High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

Engineering bacteriophages for enhanced host range and efficacy: insights from bacteriophage-bacteria interactions

Bacteriophages, the most abundant organisms on earth, have the potential to address the rise of multidrug-resistant bacteria resulting from the overuse of antibiotics. However, their high specificity and limited host range can hinder their effectiveness. Phage engineering, through the use of gene editing techniques, offers a means to enhance the host range of bacteria, improve phage efficacy, and facilitate efficient cell-free production of phage drugs. To engineer phages effectively, it is necessary to understand the interaction between phages and host bacteria. Understanding the interaction between the receptor recognition protein of bacteriophages and host receptors can serve as a valuable guide for modifying or replacing these proteins, thereby altering the receptor range of the bacteriophage. Research and development focused on the CRISPR-Cas bacterial immune system against bacteriophage nucleic acids can provide the necessary tools to promote recombination and counter-selection in engineered bacteriophage programs. Additionally, studying the transcription and assembly functions of bacteriophages in host bacteria can facilitate the engineered assembly of bacteriophage genomes in non-host environments. This review highlights a comprehensive summary of phage engineering methods, including in-host and out-of-host engineering, and the use of high-throughput methods to understand their role. The main aim of these techniques is to harness the intricate interactions between bacteriophages and hosts to inform and guide the engineering of bacteriophages, particularly in the context of studying and manipulating the host range of bacteriophages. By employing advanced high-throughput methods to identify specific bacteriophage receptor recognition genes, and subsequently introducing modifications or performing gene swapping through in-host recombination or out-of-host synthesis, it becomes possible to strategically alter the host range of bacteriophages. This capability holds immense significance for leveraging bacteriophages as a promising therapeutic approach against antibiotic-resistant bacteria.

Efficient Ternary Mn-Based Spinel Oxide with Multiple Active Sites for Oxygen Evolution Reaction Discovered via High-Throughput Screening Methods

The discovery of more efficient and stable catalysts for oxygen evolution reaction (OER) is vital in improving the efficiency of renewable energy generation devices. Given the large numbers of possible binary and ternary metal oxide OER catalysts, high-throughput methods are necessary to accelerate the rate of discovery. Herein, Mn-based spinel oxide, Fe₁₀ Co₄₀ Mn₅₀ O, is identified for the first time using high-throughput methods demonstrating remarkable catalytic activity (overpotential of 310 mV on fluorine-doped tin oxide (FTO) substrate and 237 mV on Ni foam at 10 mA cm^-2 ). Using a combination of soft X-ray absorption spectroscopy and electrochemical measurements, the high catalytic activity is attributed to 1) the formation of multiple active sites in different geometric sites, tetrahedral and octahedral sites; and 2) the formation of active oxyhydroxide phase due to the strong interaction of Co²⁺ and Fe³⁺ . Structural and surface characterizations after OER show preservation of Fe₁₀ Co₄₀ Mn₅₀ O surface structure highlighting its durability against irreversible redox damage on the catalytic surface. This work demonstrates the use of a high-throughput approach for the rapid identification of a new catalyst, provides a deeper understanding of catalyst design, and addresses the urgent need for a better and stable catalyst to target greener fuel.

Examples of Structural Motifs in Viral Genomes and Approaches for RNA Structure Characterization

The relationship between conserved structural motifs and their biological function in the virus replication cycle is the interest of many researchers around the world. RNA structure is closely related to RNA function. Therefore, technological progress in high-throughput approaches for RNA structure analysis and the development of new ones are very important. In this mini review, we discuss a few perspectives on the structural elements of viral genomes and some methods used for RNA structure prediction and characterization. Based on the recent literature, we describe several examples of studies concerning the viral genomes, especially severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (IAV). Herein, we emphasize that a better understanding of viral genome architecture allows for the discovery of the structure-function relationship, and as a result, the discovery of new potential antiviral therapeutics.

Integrated High-Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-Resistant Alloys

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here an integrated approach is presented using a set of high-throughput (HTP) alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of CrFeMnNi alloys are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled in the corrosion of CrFeMnNi alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.

Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells

During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

Future Screening Prospects for Ovarian Cancer

Simple Summary

Ovarian cancer (OC) has the highest mortality rate of all gynecological cancers. It is usually diagnosed in late stages (FIGO III-IV), and therefore, overall survival is very poor. If diagnosed at the early stages, ovarian cancer has a 90% five-year survival rate. Liquid biopsy has a good potential to improve early ovarian cancer detection and is discussed in this review.

Abstract

Current diagnostic tools used in clinical practice such as transvaginal ultrasound, CA 125, and HE4 are not sensitive and specific enough to diagnose OC in the early stages. A lack of early symptoms and an effective asymptomatic population screening strategy leads to a poor prognosis in OC. New diagnostic and screening methods are urgently needed for early OC diagnosis. Liquid biopsies have been considered as a new noninvasive and promising method, using plasma/serum, uterine lavage, and urine samples for early cancer detection. We analyzed recent studies on molecular biomarkers with specific emphasis on liquid biopsy methods and diagnostic efficacy for OC through the detection of circulating tumor cells, circulating cell-free DNA, small noncoding RNAs, and tumor-educated platelets.

Small-angle X-ray scattering for the proteomics community: current overview and future potential

Introduction: Proteins are biological nanoparticles. For structural proteomics and hybrid structural biology, complementary methods are required that allow both high throughput and accurate automated data analysis. Small-angle X-ray scattering (SAXS) is a method for observing the size and shape of particles, such as proteins and complexes, in solution. SAXS data can be used to model both the structure, oligomeric state, conformational changes, and flexibility of biomolecular samples.Areas covered: The key principles of SAXS, its sample requirements, and its current and future applications for structural proteomics are briefly reviewed. Recent technical developments in SAXS experiments are discussed, and future potential of the method in structural proteomics is evaluated.Expert opinion: SAXS is a method suitable for several aspects of integrative structural proteomics, with current technical developments allowing for higher throughput and time-resolved studies, as well as the analysis of complex samples, such as membrane proteins. Increasing automation and streamlined data analysis are expected to equip SAXS for structure-based screening workflows. Originally, structural genomics had a heavy focus on folded, crystallizable proteins and complexes - SAXS is a method allowing an expansion of this focus to flexible and disordered systems.

Assembly of Multi-Spheroid Cellular Architectures by Programmable Droplet Merging

Artificial multicellular systems are gaining importance in the field of tissue engineering and regenerative medicine. Reconstruction of complex tissue architectures in vitro is nevertheless challenging, and methods permitting controllable and high-throughput fabrication of complex multicellular architectures are needed. Here, a facile and high-throughput method is developed based on a tunable droplet-fusion technique, allowing programmed assembly of multiple cell spheroids into complex multicellular architectures. The droplet-fusion technique allows for construction of various multicellular architectures (double-spheroids, multi-spheroids, hetero-spheroids) in a miniaturized high-density array format. As an example of application, the propagation of Wnt signaling is investigated within hetero-spheroids formed from two fused Wnt-releasing and Wnt-reporter cell spheroids. The developed method provides an approach for miniaturized, high-throughput construction of complex 3D multicellular architectures and can be applied for studying various biological processes including cell signaling, cancer invasion, embryogenesis, and neural development.

High-Throughput and Cost-Effective Characterization of Induced Pluripotent Stem Cells

Reprogramming somatic cells to induced pluripotent stem cells (iPSCs) offers the possibility of studying the molecular mechanisms underlying human diseases in cell types difficult to extract from living patients, such as neurons and cardiomyocytes. To date, studies have been published that use small panels of iPSC-derived cell lines to study monogenic diseases. However, to study complex diseases, where the genetic variation underlying the disorder is unknown, a sizable number of patient-specific iPSC lines and controls need to be generated. Currently the methods for deriving and characterizing iPSCs are time consuming, expensive, and, in some cases, descriptive but not quantitative. Here we set out to develop a set of simple methods that reduce cost and increase throughput in the characterization of iPSC lines. Specifically, we outline methods for high-throughput quantification of surface markers, gene expression analysis of in vitro differentiation potential, and evaluation of karyotype with markedly reduced cost.

High-Throughput, Label-Free Isolation of Cancer Stem Cells on the Basis of Cell Adhesion Capacity

Herein we report a microfluidics method that enriches cancer stem cells (CSCs) or tumor-initiating cells on the basis of cell adhesion properties. In our on-chip enrichment system, cancer cells were driven by hydrodynamic forces to flow through microchannels coated with basement membrane extract. Highly adhesive cells were captured by the functionalized microchannels, and less adhesive cells were collected from the outlets. Two heterogeneous breast cancer cell lines (SUM-149 and SUM-159) were successfully separated into enriched subpopulations according to their adhesive capacity, and the enrichment of the cancer stem cells was confirmed by flow cytometry biomarker analysis and tumor-formation assays. Our findings show that the less adhesive phenotype is associated with a higher percentage of CSCs, higher cancer-cell motility, and higher resistance to chemotherapeutic drugs.

Toxicity screening of diclofenac, propranolol, sertraline and simvastatin using Danio rerio and Paracentrotus lividus embryo bioassays

Early life-stage bioassays have been used as an alternative to short-term adult toxicity tests since they are cost-effective. A single couple can produce hundreds or thousands of embryos and hence can be used as a simple high-throughput approach in toxicity studies. In the present study, zebrafish and sea urchin embryo bioassays were used to test the toxicity of four pharmaceuticals belonging to different therapeutic classes: diclofenac, propranolol, simvastatin and sertraline. Simvastatin was the most toxic tested compound for zebrafish embryo, followed by diclofenac. Sertraline was the most toxic drug to sea urchin embryos, inducing development abnormalities at the ng/L range. Overall, our results highlight the potential of sea urchin embryo bioassay as a promising and sensitive approach for the high-throughput methods to test the toxicity of new chemicals, including pharmaceuticals, and identify several drugs that should go through more detailed toxicity assays.

Accelerated formulation development of monoclonal antibodies (mAbs) and mAb-based modalities: review of methods and tools

More therapeutic monoclonal antibodies and antibody-based modalities are in development today than ever before, and a faster and more accurate drug discovery process will ensure that the number of candidates coming to the biopharmaceutical pipeline will increase in the future. The process of drug product development and, specifically, formulation development is a critical bottleneck on the way from candidate selection to fully commercialized medicines. This article reviews the latest advances in methods of formulation screening, which allow not only the high-throughput selection of the most suitable formulation but also the prediction of stability properties under manufacturing and long-term storage conditions. We describe how the combination of automation technologies and high-throughput assays creates the opportunity to streamline the formulation development process starting from early preformulation screening through to commercial formulation development. The application of quality by design (QbD) concepts and modern statistical tools are also shown here to be very effective in accelerated formulation development of both typical antibodies and complex modalities derived from them.

Grape colour phenotyping: development of a method based on the reflectance spectrum

INTRODUCTION

The colour of fruit is an important quality factor for cultivar classification and phenotyping techniques. Besides the subjective visual evaluation, new instruments and techniques can be used.

OBJECTIVES

This work aims at developping an objective, fast, easy and non-destructive method as a useful support for evaluating grapes' colour under different cultural and environmental conditions, as well as for breeding process and germplasm evaluation, supporting the plant characterization and the biodiversity preservation.

MATERIALS AND METHODS

Colours of 120 grape varieties were studied using reflectance spectra. The classification was realized using cluster and discriminant analysis. Reflectance of the whole berries surface was also compared with absorption properties of single skin extracts.

RESULTS

A phenotyping method based on the reflectance spectra was developed, producing reliable colour classifications. A cultivar-independent index for pigment content evaluation has also been obtained.

CONCLUSIONS

This work allowed the classification of the berry colour using an objective method.

Facilities for macromolecular crystallography at the Helmholtz-Zentrum Berlin

Three macromolecular crystallography (MX) beamlines at the Helmholtz-Zentrum Berlin (HZB) are available for the regional, national and international structural biology user community. The state-of-the-art synchrotron beamlines for MX BL14.1, BL14.2 and BL14.3 are located within the low-β section of the BESSY II electron storage ring. All beamlines are fed from a superconducting 7 T wavelength-shifter insertion device. BL14.1 and BL14.2 are energy tunable in the range 5-16 keV, while BL14.3 is a fixed-energy side station operated at 13.8 keV. All three beamlines are equipped with CCD detectors. BL14.1 and BL14.2 are in regular user operation providing about 200 beam days per year and about 600 user shifts to approximately 50 research groups across Europe. BL14.3 has initially been used as a test facility and was brought into regular user mode operation during the year 2010. BL14.1 has recently been upgraded with a microdiffractometer including a mini-κ goniometer and an automated sample changer. Additional user facilities include office space adjacent to the beamlines, a sample preparation laboratory, a biology laboratory (safety level 1) and high-end computing resources. In this article the instrumentation of the beamlines is described, and a summary of the experimental possibilities of the beamlines and the provided ancillary equipment for the user community is given.

Arabidopsis thaliana as a model organism in systems biology

Significant progress has been made in identification of genes and gene networks involved in key biological processes. Yet, how these genes and networks are coordinated over increasing levels of biological complexity, from cells to tissues to organs, remains unclear. To address complex biological questions, biologists are increasingly using high-throughput tools and systems biology approaches to examine complex biological systems at a global scale. A system is a network of interacting and interdependent components that shape the system's unique properties. Systems biology studies the organization of system components and their interactions, with the idea that unique properties of that system can be observed only through study of the system as a whole. The application of systems biology approaches to questions in plant biology has been informative. In this review, we give examples of how systems biology is currently being used in Arabidopsis to investigate the transcriptional networks regulating root development, the metabolic response to stress, and the genetic regulation of metabolic variability. From these studies, we are beginning obtain sufficient data to generate more accurate models for system function. Further investigation of plant systems will require data gathering from specific cells and tissues, continued improvement in metabolic technologies, and novel computational methods for data visualization and modeling.

Autoinduction of protein expression

This unit contains protocols for the use of lactose-derived autoinduction in Escherichia coli. The protocols allow for reproducible expression trials to be undertaken with minimal user intervention. A basic protocol covers production of unlabeled proteins for functional studies. Alternate protocols for selenomethionine labeling for X-ray structural studies, and multi-well plate growth for screening and optimization are also included.

Caenorhabditis elegans: an emerging model in biomedical and environmental toxicology

The nematode Caenorhabditis elegans has emerged as an important animal model in various fields including neurobiology, developmental biology, and genetics. Characteristics of this animal model that have contributed to its success include its genetic manipulability, invariant and fully described developmental program, well-characterized genome, ease of maintenance, short and prolific life cycle, and small body size. These same features have led to an increasing use of C. elegans in toxicology, both for mechanistic studies and high-throughput screening approaches. We describe some of the research that has been carried out in the areas of neurotoxicology, genetic toxicology, and environmental toxicology, as well as high-throughput experiments with C. elegans including genome-wide screening for molecular targets of toxicity and rapid toxicity assessment for new chemicals. We argue for an increased role for C. elegans in complementing other model systems in toxicological research.