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.

Reverse engineering DNA origami nanostructure designs from raw scaffold and staple sequence lists.

Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) which generated the low-level sequences are not made available. This de facto 'raw sequence' exchange format allows published origami designs to be re-attempted in the laboratory by other groups, but effectively stops designs from being significantly modified or re-purposed for new future applications. To make the raw sequence exchange format more accessible to further design and engineering, in this work we propose the first algorithmic solution to the inverse problem of converting staple/scaffold sequences back to a 'guide schematic' resembling the original origami schematic. The guide schematic can be used to aid the manual re-input of an origami into a CAD tool like caDNAno, hence recovering a high-level editable design file. Creation of a guide schematic can also be used to double check that a list of staple strand sequences does not have errors and indeed does assemble into a desired origami nanostructure prior to costly laboratory experimentation. We tested our reverse algorithm on 36 diverse origami designs from the literature and found that 29 origamis (81 %) had a good quality guide schematic recovered from raw sequences. Our software is made available at https://revnano.readthedocs.io.

Modified Noise-Immune Fuzzy Neural Network for Solving the Quadratic Programming With Equality Constraint Problem.

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. This article proposes a modified noise-immune fuzzy neural network (MNIFNN) model and use it to solve QPEC problems. Compared with the traditional gradient recurrent neural network (TGRNN) and traditional zeroing recurrent neural network (TZRNN) models, the MNIFNN model has the advantage of inherent noise tolerance ability and stronger robustness, which is achieved by combining proportional, integral, and differential elements. Furthermore, the design parameters of the MNIFNN model adopt two disparate fuzzy parameters generated by two fuzzy logic systems (FLSs) related to the residual and residual integral term, which can improve the adaptability of the MNIFNN model. Numerical simulations demonstrate the effectiveness of the MNIFNN model in noise tolerance.

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.

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.

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.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.

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.

Versioning biological cells for trustworthy cell engineering.

"Full-stack" biotechnology platforms for cell line (re)programming are on the horizon, thanks mostly to (a) advances in gene synthesis and editing techniques as well as (b) the growing integration of life science research with informatics, the internet of things and automation. These emerging platforms will accelerate the production and consumption of biological products. Hence, traceability, transparency, and-ultimately-trustworthiness is required from cradle to grave for engineered cell lines and their engineering processes. Here we report a cloud-based version control system for biotechnology that (a) keeps track and organizes the digital data produced during cell engineering and (b) molecularly links that data to the associated living samples. Barcoding protocols, based on standard genetic engineering methods, to molecularly link to the cloud-based version control system six species, including gram-negative and gram-positive bacteria as well as eukaryote cells, are shown. We argue that version control for cell engineering marks a significant step toward more open, reproducible, easier to trace and share, and more trustworthy engineering biology.

Targetron-Assisted Delivery of Exogenous DNA Sequences into Pseudomonas putida through CRISPR-Aided Counterselection.

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.

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.

Toward Full-Stack In Silico Synthetic Biology: Integrating Model Specification, Simulation, Verification, and Biological Compilation.

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.

A last-in first-out stack data structure implemented in DNA.

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.

For the sake of the Bioeconomy: define what a Synthetic Biology Chassis is!

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.

Expression, Localization, and Protein Interactions of the Partitioning Proteins in the Gonococcal Type IV Secretion System.

Partitioning proteins are well studied as molecular organizers of chromosome and plasmid segregation during division, however little is known about the roles partitioning proteins can play within type IV secretion systems. The single-stranded DNA (ssDNA)-secreting gonococcal T4SS has two partitioning proteins, ParA and ParB. These proteins work in collaboration with the relaxase TraI as essential facilitators of type IV secretion. Bacterial two-hybrid experiments identified interactions between each partitioning protein and the relaxase. Subcellular fractionation demonstrated that ParA is found in the cellular membrane, whereas ParB is primarily in the membrane, but some of the protein is in the soluble fraction. Since TraI is known to be membrane-associated, these data suggest that the gonococcal relaxosome is a membrane-associated complex. In addition, we found that translation of ParA and ParB is controlled by an RNA switch. Different mutations within the stem-loop sequence predicted to alter folding of this RNA structure greatly increased or decreased levels of the partitioning proteins.

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 .

Cotranscriptional Folding of a Bio-orthogonal Fluorescent Scaffolded RNA Origami.

The scaffolded origami technique is an attractive tool for engineering nucleic acid nanostructures. This paper demonstrates scaffolded RNA origami folding in vitro in which, for the first time, 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 a high synthesis 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 viable, and potentially more powerful, assembly alternative to the single-stranded origami technique for future in vivo applications.

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.

Reduced Expression of the Co-regulator TLE1 in Type 2 Diabetes Is Associated with Increased Islet α-Cell Number.

ß-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 ß-cell transcription factors, which regulate ß-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/ß 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 ß-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/ß cell ratio (r = -0.55; P < .05). TLE1 knockdown in EndoC-ßH1 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 ß-cell identity, with dysregulated expression in T2D associated with increased glucagon expression potentially reflecting ß- to α-cell conversion.