High-throughput screening for improved microbial cell factories, perspective and promise

Starting with screening strains to construct synthetic microbial communities (SynComs) for traditional food fermentation

With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.

2-Stage microfermentations

Cell based factories can be engineered to produce a wide variety of products. Advances in DNA synthesis and genome editing have greatly simplified the design and construction of these factories. It has never been easier to generate hundreds or even thousands of cell factory strain variants for evaluation. These advances have amplified the need for standardized, higher throughput means of evaluating these designs. Toward this goal, we have previously reported the development of engineered E. coli strains and associated 2-stage production processes to simplify and standardize strain engineering, evaluation and scale up. This approach relies on decoupling growth (stage 1), from production, which occurs in stationary phase (stage 2). Phosphate depletion is used as the trigger to stop growth as well as induce heterologous expression. Here, we describe in detail the development of protocols for the evaluation of engineered E. coli strains in 2-stage microfermentations. These protocols are readily adaptable to the evaluation of strains producing a wide variety of protein as well as small molecule products. Additionally, by detailing the approach to protocol development, these methods are also adaptable to additional cellular hosts, as well as other 2-stage processes with various additional triggers.

Advances in the Discovery and Engineering of Gene Targets for Carotenoid Biosynthesis in Recombinant Strains

Carotenoids are naturally occurring pigments that are abundant in the natural world. Due to their excellent antioxidant attributes, carotenoids are widely utilized in various industries, including the food, pharmaceutical, cosmetic industries, and others. Plants, algae, and microorganisms are presently the main sources for acquiring natural carotenoids. However, due to the swift progress in metabolic engineering and synthetic biology, along with the continuous and thorough investigation of carotenoid biosynthetic pathways, recombinant strains have emerged as promising candidates to produce carotenoids. The identification and manipulation of gene targets that influence the accumulation of the desired products is a crucial challenge in the construction and metabolic regulation of recombinant strains. In this review, we provide an overview of the carotenoid biosynthetic pathway, followed by a summary of the methodologies employed in the discovery of gene targets associated with carotenoid production. Furthermore, we focus on discussing the gene targets that have shown potential to enhance carotenoid production. To facilitate future research, we categorize these gene targets based on their capacity to attain elevated levels of carotenoid production.

MCF2Chem: A manually curated knowledge base of biosynthetic compound production

BACKGROUND

Microbes have been used as cell factories to synthesize various chemical compounds. Recent advances in synthetic biological technologies have accelerated the increase in the number and capacity of microbial cell factories; the variety and number of synthetic compounds produced via these cell factories have also grown substantially. However, no database is available that provides detailed information on the microbial cell factories and the synthesized compounds.

RESULTS

In this study, we established MCF2Chem, a manually curated knowledge base on the production of biosynthetic compounds using microbial cell factories. It contains 8888 items of production records related to 1231 compounds that were synthesizable by 590 microbial cell factories, including the production data of compounds (titer, yield, productivity, and content), strain culture information (culture medium, carbon source/precursor/substrate), fermentation information (mode, vessel, scale, and condition), and other information (e.g., strain modification method). The database contains statistical analyses data of compounds and microbial species. The data statistics of MCF2Chem showed that bacteria accounted for 60% of the species and that "fatty acids", "terpenoids", and "shikimates and phenylpropanoids" accounted for the top three chemical products. Escherichia coli, Saccharomyces cerevisiae, Yarrowia lipolytica, and Corynebacterium glutamicum synthesized 78% of these chemical compounds. Furthermore, we constructed a system to recommend microbial cell factories suitable for synthesizing target compounds and vice versa by combining MCF2Chem data, additional strain- and compound-related data, the phylogenetic relationships between strains, and compound similarities.

CONCLUSIONS

MCF2Chem provides a user-friendly interface for querying, browsing, and visualizing detailed statistical information on microbial cell factories and their synthesizable compounds. It is publicly available at https://mcf.lifesynther.com . This database may serve as a useful resource for synthetic biologists.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Technical upgrade of an open-source liquid handler to support bacterial colony screening

The optimization of genetically engineered biological constructs is a key step to deliver high-impact biotechnological applications. The use of high-throughput DNA assembly methods allows the construction of enough genotypic variants to successfully cover the target design space. This, however, entails extra workload for researchers during the screening stage of candidate variants. Despite the existence of commercial colony pickers, their high price excludes small research laboratories and budget-adjusted institutions from accessing such extensive screening capability. In this work we present COPICK, a technical solution to automatize colony picking in an open-source liquid handler Opentrons OT-2. COPICK relies on a mounted camera to capture images of regular Petri dishes and detect microbial colonies for automated screening. COPICK's software can then automatically select the best colonies according to different criteria (size, color and fluorescence) and execute a protocol to pick them for further analysis. Benchmark tests performed for E. coli and P. putida colonies delivers a raw picking performance over pickable colonies of 82% with an accuracy of 73.4% at an estimated rate of 240 colonies/h. These results validate the utility of COPICK, and highlight the importance of ongoing technical improvements in open-source laboratory equipment to support smaller research teams.

Machine learning in bioprocess development: from promise to practice

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Fluorescence coupling strategies in fluorescence-activated droplet sorting (FADS) for ultrahigh-throughput screening of enzymes, metabolites, and antibodies

Fluorescence-activated droplet sorting (FADS) has emerged as a powerful tool for ultrahigh-throughput screening of enzymes, metabolites, and antibodies. Fluorescence coupling strategies (FCSs) are key to the development of new FADS methods through their coupling of analyte properties such as concentration, activities, and affinity with fluorescence signals. Over the last decade, a series of FCSs have been developed, greatly expanding applications of FADS. Here, we review recent advances in FCS for different analyte types, providing a critical comparison of the available FCSs and further classification into four categories according to their principles. We also summarize successful FADS applications employing FCSs in enzymes, metabolites, and antibodies. Further, we outline possible future developments in this area.

Application of Adaptive Laboratory Evolution in Lipid and Terpenoid Production in Yeast and Microalgae

Due to the complexity of metabolic and regulatory networks in microorganisms, it is difficult to obtain robust phenotypes through artificial rational design and genetic perturbation. Adaptive laboratory evolution (ALE) engineering plays an important role in the construction of stable microbial cell factories by simulating the natural evolution process and rapidly obtaining strains with stable traits through screening. This review summarizes the application of ALE technology in microbial breeding, describes the commonly used methods for ALE, and highlights the important applications of ALE technology in the production of lipids and terpenoids in yeast and microalgae. Overall, ALE technology provides a powerful tool for the construction of microbial cell factories, and it has been widely used in improving the level of target product synthesis, expanding the range of substrate utilization, and enhancing the tolerance of chassis cells. In addition, in order to improve the production of target compounds, ALE also employs environmental or nutritional stress strategies corresponding to the characteristics of different terpenoids, lipids, and strains.

Black glucose-releasing silicon elastomer rings for fed-batch operation allow measurement of the oxygen transfer rate from the top and optical signals from the bottom for each well of a microtiter plate

BACKGROUND

In industrial microbial biotechnology, fed-batch processes are frequently used to avoid undesirable biological phenomena, such as substrate inhibition or overflow metabolism. For targeted process development, fed-batch options for small scale and high throughput are needed. One commercially available fed-batch fermentation system is the FeedPlate^®, a microtiter plate (MTP) with a polymer-based controlled release system. Despite standardisation and easy incorporation into existing MTP handling systems, FeedPlates^® cannot be used with online monitoring systems that measure optically through the transparent bottom of the plate. One such system that is broadly used in biotechnological laboratories, is the commercial BioLector. To allow for BioLector measurements, while applying the polymer-based feeding technology, positioning of polymer rings instead of polymer disks at the bottom of the well has been proposed. This strategy has a drawback: measurement requires an adjustment of the software settings of the BioLector device. This adjustment modifies the measuring position relative to the wells, so that the light path is no longer blocked by the polymer ring, but, traverses through the inner hole of the ring. This study aimed at overcoming that obstacle and allowing for measurement of fed-batch cultivations using a commercial BioLector without adjustment of the relative measurement position within each well.

RESULTS

Different polymer ring heights, colours and positions in the wells were investigated for their influence on maximum oxygen transfer capacity, mixing time and scattered light measurement. Several configurations of black polymer rings were identified that allow measurement in an unmodified, commercial BioLector, comparable to wells without rings. Fed-batch experiments with black polymer rings with two model organisms, E. coli and H. polymorpha, were conducted. The identified ring configurations allowed for successful cultivations, measuring the oxygen transfer rate and dissolved oxygen tension, pH, scattered light and fluorescence. Using the obtained online data, glucose release rates of 0.36 to 0.44 mg/h could be determined. They are comparable to formerly published data of the polymer matrix.

CONCLUSION

The final ring configurations allow for measurements of microbial fed-batch cultivations using a commercial BioLector without requiring adjustments of the instrumental measurement setup. Different ring configurations achieve similar glucose release rates. Measurements from above and below the plate are possible and comparable to measurements of wells without polymer rings. This technology enables the generation of a comprehensive process understanding and target-oriented process development for industrial fed-batch processes.

Single-cell microliter-droplet screening system (MISS Cell): An integrated platform for automated high-throughput microbial monoclonal cultivation and picking

Solid plates have been used for microbial monoclonal isolation, cultivation, and colony picking since 1881. However, the process is labor- and resource-intensive for high-throughput requirements. Currently, several instruments have been integrated for automated and high-throughput picking, but complicated and expensive. To address these issues, we report a novel integrated platform, the single-cell microliter-droplet screening system (MISS Cell), for automated, high-throughput microbial monoclonal colony cultivation and picking. We verified the monoclonality of droplet cultures in the MISS Cell and characterized culture performance. Compared with solid plates, the MISS Cell generated a larger number of monoclonal colonies with higher initial growth rates using fewer resources. Finally, we established a workflow for automated high-throughput screening of Corynebacterium glutamicum using the MISS Cell and identified high glutamate-producing strains. The MISS Cell can serve as a universal platform to efficiently produce monoclonal colonies in high-throughput applications, overcoming the limitations of solid plates to promote rapid development in biotechnology.

Metabolic reprogramming and biosensor-assisted mutagenesis screening for high-level production of L-arginine in Escherichia coli

L-arginine is a value-added amino acid with promising applications in the pharmaceutical and nutraceutical industries. Further unleashing the potential of microbial cell factories to make L-arginine production more competitive remains challenging due to the sophisticated intracellular interaction networks and the insufficient knowledge of global metabolic regulation. Here, we combined multilevel rational metabolic engineering with biosensor-assisted mutagenesis screening to exploit the L-arginine production potential of Escherichia coli. First, multiple metabolic pathways were systematically reprogrammed to redirect the metabolic flux into L-arginine synthesis, including the L-arginine biosynthesis, TCA cycle, and L-arginine export. Specifically, a toggle switch responding to special cellular physiological conditions was designed to dynamically control the expression of sucA and pull more carbon flux from the TCA cycle toward L-arginine biosynthesis. Subsequently, a biosensor-assisted high-throughput screening platform was designed and applied to further exploit the L-arginine production potential. The best-engineered ARG28 strain produced 132 g/L L-arginine in a 5-L bioreactor with a yield of 0.51 g/g glucose and productivity of 2.75 g/(L ⋅ h), which were the highest values reported so far. Through whole genome sequencing and reverse engineering, Frc frameshift mutant, PqiB A78P mutant, and RpoB P564T mutant were revealed for enhancing the L-arginine biosynthesis. Our study exhibited the power of coupling rational metabolic reprogramming and biosensor-assisted mutagenesis screening to unleash the cellular potential for value-added metabolite production.

Strengthening microbial cell factories for efficient production of bioactive molecules

High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.

Optimizing the strain engineering process for industrial-scale production of bio-based molecules

Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.

A Flexible Data Evaluation System for Improving the Quality and Efficiency of Laboratory Analysis and Testing

In a chemical analysis laboratory, sample detection via most analytical devices obtains raw data and processes it to validate data reports, including raw data filtering, editing, effectiveness evaluation, error correction, etc. This process is usually carried out manually by analysts. When the sample detection volume is large, the data processing involved becomes time-consuming and laborious, and manual errors may be introduced. In addition, analytical laboratories typically use a variety of analytical devices with different measurement principles, leading to the use of various heterogeneous control software systems from different vendors with different export data formats. Different formats introduce difficulties to laboratory automation. This paper proposes a modular data evaluation system that uses a global unified management and maintenance mode that can automatically filter data, evaluate quality, generate valid reports, and distribute reports. This modular software design concept allows the proposed system to be applied to different analytical devices; its integration into existing laboratory information management systems (LIMS) could maximise automation and improve the analysis and testing quality and efficiency in a chemical analysis laboratory, while meeting the analysis and testing requirements.

Enabling Clonal Analyses of Yeast in Outer Space by Encapsulation and Desiccation in Hollow Microparticles

Studying microbes at the single-cell level in space can accelerate human space exploration both via the development of novel biotechnologies and via the understanding of cellular responses to space stressors and countermeasures. High-throughput technologies for screening natural and engineered cell populations can reveal cellular heterogeneity and identify high-performance cells. Here, we present a method to desiccate and preserve microbes in nanoliter-scale compartments, termed PicoShells, which are microparticles with a hollow inner cavity. In PicoShells, single cells are confined in an inner aqueous core by a porous hydrogel shell, allowing the diffusion of nutrients, wastes, and assay reagents for uninhibited cell growth and flexible assay protocols. Desiccated PicoShells offer analysis capabilities for single-cell derived colonies with a simple, low resource workflow, requiring only the addition of water to rehydrate hundreds of thousands of PicoShells and the single microbes encapsulated inside. Our desiccation method results in the recovery of desiccated microparticle morphology and porosity after a multi-week storage period and rehydration, with particle diameter and porosity metrics changing by less than 18% and 7%, respectively, compared to fresh microparticles. We also recorded the high viability of Saccharomyces cerevisiae yeast desiccated and rehydrated inside PicoShells, with only a 14% decrease in viability compared to non-desiccated yeast over 8.5 weeks, although we observed an 85% decrease in initial growth potential over the same duration. We show a proof-of-concept for a growth rate-based analysis of single-cell derived colonies in rehydrated PicoShells, where we identified 11% of the population that grows at an accelerated rate. Desiccated PicoShells thus provide a robust method for cell preservation before and during launch, promising a simple single-cell analysis method for studying heterogeneity in microbial populations in space.

Recent advances in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

Debaryomyces hansenii: an old acquaintance for a fresh start in the era of the green biotechnology

The halophilic yeast Debaryomyces hansenii has been studied for several decades, serving as eukaryotic model for understanding salt and osmotic tolerance. Nevertheless, lack of consensus among different studies is found and, sometimes, contradictory information derived from studies performed in very diverse conditions. These two factors hampered its establishment as the key biotechnological player that was called to be in the past decade. On top of that, very limited (often deficient) engineering tools are available for this yeast. Fortunately Debaryomyces is again gaining momentum and recent advances using highly instrumented lab scale bioreactors, together with advanced –omics and HT-robotics, have revealed a new set of interesting results. Those forecast a very promising future for D. hansenii in the era of the so-called green biotechnology. Moreover, novel genetic tools enabling precise gene editing on this yeast are now available. In this review, we highlight the most recent developments, which include the identification of a novel gene implicated in salt tolerance, a newly proposed survival mechanism for D. hansenii at very high salt and limiting nutrient concentrations, and its utilization as production host in biotechnological processes.

Advantages of optical fibers for facile and enhanced detection in droplet microfluidics

Droplet microfluidics offers a unique opportunity for ultrahigh-throughput experimentation with minimal sample consumption and thus has obtained increasing attention, particularly for biological applications. Detection and measurements of analytes or biomarkers in tiny droplets are essential for proper analysis of biological and chemical assays like single-cell studies, cytometry, nucleic acid detection, protein quantification, environmental monitoring, drug discovery, and point-of-care diagnostics. Current detection setups widely use microscopes as a central device and other free-space optical components. However, microscopic setups are bulky, complicated, not flexible, and expensive. Furthermore, they require precise optical alignments, specialized optical and technical knowledge, and cumbersome maintenance. The establishment of efficient, simple, and cheap detection methods is one of the bottlenecks for adopting microfluidic strategies for diverse bioanalytical applications and widespread laboratory use. Together with great advances in optofluidic components, the integration of optical fibers as a light guiding medium into microfluidic chips has recently revolutionized analytical possibilities. Optical fibers embedded in a microfluidic platform provide a simpler, more flexible, lower-cost, and sensitive setup for the detection of several parameters from biological and chemical samples and enable widespread, hands-on application much beyond thriving point-of-care developments. In this review, we examine recent developments in droplet microfluidic systems using optical fiber as a light guiding medium, primarily focusing on different optical detection methods such as fluorescence, absorbance, light scattering, and Raman scattering and the potential applications in biochemistry and biotechnology that are and will be arising from this.

Microbial production of chemicals driven by CRISPR-Cas systems

Microorganisms have provided an attractive route for biosynthesis of various chemicals from renewable resources. CRISPR-Cas systems have served as powerful mechanisms for generating cell factories with desirable properties by manipulating nucleic acids quickly and efficiently. The CRISPR-Cas system provides a toolbox with excellent opportunities for identifying better biocatalysts, multiplexed fine-tuning of metabolic flux, efficient utilization of low-cost substrates, and improvement of metabolic robustness. The overall goal of this review highlights recent advances in the development of microbial cell factories for chemical production using various CRISPR-Cas systems. The perspectives for further development or applications of CRISPR-Cas systems for strain improvement are also discussed.

A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.

Recent advances in droplet microfluidics for enzyme and cell factory engineering

Enzymes and cell factories play essential roles in industrial biotechnology for the production of chemicals and fuels. The properties of natural enzymes and cells often cannot meet the requirements of different industrial processes in terms of cost-effectiveness and high durability. To rapidly improve their properties and performances, laboratory evolution equipped with high-throughput screening methods and facilities is commonly used to tailor the desired properties of enzymes and cell factories, addressing the challenges of achieving high titer and the yield of the target products at high/low temperatures or extreme pH, in unnatural environments or in the presence of unconventional media. Droplet microfluidic screening (DMFS) systems have demonstrated great potential for exploring vast genetic diversity in a high-throughput manner (>10⁶/h) for laboratory evolution and have been increasingly used in recent years, contributing to the identification of extraordinary mutants. This review highlights the recent advances in concepts and methods of DMFS for library screening, including the key factors in droplet generation and manipulation, signal sources for sensitive detection and sorting, and a comprehensive summary of success stories of DMFS implementation for engineering enzymes and cell factories during the past decade.

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

Profiling Anticancer and Antioxidant Activities of Phenolic Compounds Present in Black Walnuts (Juglans nigra) Using a High-Throughput Screening Approach

Our recent studies have demonstrated multiple health-promoting benefits from black walnut kernels. These biological functions of black walnuts are likely associated with their bioactive constituents. Characterization of phenolic compounds found in black walnut could point out underexplored bioactive activities of black walnut extracts and promote the development of novel applications of black walnut and its by-products. In the present study, we assessed bioactivity profiles of phenolic compounds identified in the kernels of black walnuts using a high-throughput screening (HTS) approach. Black walnut phenolic compounds were evaluated in terms of their total antioxidant capacity, antioxidant response element (ARE) induction, and anticancer activities. The anticancer activities were identified by evaluating the effects of the phenolic compounds on the growth of the tumorigenic alveolar epithelial cells (A549) and non-tumorigenic lung fibroblast cells (MRC-5). Out of 16 phenolic compounds tested, several compounds (penta-O-galloyl-β-d-glucose, epicatechin gallate, quercetin, (–)-epicatechin, rutin, quercetin 3-β-d-glucoside, gallic acid, (+)-catechin, ferulic acid, syringic acid) exerted antioxidant activities that were significantly higher compared to Trolox, which was used as a control. Two phenolic compounds, penta-O-galloyl-β-d-glucose and quercetin 3-β-d-glucoside, exhibited antiproliferative activities against both the tumorigenic alveolar epithelial cells (A549) and non-tumorigenic lung fibroblast cells (MRC-5). The antioxidant activity of black walnut is likely driven not only by penta-O-galloyl-β-d-glucose but also by a combination of multiple phenolic compounds. Our findings suggested that black walnut extracts possibly possess anticancer activities and supported that penta-O-galloyl-β-d-glucose could be a potential bioactive agent for the cosmetic and pharmaceutical industries.

High-throughput screening for high-efficiency small-molecule biosynthesis

Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collections of engineered organism variants must be systematically created and evaluated through an enormous trial-and-error process in order to identify a manufacturing-ready strain. The high-throughput screening of strains to optimize their scalable manufacturing potential requires execution of many carefully controlled, parallel, miniature fermentations, followed by high-precision analysis of the resulting complex mixtures. This review discusses strategies for the design of high-throughput, small-scale fermentation models to predict improved strain performance at large commercial scale. Established and promising approaches from industrial and academic groups are presented for both cell culture and analysis, with primary focus on microplate- and microfluidics-based screening systems.

Imaging the invisible-Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags.

Challenges and opportunities with CRISPR activation in bacteria for data-driven metabolic engineering

Creating CRISPR gene activation (CRISPRa) technologies in industrially promising bacteria could be transformative for accelerating data-driven metabolic engineering and strain design. CRISPRa has been widely used in eukaryotes, but applications in bacterial systems have remained limited. Recent work shows that multiple features of bacterial promoters impose stringent requirements on CRISPRa-mediated gene activation. However, by systematically defining rules for effective bacterial CRISPRa sites and developing new approaches for encoding complex functions in engineered guide RNAs, there are now clear routes to generalize synthetic gene regulation in bacteria. When combined with multi-omics data collection and machine learning, the full development of bacterial CRISPRa will dramatically improve the ability to rapidly engineer bacteria for bioproduction through accelerated design-build-test-learn cycles.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

An overview of drug discovery and development

A new medicine will take an average of 10-15 years and more than US$2 billion before it can reach the pharmacy shelf. Traditionally, drug discovery relied on natural products as the main source of new drug entities, but was later shifted toward high-throughput synthesis and combinatorial chemistry-based development. New technologies such as ultra-high-throughput drug screening and artificial intelligence are being heavily employed to reduce the cost and the time of early drug discovery, but they remain relatively unchanged. However, are there other potentially faster and cheaper means of drug discovery? Is drug repurposing a viable alternative? In this review, we discuss the different means of drug discovery including their advantages and disadvantages.

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.