Life Sciences Discovery and Technology Highlights

High-Throughput Biology and Chemistry

Biosynthesis of Saxitoxin in Marine Dinoflagellates: An Omics Perspective

Saxitoxin is an alkaloid neurotoxin originally isolated from the clam Saxidomus giganteus in 1957. This group of neurotoxins is produced by several species of freshwater cyanobacteria and marine dinoflagellates. The saxitoxin biosynthesis pathway was described for the first time in the 1980s and, since then, it was studied in more than seven cyanobacterial genera, comprising 26 genes that form a cluster ranging from 25.7 to 35 kb in sequence length. Due to the complexity of the genomic landscape, saxitoxin biosynthesis in dinoflagellates remains unknown. In order to reveal and understand the dynamics of the activity in such impressive unicellular organisms with a complex genome, a strategy that can carefully engage them in a systems view is necessary. Advances in omics technology (the collective tools of biological sciences) facilitated high-throughput studies of the genome, transcriptome, proteome, and metabolome of dinoflagellates. The omics approach was utilized to address saxitoxin-producing dinoflagellates in response to environmental stresses to improve understanding of dinoflagellates’ gene–environment interactions. Therefore, in this review, the progress in understanding dinoflagellate saxitoxin biosynthesis using an omics approach is emphasized. Further potential applications of metabolomics and genomics to unravel novel insights into saxitoxin biosynthesis in dinoflagellates are also reviewed. (Akbar, M. A., et al. Mar. Drugs 2020, 18, E103.)

High-Throughput Identification of Synthetic Riboswitches by Barcode-Free Amplicon Sequencing in Human Cells

Synthetic riboswitches mediating ligand-dependent RNA cleavage or splicing modulation represent elegant tools to control gene expression in various applications, including next-generation gene therapy. However, due to the limited understanding of context-dependent structure–function relationships, the identification of functional riboswitches requires large-scale screening of aptamer–effector domain designs, which is hampered by the lack of suitable cellular high-throughput methods. Here Strobe et al. describe a fast and broadly applicable method to functionally screen complex riboswitch libraries (~1.8 × 10⁴ constructs) by cDNA amplicon sequencing in transiently transfected and stimulated human cells. The self-barcoding nature of each construct enables quantification of differential mRNA levels without additional preselection or cDNA manipulation steps. The authors apply this method to engineer tetracycline- and guanine-responsive ON and OFF switches based on hammerhead, hepatitis delta virus, and Twister ribozymes as well as U1-snRNP polyadenylation-dependent RNA devices. In summary, the authors’ method enables fast and efficient high-throughput riboswitch identification, thereby overcoming a major hurdle in the development cascade for therapeutically applicable gene switches. (Strobe, B., et al. Nat. Commun. 2020, 11, 714.)

Synthetic Antibody Discovery against Native Antigens by CRISPR/Cas9-Library Generation and Endoplasmic Reticulum Screening

Despite the significant advances of antibodies as therapeutic agents, there is still much room for improvement concerning the discovery of these macromolecules. Here, Ministry et al. present a new synthetic cell-based strategy that takes advantage of eukaryotic cell biology to produce highly diverse antibody libraries and, simultaneously, link them to a high-throughput selection mechanism, replicating B-cell diversification mechanisms. The interference of site-specific recognition by CRISPR/Cas9 with error-prone DNA repair mechanisms was explored for the generation of diversity, in a cell population containing a gene for a light-chain antibody fragment. The authors achieved up to 93% of cells containing a mutated antibody gene after diversification mechanisms, specifically inside one of the antigen-binding sites. This targeted variability strategy was then integrated into an intracellular selection mechanism. By fusing the antibody with a KDEL retention signal, the interaction of antibodies and native membrane antigens occurs inside the endoplasmic reticulum during the process of protein secretion, enabling the detection of high-quality leads for expression and affinity by flow cytometry. The authors successfully obtained antibody lead candidates against CD3 as proof of concept. In summary, Ministry et al. developed a novel antibody discovery platform against native antigens by endoplasmic synthetic library generation using CRISPR/Cas9, which will contribute to faster discovery of new biotherapeutic molecules, reducing the time to market. (Ministry, J. H., et al. Appl. Microbol. Biotechnol. 2020, 104, 2501–2512.)

Microfluidics

Robust RNA-Seq of aRNA-Amplified Single-Cell Material Collected by Patch Clamp

Most single-cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to repurpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single-cell RNA-Seq (“Patch-Seq”). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single-cell transcriptomes with cell location, morphology, and electrophysiology. Recent Patch-Seq studies utilize PCR amplification to increase the amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries—especially with the extremely high degrees of exponential amplification required for single-cell amounts of RNA. Kim et al. compared a PCR-based approach with linear amplifications and demonstrated that aRNA amplification (in vitro transcription [IVT]) is more sensitive and robust for single-cell RNA collected by a patch clamp pipette. (Kim, J. M. H., et al. Sci. Rep. 2020, 10, 1979.)

New Solutions to Capture and Enrich Bacteria from Complex Samples

Current solutions to diagnose bacterial infections, though reliable, are often time-consuming, laborious, and need a specific laboratory setting. There is an unmet need for bedside accurate diagnosis of infectious diseases with a short turnaround time. Moreover, low-cost diagnostics will greatly benefit regions with poor resources. Immunoassays and molecular techniques have been used to develop highly sensitive diagnosis solutions, but retaining many of the abovementioned limitations. The detection of bacteria in a biological sample can be enhanced by a previous step of capture and enrichment. This will ease the following process, enabling a more sensitive detection and increasing the possibility of a conclusive identification in the downstream diagnosis. This review explores the latest developments regarding the initial steps of capture and enrichment of bacteria from complex samples with the ultimate goal of designing low-cost and reliable diagnostics for bacterial infections. Some solutions use specific ligands tethered to magnetic constructs for separation under magnetic fields, microfluidic platforms, and engineered nanopatterned surfaces to trap bacteria. Bulk acoustics, advection, and nanofilters comprise some of the most innovative solutions for bacteria enrichment. (Sande, M. G., et al. Med. Microbiol. Immunol. 2020. doi: 10.1007/s00430-020-00659-1.)

Paper-Based Microfluidics for Electrochemical Applications

Paper-based microfluidics is characteristic of fluid transportation through spontaneous capillary action of paper and has exhibited great promise for a variety of applications, especially for sensing. Furthermore, paper-based microfluidics enables the design of miniaturized electrochemical devices to be applied in the energy sector, which is especially attractive for the rapid growing market of small-size disposable electronics. This review gives a brief summary on the basics of paper chemistry and capillary-driven microfluidic behavior, and highlights recent advances of paper-based microfluidics in developing electrochemical sensing devices and miniaturized energy storage/conversion devices. Their structural features, working principles, and exemplary applications are comprehensively elaborated and discussed. Additionally, this review also points out the existing challenges and future opportunities of paper-based microfluidic electronics. (Shan, L. L., et al. Chem. Electrochem. 2020, 7, 10–30.)

Advances in Artificial Intelligence and Machine Learning

Accuracy of a Machine Learning Muscle MRI-Based Tool for the Diagnosis of Muscular Dystrophies

Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. The authors’ aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs. Verdú-Diaz et al. collected 976 pelvic and lower-limb T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using the Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with the highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by four specialists in the field. A total of 976 lower-limb MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts in the field, the diagnostic accuracy of the model generated was significantly higher in the new set of 20 MRIs. Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests. This study provides class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies. (Verdú-Diaz, J., et al. Neurology 2020, 94, e1094–e1102.)

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Precision/personalized medicine is a hot topic in healthcare. Often presented with the motto “the right drug, for the right patient, at the right dose, and the right time,” precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the “sugar code” (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, the authors discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, Özdemir et al. present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open-ended than sequencing of a finite-length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the “sugar lag” in precision/personalized medicine diagnostics over the past decades. Özdemir et al. conclude by introducing systems scientists, physicians, and the biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in healthcare, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others. (Özdemir, V., et al. OMICS 2020, 24, 57–59.)

Analyses of Noncoding Somatic Drivers in 2658 Cancer Whole Genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes. Here Rheinbay et al. present analyses of driver point mutations and structural variants in noncoding regions across 2658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, the authors developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, the authors present two methods of driver discovery and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. The authors’ analyses confirm previously reported drivers, raise doubts about others, and identify novel candidates, including point mutations in the 5′ region of TP53 and in the 3′ untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4, and rearrangements in the loci of AKR1C genes. The authors show that although point mutations and structural variants that drive cancer are less frequent in noncoding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available. (Rheinbay, E., et al. Nature 2020, 578, 102–111.)

Machine Intelligence in Peptide Therapeutics: A Next-Generation Tool for Rapid Disease Screening

Discovery and development of biopeptides are time-consuming, laborious, and dependent on various factors. Data-driven computational methods, especially the machine learning (ML) approach, can rapidly and efficiently predict the utility of therapeutic peptides. ML methods offer an array of tools that can accelerate and enhance decision-making and discovery for well-defined queries with ample and sophisticated data quality. Various ML approaches, such as support vector machines, random forest, extremely randomized tree, and, more recently, deep learning methods, are useful in peptide-based drug discovery. These approaches leverage the peptide data sets, created via high-throughput sequencing and computational methods, and enable the prediction of functional peptides with increased levels of accuracy. The use of ML approaches in the development of peptide-based therapeutics is relatively recent; however, these techniques are already revolutionizing protein research by unraveling their novel therapeutic peptide functions. In this review, the authors discuss several ML-based state-of-the-art peptide prediction tools and compare these methods in terms of their algorithms, feature encodings, prediction scores, evaluation methodologies, and software utilities. Basith et al. also assessed the prediction performance of these methods using well-constructed independent data sets. In addition, Basith et al. discuss the common pitfalls and challenges of using ML approaches for peptide therapeutics. Overall, the authors show that using ML models in peptide research can streamline the development of targeted peptide therapies. (Basith, S., et al. Med. Res. Rev. 2020. doi: 10.1002/med.21658.)