Large-Scale, Quantitative Protein Assays on a High-Throughput DNA Sequencing Chip

Enzoology: understanding enzyme interactions and epistasis in the cell

Recent work from Nguyen et al. unveils massively parallel measurements of epistatic interactions between two enzymes, dihydrofolate reductase and thymidylate synthase, in their natural cellular context. Almost 3000 mutations of DHFR in three TYMS backgrounds reveal a complex interaction network. The authors capture much of this complexity using a simple model.

High-throughput discovery of inhibitory protein fragments with AlphaFold

Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind to protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length proteins in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. Comparisons with experimental measurements establish that our approach is a sensitive predictor of fragment function: Evaluating inhibitory fragments from known protein-protein interaction interfaces, we find 87% are predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. Deep mutational scanning experiments support the predicted binding modes and uncover superior inhibitory peptides in high throughput. Further, FragFold is able to predict previously unknown protein binding modes, explaining prior genetic and biochemical data. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.

Massively parallel analysis of single-molecule dynamics on next-generation sequencing chips

Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe multiplexed single-molecule characterization at the library scale (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profiled the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding-rewinding dynamics. The ability to explore a large sequence space for Cas9 allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes.

The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.

Fluorogenic Aptamer Optimizations on a Massively Parallel Sequencing Platform

F luorogenic ap tamers (FAPs) have become an increasingly important tool in cellular sensing and pathogen diagnostics. However, fine-tuning FAPs for enhanced performance remains challenging even with the structural details provided by X-ray crystallography. Here we present a novel approach to optimize a DNA-based FAP (D-FAP), Lettuce, on repurposed Illumina next-generation sequencing (NGS) chips. When substituting its cognate chromophore, DFHBI-1T, with TO1-biotin, Lettuce not only shows a red-shifted emission peak by 53 nm (from 505 to 558 nm), but also a 4-fold bulk fluorescence enhancement. After screening 8,821 Lettuce variants complexed with TO1-biotin, the C14T mutation is found to exhibit an improved apparent dissociated constant ( vs. 0.82 µM), an increased quantum yield (QY: 0.62 vs. 0.59) and an elongated fluorescence lifetime (τ: 6.00 vs. 5.77 ns), giving 45% more ensemble fluorescence than the canonical Lettuce/TO1-biotin complex. Molecular dynamic simulations further indicate that the π-π stacking interaction is key to determining the coordination structure of TO1-biotin in Lettuce. Our screening-and-simulation pipeline can effectively optimize FAPs without any prior structural knowledge of the canonical FAP/chromophore complexes, providing not only improved molecular probes for fluorescence sensing but also insights into aptamer-chromophore interactions.

Ribosome Pool Engineering Increases Protein Biosynthesis Yields

The biosynthetic capability of the bacterial ribosome motivates efforts to understand and harness sequence-optimized versions for synthetic biology. However, functional differences between natively occurring ribosomal RNA (rRNA) operon sequences remain poorly characterized. Here, we use an in vitro ribosome synthesis and translation platform to measure protein production capabilities of ribosomes derived from all unique combinations of 16S and 23S rRNAs from seven distinct Escherichia coli rRNA operon sequences. We observe that polymorphisms that distinguish native E. coli rRNA operons lead to significant functional changes in the resulting ribosomes, ranging from negligible or low gene expression to matching the protein production activity of the standard rRNA operon B sequence. We go on to generate strains expressing single rRNA operons and show that not only do some purified in vivo expressed homogeneous ribosome pools outperform the wild-type, heterogeneous ribosome pool but also that a crude cell lysate made from the strain expressing only operon A ribosomes shows significant yield increases for a panel of medically and industrially relevant proteins. We anticipate that ribosome pool engineering can be applied as a tool to increase yields across many protein biomanufacturing systems, as well as improve basic understanding of ribosome heterogeneity and evolution.

Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening

Developing therapeutic antibodies is laborious and costly. Here we report a method for antibody discovery that leverages the Illumina HiSeq platform to, within 3 days, screen in the order of 108 antibody-antigen interactions. The method, which we named 'deep screening', involves the clustering and sequencing of antibody libraries, the conversion of the DNA clusters into complementary RNA clusters covalently linked to the instrument's flow-cell surface on the same location, the in situ translation of the clusters into antibodies tethered via ribosome display, and their screening via fluorescently labelled antigens. By using deep screening, we discovered low-nanomolar nanobodies to a model antigen using 4 × 106 unique variants from yeast-display-enriched libraries, and high-picomolar single-chain antibody fragment leads for human interleukin-7 directly from unselected synthetic repertoires. We also leveraged deep screening of a library of 2.4 × 105 sequences of the third complementarity-determining region of the heavy chain of an anti-human epidermal growth factor receptor 2 (HER2) antibody as input for a large language model that generated new single-chain antibody fragment sequences with higher affinity for HER2 than those in the original library.

Recent advances in generative biology for biotherapeutic discovery

Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.

Massively parallel profiling of RNA-targeting CRISPR-Cas13d

CRISPR-Cas13d cleaves RNA and is used in vivo and for diagnostics. However, a systematic understanding of its RNA binding and cleavage specificity is lacking. Here, we describe an RNA Chip-Hybridized Association-Mapping Platform (RNA-CHAMP) for measuring the binding affinity for > 10,000 RNAs containing structural perturbations and other alterations relative to the CRISPR RNA (crRNA). Deep profiling of Cas13d reveals that it does not require a protospacer flanking sequence but is exquisitely sensitive to secondary structure within the target RNA. Cas13d binding is penalized by mismatches in the distal crRNA-target RNA region, while alterations in the proximal region inhibit nuclease activity. A biophysical model built from these data reveals that target recognition initiates in the distal end of the target RNA. Using this model, we design crRNAs that can differentiate between SARS-CoV-2 variants by modulating nuclease activation. This work describes the key determinants of RNA targeting by a type VI CRISPR enzyme.

A rapid cell-free expression and screening platform for antibody discovery

Antibody discovery is bottlenecked by the individual expression and evaluation of antigen-specific hits. Here, we address this bottleneck by developing a workflow combining cell-free DNA template generation, cell-free protein synthesis, and binding measurements of antibody fragments in a process that takes hours rather than weeks. We apply this workflow to evaluate 135 previously published antibodies targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including all 8 antibodies previously granted emergency use authorization for coronavirus disease 2019 (COVID-19), and demonstrate identification of the most potent antibodies. We also evaluate 119 anti-SARS-CoV-2 antibodies from a mouse immunized with the SARS-CoV-2 spike protein and identify neutralizing antibody candidates, including the antibody SC2-3, which binds the SARS-CoV-2 spike protein of all tested variants of concern. We expect that our cell-free workflow will accelerate the discovery and characterization of antibodies for future pandemics and for research, diagnostic, and therapeutic applications more broadly.

Massively Parallel Profiling of RNA-targeting CRISPR-Cas13d

Type VI CRISPR enzymes cleave target RNAs and are widely used for gene regulation, RNA tracking, and diagnostics. However, a systematic understanding of their RNA binding specificity and cleavage activation is lacking. Here, we describe RNA chip-hybridized association-mapping platform (RNA-CHAMP), a massively parallel platform that repurposes next-generation DNA sequencing chips to measure the binding affinity for over 10,000 RNA targets containing structural perturbations, mismatches, insertions, and deletions relative to the CRISPR RNA (crRNA). Deep profiling of Cas13d, a compact and widely used RNA nuclease, reveals that it does not require a protospacer flanking sequence (PFS) but is exquisitely sensitive to secondary structure within the target RNA. Cas13d binding is strongly penalized by mismatches, insertions, and deletions in the distal crRNA-target RNA regions, while alterations in the proximal region inhibit nuclease activity without affecting binding. A biophysical model built from these data reveals that target recognition begins at the distal end of unstructured target RNAs and proceeds to the proximal end. Using this model, we designed a series of partially mismatched guide RNAs that modulate nuclease activity to detect single nucleotide polymorphisms (SNPs) in circulating SARS-CoV-2 variants. This work describes the key determinants of RNA targeting by a type VI CRISPR enzyme to improve CRISPR diagnostics and in vivo RNA editing. More broadly, RNA-CHAMP provides a quantitative platform for systematically measuring protein-RNA interactions.

Point-of-Care Peptide Hormone Production Enabled by Cell-Free Protein Synthesis

In resource-limited settings, it can be difficult to safely deliver sensitive biologic medicines to patients due to cold chain and infrastructure constraints. Point-of-care drug manufacturing could circumvent these challenges since medicines could be produced locally and used on-demand. Toward this vision, we combine cell-free protein synthesis (CFPS) and a 2-in-1 affinity purification and enzymatic cleavage scheme to develop a platform for point-of-care drug manufacturing. As a model, we use this platform to synthesize a panel of peptide hormones, an important class of medications that can be used to treat a wide variety of diseases including diabetes, osteoporosis, and growth disorders. With this approach, temperature-stable lyophilized CFPS reaction components can be rehydrated with DNA encoding a SUMOylated peptide hormone of interest when needed. Strep-Tactin affinity purification and on-bead SUMO protease cleavage yield peptide hormones in their native form that are recognized by ELISA antibodies and that can bind their respective receptors. With further development to ensure proper biologic activity and patient safety, we envision that this platform could be used to manufacture valuable peptide hormone drugs in a decentralized way.

Characterization of cuproptosis identified immune microenvironment and prognosis in acute myeloid leukemia

BACKGROUND

Recent studies have reported that cuproptosis, a novel cell death pathway, strongly correlates with mitochondrial metabolism. In addition, the studies reported that cuproptosis plays a role in the development of several cancers and is regulated by protein lipoylation. During cuproptosis, copper binds to the lipoylated proteins and mediates cancer progression. However, the role of cuproptosis in acute myeloid leukemia (AML) patients is yet to be explored.

METHODS

This study curated seven cuproptosis-related-genes (CRGs): FDX1, DLAT, PDHB, PDHA1, DLD, LIAS, and LIPT1 to determine cuproptosis modification patterns and the CRGs signature in AML. The CIBERSORT and ssGSEA algorithms were utilized to evaluate the infiltration levels of different immune cell subtypes. A cuproptosis score system based on differentially expressed genes (DEGs) was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The developed cuproptosis score system was validated using two immunotherapy datasets, IMvigor210 and GSE78220.

RESULTS

Three distinct cuproptosis regulation patterns were identified using the Beat AML cohort. The results demonstrated that the three cuproptosis regulation patterns were correlated with various biological pathways and clinical outcomes. Tumor microenvironment (TME) characterization revealed that the identified cuproptosis regulation patterns were consistent with three immune profiles: immune-desert, immune-inflamed, and immune-excluded. The AML patients were grouped into low- and high-score groups based on the cuproptosis score system abstracted from 486 cuproptosis-related DEGs. Patients with lower cuproptosis scores were characterized by longer survival time and attenuated immune infiltration. It was found that lower cuproptosis scores were strongly correlated with lower somatic mutation frequency. Moreover, patients with lower cuproptosis scores presented more favorable immune responses and dual clinical benefits among external validation cohorts.

CONCLUSIONS

Cuproptosis phenotypes are significantly correlated with immune microenvironment complexity and variety. Cuprotopsis regulates the response of cancer cells to the immune system. Quantitatively assessing cuproptosis phenotypes in AML improves the understanding and knowledge regarding immune microenvironment characteristics and promotes the development of therapeutic interventions.

High-throughput biochemistry in RNA sequence space: predicting structure and function

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

Construction of a potentially functional lncRNA-miRNA-mRNA network in sepsis by bioinformatics analysis

Objective: Sepsis is a common disease in internal medicine, with a high incidence and dangerous condition. Due to the limited understanding of its pathogenesis, the prognosis is poor. The goal of this project is to screen potential biomarkers for the diagnosis of sepsis and to identify competitive endogenous RNA (ceRNA) networks associated with sepsis.

Methods: The expression profiles of long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) were derived from the Gene Expression Omnibus (GEO) dataset. The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened by bioinformatics analysis. DEmRNAs were analyzed by protein-protein interaction (PPI) network analysis, transcription factor enrichment analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Set Enrichment Analysis (GSEA). After the prediction of the relevant database, the competitive ceRNA network is built in Cytoscape. The gene-drug interaction was predicted by DGIgb. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm five lncRNAs from the ceRNA network.

Results: Through Venn diagram analysis, we found that 57 DElncRNAs, 6 DEmiRNAs and 317 DEmRNAs expressed abnormally in patients with sepsis. GO analysis and KEGG pathway analysis showed that 789 GO terms and 36 KEGG pathways were enriched. Through intersection analysis and data mining, 5 key KEGG pathways and related core genes were revealed by GSEA. The PPI network consists of 247 nodes and 1,163 edges, and 50 hub genes are screened by the MCODE plug-in. In addition, there are 5 DElncRNAs, 6 DEmiRNAs and 28 DEmRNAs in the ceRNA network. Drug action analysis showed that 7 genes were predicted to be molecular targets of drugs. Five lncRNAs in ceRNA network are verified by qRT-PCR, and the results showed that the relative expression of five lncRNAs was significantly different between sepsis patients and healthy control subjects.

Conclusion: A sepsis-specific ceRNA network has been effectively created, which is helpful to understand the interaction between lncRNAs, miRNAs and mRNAs. We discovered prospective sepsis peripheral blood indicators and proposed potential treatment medicines, providing new insights into the progression and development of sepsis.

CasPlay provides a gRNA-barcoded CRISPR-based display platform for antibody repertoire profiling

Protein display technologies link proteins to distinct nucleic acid sequences (barcodes), enabling multiplexed protein assays via DNA sequencing. Here, we develop Cas9 display (CasPlay) to interrogate customized peptide libraries fused to catalytically inactive Cas9 (dCas9) by sequencing the guide RNA (gRNA) barcodes associated with each peptide. We first confirm the ability of CasPlay to characterize antibody epitopes by recovering a known binding motif for a monoclonal anti-FLAG antibody. We then use a CasPlay library tiling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteome to evaluate vaccine-induced antibody reactivities. Using a peptide library representing the human virome, we demonstrate the ability of CasPlay to identify epitopes across many viruses from microliters of patient serum. Our results suggest that CasPlay is a viable strategy for customized protein interaction studies from highly complex libraries and could provide an alternative to phage display technologies.

Massively Parallel Selection of NanoCluster Beacons

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.

Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis

Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. "Gene ontology (GO)" and "Kyoto Encyclopedia of Genes and Genomes (KEGG)" data were analyzed by the "DAVID" software. The online search tool "STRING" was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using "GEPIA" with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.

Exploring molecular biology in sequence space: The road to next-generation single-molecule biophysics

Next-generation sequencing techniques have led to a new quantitative dimension in the biological sciences. In particular, integrating sequencing techniques with biophysical tools allows sequence-dependent mechanistic studies. Using the millions of DNA clusters that are generated during sequencing to perform high-throughput binding affinity and kinetics measurements enabled the construction of energy landscapes in sequence space, uncovering relationships between sequence, structure, and function. Here, we review the approaches to perform ensemble fluorescence experiments on next-generation sequencing chips for variations of DNA, RNA, and protein sequences. As the next step, we anticipate that these fluorescence experiments will be pushed to the single-molecule level, which can directly uncover kinetics and molecular heterogeneity in an unprecedented high-throughput fashion. Molecular biophysics in sequence space, both at the ensemble and single-molecule level, leads to new mechanistic insights. The wide spectrum of applications in biology and medicine ranges from the fundamental understanding of evolutionary pathways to the development of new therapeutics.

An open source toolkit for repurposing Illumina sequencing systems as versatile fluidics and imaging platforms

Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument, comprising an epifluorescence microscope with integrated fluidics, into an open platform for programmable applications without need for specialized engineering or software development expertise. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. Given the widespread availability of HiSeq 2500 platforms and the simplicity of the modifications required to repurpose these systems, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.

MicroRNA-146a Serves as a Biomarker for Adverse Prognosis of ST-Segment Elevation Myocardial Infarction

Objective

This study is aimed at exploring the underlying molecular mechanisms of ST-segment elevation myocardial infarction (STEMI) and provides potential clinical prognostic biomarkers for STEMI.

Methods

The GSE60993 dataset was downloaded from the GEO database, and the differentially expressed genes (DEGs) between STEMI and control groups were screened. Enrichment analysis of the DEGs was subsequently performed using the DAVID database. A protein-protein interaction network was constructed, and hub genes were identified. The hub genes in patients were then validated by quantitative reverse transcription-PCR. Furthermore, hub gene-miRNA interactions were evaluated using the miRTarBase database. Finally, patient data on classical cardiovascular risk factors were collected, and plasma microRNA-146a (miR-146a) levels were detected. An individualized nomogram was constructed based on multivariate Cox regression analysis.

Results

A total of 239 DEGs were identified between the STEMI and control groups. Expression of S100A12 and miR-146a was significantly upregulated in STEMI samples compared with controls. STEMI patients with high levels of miR-146a had a higher risk of major adverse cardiovascular events (MACEs) than those with low levels of miR-146a (log-rank P = 0.034). Multivariate Cox regression analysis identified five statistically significant variables, including age, hypertension, diabetes mellitus, white blood cells, and miR-146a. A nomogram was constructed to estimate the likelihood of a MACE at one, two, and three years after STEMI.

Conclusion

The incidence of MACEs in STEMI patients expressing high levels of miR-146a was significantly greater than in those expressing low levels. MicroRNA-146a can serve as a biomarker for adverse prognosis of STEMI and might function in its pathogenesis by targeting S100A12, which may exert its role via an inflammatory response. In addition, our study presents a valid and practical model to assess the probability of MACEs within three years of STEMI.

CRISPR-based peptide library display and programmable microarray self-assembly for rapid quantitative protein binding assays

CRISPR-inspired systems have been extensively developed for applications in genome editing and nucleic acid detection. Here, we introduce a CRISPR-based peptide display technology to facilitate customized, high-throughput in vitro protein interaction studies. We show that bespoke peptide libraries fused to catalytically inactive Cas9 (dCas9) and barcoded with unique single guide RNA (sgRNA) molecules self-assemble from a single mixed pool to programmable positions on a DNA microarray surface for rapid, multiplexed binding assays. We develop dCas9-displayed saturation mutagenesis libraries to characterize antibody-epitope binding for a commercial anti-FLAG monoclonal antibody and human serum antibodies. We also show that our platform can be used for viral epitope mapping and exhibits promise as a multiplexed diagnostics tool. Our CRISPR-based peptide display platform and the principles of complex library self-assembly using dCas9 could be adapted for rapid interrogation of varied customized protein libraries or biological materials assembly using DNA scaffolding.

High-Throughput Selection and Characterisation of Aptamers on Optical Next-Generation Sequencers

Aptamers feature a number of advantages, compared to antibodies. However, their application has been limited so far, mainly because of the complex selection process. 'High-throughput sequencing fluorescent ligand interaction profiling' (HiTS-FLIP) significantly increases the selection efficiency and is consequently a very powerful and versatile technology for the selection of high-performance aptamers. It is the first experiment to allow the direct and quantitative measurement of the affinity and specificity of millions of aptamers simultaneously by harnessing the potential of optical next-generation sequencing platforms to perform fluorescence-based binding assays on the clusters displayed on the flow cells and determining their sequence and position in regular high-throughput sequencing. Many variants of the experiment have been developed that allow automation and in situ conversion of DNA clusters into base-modified DNA, RNA, peptides, and even proteins. In addition, the information from mutational assays, performed with HiTS-FLIP, provides deep insights into the relationship between the sequence, structure, and function of aptamers. This enables a detailed understanding of the sequence-specific rules that determine affinity, and thus, supports the evolution of aptamers. Current variants of the HiTS-FLIP experiment and its application in the field of aptamer selection, characterisation, and optimisation are presented in this review.

Systems Approaches to Unravel T Cell Function and Therapeutic Potential in Autoimmune Disease

Producing Ag-specific immune responses constrained to target tissues or cells that can be engaged or disengaged at will is predicated on understanding the network of genes governing immune cell function, defining the rules underlying Ag specificity, and synthesizing the tools to engineer them. The successes and limitations of chimeric Ag receptor (CAR) T cells emphasize this goal, and advances in high-throughput sequencing, large-scale genomic screens, single-cell profiling, and genetic modification are providing the necessary data to bring it to fruition-including a broader application into the treatment of autoimmune diseases. In this review, we delve into the implementation of these developments, survey the relevant works, and propose a framework for generating the next generation of synthetic T cells informed by the principles learned from these systems approaches.

Construction of a ceRNA immunoregulatory network related to the development of vascular dementia through a weighted gene coexpression network analysis

Background

To date, vascular dementia (VaD) lacks effective treatment in clinical practice. There is also growing evidence that VaD may be closely related to the immune response. The development of high-throughput technology, and the recently discovered group of new mediators called competitive endogenous RNAs (ceRNA), provides a unique opportunity to study the immunomodulation of VaD.

Methods

In this study, we used gene expression profiles in the Gene Expression Omnibus (GEO) database to obtain immune-related gene coexpression modules through a weighted gene coexpression network analysis (WGCNA) and gene enrichment analysis. We extracted and merged long non-coding RNA (lncRNA) and microRNA (miRNA) expressions from the GEO database and mapped them with related databases. Subsequently, we used Cytoscape to construct a lncRNA-miRNA-mRNA network, and then we performed an enrichment analysis on the mRNAs in the network to determine their regulatory function. Subsequently, we used an ImmuCellAI immune infiltration analysis and constructed a ceRNA sub-network of related immune cells. Finally, we conducted a gene set enrichment analysis (GSEA) to determine the potential regulatory pathways of the key factors.

Results

As a result, we identified the blue module as the key module of immunity and constructed the related CeRNA network. Immune infiltration analysis showed that natural killer T (NKT) cells may be the key immune cells of VaD. Using a Pearson correlation analysis, we identified that B4GALT1, PPP1R3B, MICB, HHAT, DSC2, DNA2, SCARA3, and lncRNA NEAT1 may be the key factors of VaD. Our subsequent GSEA analysis showed that lncRNA NEAT1 may be regulated by NK cells and toll-like receptors.

Conclusions

Our research provides new therapeutic targets for vascular dementia from the immunological perspective for the first time, including B4GALT1, PPP1R3B, MICB, HHAT, DSC2, DNA2, SCARA3, and lncRNA NEAT1, and our research hopes to provide new treatment options for VaD.

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.

Substitutional landscape of a split fluorescent protein fragment using high-density peptide microarrays

Split fluorescent proteins have wide applicability as biosensors for protein-protein interactions, genetically encoded tags for protein detection and localization, as well as fusion partners in super-resolution microscopy. We have here established and validated a novel platform for functional analysis of leave-one-out split fluorescent proteins (LOO-FPs) in high throughput and with rapid turnover. We have screened more than 12,000 variants of the beta-strand split fragment using high-density peptide microarrays for binding and functional complementation in Green Fluorescent Protein. We studied the effect of peptide length and the effect of different linkers to the solid support. We further mapped the effect of all possible amino acid substitutions on each position as well as in the context of some single and double amino acid substitutions. As all peptides were tested in 12 duplicates, the analysis rests on a firm statistical basis allowing for confirmation of the robustness and precision of the method. Based on experiments in solution, we conclude that under the given conditions, the signal intensity on the peptide microarray faithfully reflects the binding affinity between the split fragments. With this, we are able to identify a peptide with 9-fold higher affinity than the starting peptide.

PURE mRNA display and cDNA display provide rapid detection of core epitope motif via high-throughput sequencing

The reconstructed in vitro translation system known as the PURE system has been used in a variety of cell-free experiments such as the expression of native and de novo proteins as well as various display methods to select for functional polypeptides. We developed a refined PURE-based display method for the preparation of stable messenger RNA (mRNA) and complementary DNA (cDNA)-peptide conjugates and validated its utility for in vitro selection. Our conjugate formation efficiency exceeded 40%, followed by gel purification to allow minimum carry-over of components from the translation system to the downstream assay enabling clean and efficient random peptide sequence screening. We chose the commercially available anti-FLAG M2 antibody as a target molecule for validation. Starting from approximately 1.7 × 10¹² random sequences, a round-by-round high-throughput sequencing showed clear enrichment of the FLAG epitope DYKDDD as well as revealing consensus FLAG epitope motif DYK(D/L/N)(L/Y/D/N/F)D. Enrichment of core FLAG motifs lacking one of the four key residues (DYKxxD) indicates that Tyr (Y) and Lys (K) appear as the two key residues essential for binding. Furthermore, the comparison between mRNA display and cDNA display method resulted in overall similar performance with slightly higher enrichment for mRNA display. We also show that gel purification steps in the refined PURE-based display method improve conjugate formation efficiency and enhance the enrichment rate of FLAG epitope motifs in later rounds of selection especially for mRNA display. Overall, the generalized procedure and consistent performance of two different display methods achieved by the commercially available PURE system will be useful for future studies to explore the sequence and functional space of diverse polypeptides.

Comprehensive Analysis of ceRNA Regulation Network Involved in the Development of Coronary Artery Disease

Background

Coronary artery disease (CAD) is one of the most common causes of sudden death with high morbidity in recent years. This paper is aimed at exploring the early peripheral blood biomarkers of sudden death and providing a new perspective for clinical diagnosis and forensic pathology identification by integrated bioinformatics analysis.

Methods

Two microarray expression profiling datasets (GSE113079 and GSE31568) were downloaded from the Gene Expression Omnibus (GEO) database, and we identified differentially expressed lncRNAs, miRNAs, and mRNAs in CAD. Gene Ontology (GO) and KEGG pathway analyses of DEmRNAs were executed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified. Finally, we constructed a competitive endogenous RNA (ceRNA) regulation network among lncRNAs, miRNAs, and mRNAs. Also, the 5 miRNAs of the ceRNA network were verified by RT-PCR.

Results

In total, 86 DElncRNAs, 148 DEmiRNAs, and 294 DEmRNAs were dysregulated in CAD. We received 12 GO terms and 5 pathways of DEmRNAs. 31 nodes and 78 edges were revealed in the PPI network. The top 10 genes calculated by degree method were identified as hub genes. Moreover, there were a total of 26 DElncRNAs, 5 DEmiRNAs, and 13 DEmRNAs in the ceRNA regulation network. We validated the 5 miRNAs of the ceRNA network by RT-PCR, which were consistent with the results of the microarray.

Conclusions

In this paper, a CAD-specific ceRNA network was successfully constructed, contributing to the understanding of the relationship among lncRNAs, miRNAs, and mRNAs. We identified potential peripheral blood biomarkers in CAD and provided novel insights into the development and progress of CAD.

In vivo mRNA display enables large-scale proteomics by next generation sequencing

Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.

Suppressing the Ring Stain Effect with Superhydrophilic/Superhydrophobic Patterned Surfaces

The ring stain phenomenon is a critical hindrance to the distribution of the solute during drying for biochemical assays and materials deposition. Herein, we developed a substrate, characterized with hydrophilic spots surrounded by hydrophobic areas, to suppress the ring stain effect, and fabricated four kinds of patterned surfaces to investigate the relationship between the surface free energy and ring-suppressing performance. We found that during the evaporation process, a drop was constrained on the hydrophilic spot with a pinned contact line, and the ring stain effect was suppressed significantly. The suppressing performance of the ring stain effect increases with surface free energy differences between the hydrophilic and hydrophobic regions.

Differences in serum proteins in traditional Chinese medicine constitutional population: Analysis and verification

Traditional Chinese medicine assigns individuals into different categories called "constitutions" to help guide the clinical treatment according to subjective physiologic, psychologic analyses, large-scale clinical observations, and epidemiologic studies. To further explore more objective expressions of constitutions, antibody microarrays were used to analyze the serologic protein profiles of two different constitutions, a balanced (or healthy) constitution (BC) and the dampness constitution (DC) comprising phlegm-dampness and damp-heat constitutions. The profiles of changing constitutions across time were also analyzed. Nineteen differentially expressed proteins between the two groups were identified, with known biologic functions involved in immunity and inflammation. This proteomic study may provide a biologic explanation why the BC is different than the dampness constitution.

A widespread family of heat-resistant obscure (Hero) proteins protect against protein instability and aggregation

Proteins are typically denatured and aggregated by heating at near-boiling temperature. Exceptions to this principle include highly disordered and heat-resistant proteins found in extremophiles, which help these organisms tolerate extreme conditions such as drying, freezing, and high salinity. In contrast, the functions of heat-soluble proteins in non-extremophilic organisms including humans remain largely unexplored. Here, we report that heat-resistant obscure (Hero) proteins, which remain soluble after boiling at 95°C, are widespread in Drosophila and humans. Hero proteins are hydrophilic and highly charged, and function to stabilize various "client" proteins, protecting them from denaturation even under stress conditions such as heat shock, desiccation, and exposure to organic solvents. Hero proteins can also block several different types of pathological protein aggregations in cells and in Drosophila strains that model neurodegenerative diseases. Moreover, Hero proteins can extend life span of Drosophila. Our study reveals that organisms naturally use Hero proteins as molecular shields to stabilize protein functions, highlighting their biotechnological and therapeutic potential.

micrIO: an open-source autosampler and fraction collector for automated microfluidic input-output

Microfluidic devices are an enabling technology for many labs, facilitating a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a 'world-to-chip' barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an 'AutoSipper' for input and a 'Fraction Collector' for output. To facilitate widespread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to two microfluidic devices: a simple, valved inlet manifold and a microfluidic droplet generator that produces beads with distinct spectral codes. Analysis of the collected materials in each case establishes the ability of the platform to draw from and output to specific wells of multiwell plates with negligible cross-contamination between samples.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Sequencer Hacking Unlocks Quantitative Protein Studies

In this issue of Molecular Cell, Layton et al. (2019) repurpose a common next-generation DNA sequencer to enable high-throughput protein biochemical studies, identifying improved sequence variants for stronger protein-protein interactions and dissecting the contributions of specific amino acids to enzymatic activity.