nucleic acid origami through machine learning informed lab protocols and algorithm improved sequence design. This will lead to improved application of nucleic acid origami, with increased yield, scale and complexity. [1] Paul W. K. Rothemund. Folding DNA to create nanoscale shapes and patterns. Nature, 440(7082):297–302, March 2006.

Machine learning and computational algorithms used to see patterns within collected data are becoming increasingly popular and valuable across many intersecting fields of study. For biological fields there needs to be cooperation between scientists working in both computer science and biological fields to close the gap between traditional lab experiments data collection and sharing towards the prospect of more modern data curation methods that would be invaluable for the data mining of shared stored experimental data. Challenges include the representation of experimental data, the design and focus of experiments from their outset and when looking at comparability of experiments across papers with different goals. &nbsp

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

At the onset of the 4th Industrial Revolution, the role of synthetic biology (SynBio) as a fuel for the bioeconomy requires clarification of the terms typically adopted by this growing scientific-technical field. The concept of the chassis as a defined, reusable biological frame where non-native components can be plugged in and out to create new functionalities lies at the boundary between frontline bioengineering and more traditional recombinant DNA technology. As synthetic biology leaves academic laboratories and starts penetrating industrial and environmental realms regulatory agencies demand clear definitions and descriptions of SynBio constituents, processes and products. In this article, the state of the ongoing discussion on what is a chassis is reviewed, a non-equivocal nomenclature for the jargon used is proposed and objective criteria are recommended for distinguishing SynBio agents from traditional GMOs. The use of genomic barcodes as unique identifiers is strongly advocated. Finally the soil bacterium Pseudomonas putida is shown as an example of the roadmap that one environmental isolate may go through to become a bona fide SynBio chassis.

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.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.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.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.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.

The success of any machine learning technique depends on the correct setting of its parameters and, when it comes to large-scale datasets, hand-tuning these parameters becomes impractical. However, very large-datasets can be pre-processed in order to distil information that could help in appropriately setting various systems parameters. In turn, this makes sophisticated machine learning methods easier to use to end-users. Thus, by modelling the performance of machine learning algorithms as a function of the structure inherent in very large datasets one could, in principle, detect "hotspots" in the parameters' space and thus, auto-tune machine learning algorithms for better dataset-specific performance. In this work we present a parameter setting mechanism for a rule-based evolutionary machine learning system that is capable of finding the adequate parameter value for a wide variety of synthetic classification problems with binary attributes and with/without added noise. Moreover, in the final validation stage our automated mechanism is able to reduce the computational time of preliminary experiments up to 71% for a challenging real-world bioinformatics dataset.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker’s preference in different forms, are very effective to boost the performance of algorithms. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state-of-the-arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.

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.

As DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biological engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnology and digital technology, a deficit in software and laboratory techniques diminishes the ability to make biotechnology more agile, reproducible, and transparent while, at the same time, limiting the security and safety of synthetic biology constructs. To partially address some of these problems, this paper presents an approach for physically linking engineered cells to their digital footprint—we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialized version control system for collaborative strain engineering via simple barcoding protocols., J.T.L., C.W., J.K., and N.K. were supported by the UK Engineering and Physical Research Council under project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. N.K. is funded by a Royal Academy of Engineering Chair in Emerging Technology award. V.d.L. was supported by project “BioRoboost (H2020-NMBP-BIO-CSA-2018, grant agreement N820699)”.

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., Schematic summary of our results showing that the addition of retinoic acid + T3 (days 90‐120) led to enhanced generation of rod and S‐cone photoreceptors while combined addition of retinoic acid + Levodopa, at the same time window, promoted only the S‐cone formation. The supplementation of retinoic acid + DAPT (days 30‐120 for RA+ days 28‐42 for DAPT) resulted in an increased L/M‐cone photoreceptor generation.

Genome editing methods based on group II introns (known as targetron technology) have long been used as a gene knockout strategy in a wide range of organisms, in a fashion independent of homologous recombination. Yet, their utility as delivery systems has typically been suboptimal due to the reduced efficiency of insertion when carrying exogenous sequences. We show that this limitation can be tackled and targetrons can be adapted as a general tool in Gram-negative bacteria. To this end, a set of broad-host-range standardized vectors were designed for the conditional expression of the Ll.LtrB intron. After establishing the correct functionality of these plasmids in Escherichia coli and Pseudomonas putida, we created a library of Ll.LtrB variants carrying cargo DNA sequences of different lengths, to benchmark the capacity of intron-mediated delivery in these bacteria. Next, we combined CRISPR/Cas9-facilitated counterselection to increase the chances of finding genomic sites inserted with the thereby engineered introns. With these novel tools, we were able to insert exogenous sequences of up to 600 bp at specific genomic locations in wild-type P. putida KT2440 and its ΔrecA derivative. Finally, we applied this technology to successfully tag P. putida with an orthogonal short sequence barcode that acts as a unique identifier for tracking this microorganism in biotechnological settings. These results show the value of the targetron approach for the unrestricted delivery of small DNA fragments to precise locations in the genomes of Gram-negative bacteria, which will be useful for a suite of genome editing endeavors.

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.

Genome editing methods based on Group II introns (known as Targetron technology) have been long used as a gene knock-out strategy in a wide range of organisms in a fashion independent of homologous recombination. Yet, their utility as delivery systems has been typically suboptimal because of their reduced efficiency of insertion when they carry exogenous sequences. We show that this limitation can be tackled and Targetron adapted as a general tool in Gram-negative bacteria. To this end, a set of broad host range standardized vectors were designed for conditional expression of the Ll.LtrB intron. After testing the correct functionality of these plasmids inEscherichia coliandPseudomonas putida, we created a library of Ll.LtrB variants carrying cargo DNA sequences of different lengths to benchmark the capacity of intron-mediated delivery in these bacteria. Next, we combined CRISPR/Cas9-facilitated counterselection to increase the chances of finding genomic sites inserted with the thereby engineered introns. By following this pipeline, we were able to insert exogenous sequences of up to 600 bp at specific genomic locations in wild-typeP. putidaKT2440 and its ΔrecAderivative. Finally, we were able to apply this technology to successfully tag this strain with an orthogonal short sequence (barcode) that acts as a unique identifier for tracking this microorganism in biotechnological settings. The results withP. putidaexemplified the value of the Targetron approach for unrestricted delivery of small DNA fragments to the genomes of Gram-negative bacteria for a suite of genome editing endeavours.

“Full-stack”biotechnology platforms for cell line (re)programming are on the horizon, due mostly to (a) advances in gene synthesis and editing techniques as well as (b) the growing integration with informatics, the internet of things and automation. These emerging platforms will accelerate the production and consumption of biological products. Hence, transparency, traceability and -ultimately-trustworthiness is required -from cradle to grave- for engineered cell lines and their engineering processes. We report here the first version control system for cell engineering that integrates a new cloud-based version control software for cell lines’ digital footprint with molecular barcoding of living samples. We argue that version control for cell engineering marks a significant step towards more open, reproducible, easier to trace and share, and more trustworthy engineering biology.One Sentence SummaryWe demonstrate a transparent and open way of engineering and sharing cell lines.

DNA-based memory systems are being reported with increasing frequency. However, dynamic DNA data structures able to store and recall information in an ordered way, and able to be interfaced with external nucleic acid computing circuits, have so far received little attention. Here we present an in vitro implementation of a stack data structure using DNA polymers. The stack is able to record combinations of two different DNA signals, release the signals into solution in reverse order, and then re-record. We explore the accuracy limits of the stack data structure through a stochastic rule-based model of the underlying polymerisation chemistry. We derive how the performance of the stack increases with the efficiency of washing steps between successive reaction stages, and report how stack performance depends on the history of stack operations under inefficient washing. Finally, we discuss refinements to improve molecular synchronisation and future open problems in implementing an autonomous chemical data structure., DNA is becoming increasingly used as a medium to store non-genetic information. Here the authors present a dynamic stack data structure implemented as a DNA polymer chemistry able to record and retrieve signals in a last-in first-out order.

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 TraH(N) was found to localize to the outer membrane and the presence of significant amounts of TraH(N) in the outer membrane requires TraG(N).

The scaffolded origami technique has provided an attractive tool for engineering nucleic acid nanostructures. This paper demonstrates scaffolded RNA origami folding in vitro in which all components are transcribed simultaneously in a single-pot reaction. Double-stranded DNA sequences are transcribed by T7 RNA polymerase into scaffold and staple strands able to correctly fold in high yield into the nanoribbon. Synthesis is successfully confirmed by atomic force microscopy and the unpurified transcription reaction mixture is analyzed by an in gel-imaging assay where the transcribed RNA nanoribbons are able to capture the specific dye through the reconstituted split Broccoli aptamer showing a clear green fluorescent band. Finally, we simulate the RNA origami in silico using the nucleotide-level coarse-grained model oxRNA to investigate the thermodynamic stability of the assembled nanostructure in isothermal conditions over a period of time.Our work suggests that the scaffolded origami technique is a valid, and potentially more powerful, assembly alternative to the single-stranded origami technique for future in vivo applications.Abstract Figure

β-Cell dysfunction in type 2 diabetes (T2D) is associated with loss of cellular identity and mis-expression of alternative islet hormones, including glucagon. The molecular basis for these cellular changes has been attributed to dysregulation of core β-cell transcription factors, which regulate β-cell identity through activating and repressive mechanisms. The TLE1 gene lies near a T2D susceptibility locus and, recently, the glucagon repressive actions of this transcriptional coregulator have been demonstrated in vitro. We investigated whether TLE1 expression is disrupted in human T2D, and whether this is associated with increased islet glucagon-expressing cells. Automated image analysis following immunofluorescence in donors with (n = 7) and without (n = 7) T2D revealed that T2D was associated with higher islet α/β cell ratio (Control: 0.7 ± 0.1 vs T2D: 1.6 ± 0.4; P < .05) and an increased frequency of bihormonal (insulin+/glucagon+) cells (Control: 0.8 ± 0.2% vs T2D: 2.0 ± 0.4%, P < .05). In nondiabetic donors, the majority of TLE1-positive cells were mono-hormonal β-cells (insulin+/glucagon–: 98.2 ± 0.5%; insulin+/glucagon+: 0.7 ± 0.2%; insulin–/glucagon+: 1.1 ± 0.4%; P < .001). TLE1 expression was reduced in T2D (Control: 36 ± 2.9% vs T2D: 24 ± 2.6%; P < .05). Reduced islet TLE1 expression was inversely correlated with α/β cell ratio (r = –0.55; P < .05). TLE1 knockdown in EndoC-βH1 cells was associated with a 2.5-fold increase in glucagon gene mRNA and mis-expression of glucagon in insulin-positive cells. These data support TLE1 as a putative regulator of human β-cell identity, with dysregulated expression in T2D associated with increased glucagon expression potentially reflecting β- to α-cell conversion.

Phosphoinositide 3-kinase δ (PI3Kδ), a lipid kinase consisting of a catalytic (p110δ, encoded by PIK3CD) and a regulatory subunit (p85, encoded by PIK3R1), generates the second messenger phosphatidylinositol (3,4,5)-trisphosphate (PIP3) in the plasma membrane of leukocytes downstream of antigen and cytokine receptors.1 Signaling via PDK1, AKT, mTOR and downstream targets such as FOXO1, contributes to the metabolic and transcriptional changes required for the expansion, differentiation and effector function of lymphocytes. Activating germline mutations in PIK3CD cause the immune dysregulatory disease activated PI3Kδ syndrome (APDS), usually presenting with recurrent sinopulmonary infections in childhood, herpesvirus infections and CD4+ lymphopenia, underscoring the important role of balanced p110δ activity in human adaptive immunity. Ablation of p110δ in mice leads to aberrant T cell responses and intestinal inflammation. In humans, immune dysregulation including severe colitis is present in many cancer patients who are treated with the p110δ-specific inhibitor Idelalisib. Recently, one patient with autosomal recessive deficiency of p85α and two patients with loss-of function mutations in p110δ have been described who developed humoral immunodeficiency and colitis.

1.AbstractAs DNA sequencing and synthesis become cheaper and more easily accessible, the scale and complexity of biological engineering projects is set to grow. Yet, although there is an accelerating convergence between biotechnology and computing science, a deficit in software and laboratory techniques diminishes the ability to make biotechnology more agile, reproducible and transparent while, at the same time, limiting the security and safety of synthetic biology constructs. To partially address some of these problems, this paper presents an approach for physically linking engineered cells to their digital footprint - we called it digital twinning. This enables the tracking of the entire engineering history of a cell line in a specialised version control system for collaborative strain engineering.

Flux balance analysis (FBA) based bilevel optimisation 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 optimisation 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. In this work, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimisation, 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 hour).

Context Although diabetes affects 40% to 50% of adults with cystic fibrosis, remarkably little is known regarding the underlying mechanisms leading to impaired pancreatic β-cell insulin secretion. Efforts toward improving the functional β-cell deficit in cystic fibrosis-related diabetes (CFRD) have been hampered by an incomplete understanding of whether β-cell function is intrinsically regulated by cystic fibrosis transmembrane conductance regulator (CFTR). Definitively excluding meaningful CFTR expression in human β-cells in situ would contribute significantly to the understanding of CFRD pathogenesis. Objective To determine CFTR messenger ribonucleic acid (mRNA) and protein expression within β-cells in situ in the unmanipulated human pancreas of donors without any known pancreatic pathology. Design In situ hybridization for CFTR mRNA expression in parallel with insulin immunohistochemical staining and immunofluorescence co-localization of CFTR with insulin and the ductal marker, Keratin-7 (KRT7), were undertaken in pancreatic tissue blocks from 10 normal adult, nonobese deceased organ donors over a wide age range (23–71 years) with quantitative image analysis. Results CFTR mRNA was detectable in a mean 0.45% (range 0.17%–0.83%) of insulin-positive cells. CFTR protein expression was co-localized with KRT7. One hundred percent of insulin-positive cells were immunonegative for CFTR. Conclusions For the first time, in situ CFTR mRNA expression in the unmanipulated pancreas has been shown to be present in only a very small minority (

Many oral bacteria form macroscopic clumps known as coaggregates when mixed with a different species. It is thought that these cell-cell interactions are critical for the formation of mixed-species biofilms such as dental plaque. Here, we assessed the impact of coaggregation between two key initial colonizers of dental plaque, Streptococcus gordonii and Veillonella parvula, on gene expression in each partner. These species were shown to coaggregate in buffer or human saliva. To monitor gene regulation, coaggregates were formed in human saliva and, after 30 minutes, whole-transcriptomes were extracted for sequencing and Dual RNA-Seq analysis. In total, 272 genes were regulated in V. parvula, including 39 genes in oxidoreductase processes. In S. gordonii, there was a high degree of inter-sample variation. Nevertheless, 69 genes were identified as potentially regulated by coaggregation, including two phosphotransferase system transporters and several other genes involved in carbohydrate metabolism. Overall, these data indicate that responses of V. parvula to coaggregation with S. gordonii are dominated by oxidative stress-related processes, whereas S. gordonii responses are more focussed on carbohydrate metabolism. We hypothesize that these responses may reflect changes in the local microenvironment in biofilms when S. gordonii or V. parvula immigrate into the system.

Dynamic multiobjective optimisation has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of existing dynamic multiobjective test problems have not been rigorously constructed and analysed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more importantly, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the dynamic multiobjective optimisation algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites can not., Comment: 19 pages, 22 figures and 7 tables

Tissue specific extracellular matrices (ECM) provide structural support and enable access to molecular signals and metabolites, which are essential for directing stem cell renewal and differentiation. To mimic this phenomenon in vitro, tissue decellularisation approaches have been developed, resulting in the generation of natural ECM scaffolds that have comparable physical and biochemical properties of the natural tissues and are currently gaining traction in tissue engineering and regenerative therapies due to the ease of standardised production, and constant availability. In this manuscript we report the successful generation of decellularised ECM-derived peptides from neural retina (decel NR) and retinal pigment epithelium (decel RPE), and their impact on differentiation of human pluripotent stem cells (hPSCs) to retinal organoids. We show that culture media supplementation with decel RPE and RPE-conditioned media (CM RPE) significantly increases the generation of rod photoreceptors, whilst addition of decel NR and decel RPE significantly enhances ribbon synapse marker expression and the light responsiveness of retinal organoids. Photoreceptor maturation, formation of correct synapses between retinal cells and recording of robust light responses from hPSC-derived retinal organoids remain unresolved challenges for the field of regenerative medicine. Enhanced rod photoreceptor differentiation, synaptogenesis and light response in response to addition of decellularised matrices from RPE and neural retina as shown herein provide a novel and substantial advance in generation of retinal organoids for drug screening, tissue engineering and regenerative medicine.

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.

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.

Cell-cell interactions between genetically distinct bacteria, known as coaggregation, are important for the formation of mixed-species biofilms such as dental plaque. Interactions lead to gene regulation in the partner organisms that may be critical for adaptation and survival in mixed-species biofilms. Here, gene regulation responses to coaggregation between Streptococcus gordonii and Fusobacterium nucleatum were studied using dual RNA-Seq. Initially, S. gordonii was shown to coaggregate strongly with F. nucleatum in buffer or human saliva. Using confocal laser scanning microscopy and transmission electron microscopy, cells of different species were shown to be evenly distributed throughout the coaggregate and were closely associated with one another. This distribution was confirmed by serial block face sectioning scanning electron microscopy, which provided high resolution three-dimensional images of coaggregates. Cell-cell sensing responses were analysed 30 minutes after inducing coaggregation in human saliva. By comparison with monocultures, 16 genes were regulated following coaggregation in F. nucleatum whereas 119 genes were regulated in S. gordonii. In both species, genes involved in amino acid and carbohydrate metabolism were strongly affected by coaggregation. In particular, one 8-gene operon in F. nucleatum encoding sialic acid uptake and catabolism was up-regulated 2- to 5-fold following coaggregation. In S. gordonii, a gene cluster encoding functions for phosphotransferase system-mediated uptake of lactose and galactose was down-regulated up to 3-fold in response to coaggregation. The genes identified in this study may play key roles in the development of mixed-species communities and represent potential targets for approaches to control dental plaque accumulation.

Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms.

Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.

The extracellular matrix (ECM) plays an important role in numerous processes including cellular proliferation, differentiation, migration, maturation, adhesion guidance and axonal growth. To date, there has been no detailed analysis of the ECM distribution during retinal ontogenesis in humans and the functional importance of many ECM components is poorly understood. In this study, the expression of key ECM components in adult mouse and monkey retina, developing and adult human retina and retinal organoids derived from human pluripotent stem cells was studied. Our data indicate that basement membrane ECMs (Fibronectin and Collagen IV) were expressed in Bruch's membrane and the inner limiting membrane of the developing human retina, whilst the hyalectins (Versican and Brevican), cluster of differentiation 44 (CD44), photoreceptor-specific ECMs Interphotoreceptor Matrix Proteoglycan 1 (IMPG1) and Interphotoreceptor Matrix Proteoglycan 2 (IMPG2) were detected in the developing interphotoreceptor matrix (IPM). The expression of IMPG1, Versican and Brevican in the developing IPM was conserved between human developing retina and human pluripotent stem cell-derived retinal organoids. Blocking the action of CD44 and IMPG1 in pluripotent stem cell derived retinal organoids affected the development of photoreceptors, their inner/outer segments and connecting cilia and disrupted IPM formation, with IMPG1 having an earlier and more significant impact. Together, our data suggest an important role for IMPG1 and CD44 in the development of photoreceptors and IPM formation during human retinogenesis.The expression and the role of many extracellular matrix (ECM) components during human retinal development is not fully understood. In this study, expression of key ECM components (Collagen IV, Fibronectin, Brevican, Versican, IMPG1 and IMPG2) was investigated during human retinal ontogenesis. Collagen IV and Fibronectin were expressed in Bruch's membrane; whereas Brevican, Versican, IMPG1IMPG2 in the developing interphotoreceptor matrix (IPM). Retinal organoids were successfully generated from pluripotent stem cells. The expression of ECM components was examined in the retinal organoids and found to recapitulate human retinal development in vivo. Using functional blocking experiments, we were able to highlight an important role for IMPG1 and CD44 in the development of photoreceptors and IPM formation.