De novo identification and biophysical characterization of transcription-factor binding sites with microfluidic affinity analysis

Mechanisms for DNA Interplay in Eukaryotic Transcription Factors

Like their prokaryotic counterparts, eukaryotic transcription factors must recognize specific DNA sites, search for them efficiently, and bind to them to help recruit or block the transcription machinery. For eukaryotic factors, however, the genetic signals are extremely complex and scattered over vast, multichromosome genomes, while the DNA interplay occurs in a varying landscape defined by chromatin remodeling events and epigenetic modifications. Eukaryotic factors are rich in intrinsically disordered regions and are also distinct in their recognition of short DNA motifs and utilization of open DNA interaction interfaces as ways to gain access to DNA on nucleosomes. Recent findings are revealing the profound, unforeseen implications of such characteristics for the mechanisms of DNA interplay. In this review we discuss these implications and how they are shaping the eukaryotic transcription control paradigm into one of promiscuous signal recognition, highly dynamic interactions, heterogeneous DNA scanning, and multiprong conformational control.

Quantifying protein unfolding kinetics with a high-throughput microfluidic platform

Even after folding, proteins transiently sample unfolded or partially unfolded intermediates, and these species are often at risk of irreversible alteration (e.g. via proteolysis, aggregation, or post-translational modification). Kinetic stability, in addition to thermodynamic stability, can directly impact protein lifetime, abundance, and the formation of alternative, sometimes disruptive states. However, we have very few measurements of protein unfolding rates or how mutations alter these rates, largely due to technical challenges associated with their measurement. To address this need, we developed SPARKfold (Simultaneous Proteolysis Assay Revealing Kinetics of Folding), a microfluidic platform to express, purify, and measure unfolding rate constants for >1000 protein variants in parallel via on-chip native proteolysis. To demonstrate the power and potential of SPARKfold, we determined unfolding rate constants for 1,104 protein samples in parallel. We built a library of 31 dihydrofolate reductase (DHFR) orthologs with up to 78 chamber replicates per variant to provide the statistical power required to evaluate the system's ability to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with those obtained using traditional techniques across a 150-fold range, validating the accuracy of the technique. Comparisons of mutant kinetic effects via SPARKfold with previously published measurements impacts on folding thermodynamics provided information about the folding transition state and pathways via φ analysis. Overall, SPARKfold enables rapid characterization of protein variants to dissect the nature of the unfolding transition state. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable rational design of kinetically hyperstable variants for industrial use in harsh environments.

Mutations to transcription factor MAX allosterically increase DNA selectivity by altering folding and binding pathways

Understanding how proteins discriminate between preferred and non-preferred ligands ('selectivity') is essential for predicting biological function and a central goal of protein engineering efforts, yet the biophysical mechanisms underpinning selectivity remain poorly understood. Towards this end, we study how variants of the promiscuous transcription factor (TF) MAX (H. sapiens) alter DNA specificity and selectivity, yielding >1700 Kds and >500 rate constants in complex with multiple DNA sequences. Twenty-two of the 240 assayed MAX point mutations enhance selectivity, yet none of these mutations occur at residues that contact nucleotides in published structures. By applying thermodynamic and kinetic models to these results and previous observations for the highly similar yet far more selective TF Pho4 (S. cerevisiae), we find that these mutations enhance selectivity by altering partitioning between or affinity within conformations with different intrinsic selectivity, providing a mechanistic basis for allosteric modulation of ligand selectivity. These results highlight the importance of conformational heterogeneity in determining sequence selectivity and can guide future efforts to engineer selective proteins.

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 K ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

The method in the madness: Transcriptional control from stochastic action at the single-molecule scale

Cell states result from the ordered activation of gene expression by transcription factors. Transcription factors face opposing design constraints: they need to be dynamic to trigger rapid cell state transitions, but also stable enough to maintain terminal cell identities indefinitely. Recent progress in live-cell single-molecule microscopy has helped define the biophysical principles underlying this paradox. Beyond transcription factor activity, single-molecule experiments have revealed that at nearly every level of transcription regulation, control emerges from multiple short-lived stochastic interactions, rather than deterministic, stable interactions typical of other biochemical pathways. This architecture generates consistent outcomes that can be rapidly choreographed. Here, we highlight recent results that demonstrate how order in transcription regulation emerges from the apparent molecular-scale chaos and discuss remaining conceptual challenges.

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Yeast transcription factor Msn2 binds to G4 DNA

Sequences capable of forming quadruplex or G4 DNA are prevalent in the promoter regions. The transformation from canonical to non-canonical secondary structure apparently regulates transcription of a number of human genes. In the budding yeast Saccharomyces cerevisiae, we identified 37 genes with a G4 motif in the promoters including 20 genes that contain stress response element (STRE) overlapping a G4 motif. STRE is the binding site of stress response regulators Msn2 and Msn4, transcription factors belonging to the C2H2 zinc-finger protein family. We show here that Msn2 binds directly to the G4 DNA structure through its zinc-finger domain with a dissociation constant similar to that of STRE-binding and that, in a stress condition, Msn2 is enriched at G4 DNA-forming loci in the yeast genome. For a large fraction of genes with G4/STRE-containing promoters, treating with G4-ligands led to significant elevations in transcription levels. Such transcriptional elevation was greatly diminished in a msn2Δ msn4Δ background and was partly muted when the G4 motif was disrupted. Taken together, our data suggest that G4 DNA could be an alternative binding site of Msn2 in addition to STRE, and that G4 DNA formation could be an important element of transcriptional regulation in yeast.

KaScape: a sequencing-based method for global characterization of protein‒DNA binding affinity

It is difficult to exhaustively screen all possible DNA binding sequences for a given transcription factor (TF). Here, we developed the KaScape method, in which TFs bind to all possible DNA sequences in the same DNA pool where DNA sequences are prepared by randomized oligo synthesis and the random length can be adjusted to a length such as 4, 5, 6, or 7. After separating bound from unbound double-stranded DNAs (dsDNAs), their sequences are determined by next-generation sequencing. To demonstrate the relative binding affinities of all possible DNA sequences determined by KaScape, we developed three-dimensional KaScape viewing software based on a K-mer graph. We applied KaScape to 12 plant TF family AtWRKY proteins and found that all AtWRKY proteins bound to the core sequence GAC with similar profiles. KaScape can detect not only binding sequences consistent with the consensus W-box "TTGAC(C/T)" but also other sequences with weak affinity. KaScape provides a high-throughput, easy-to-operate, sensitive, and exhaustive method for quantitatively characterizing the relative binding strength of a TF with all possible binding sequences, allowing us to comprehensively characterize the specificity and affinity landscape of transcription factors, particularly for moderate- and low-affinity binding sites.

Short tandem repeats bind transcription factors to tune eukaryotic gene expression

Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites.

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

An ELISA-based platform for rapid identification of structure-dependent nucleic acid-protein interactions detects novel DNA triplex interactors

Unusual nucleic acid structures play vital roles as intermediates in many cellular processes and, in the case of peptide nucleic acid (PNA)-mediated triplexes, are leveraged as tools for therapeutic gene editing. However, due to their transient nature, an understanding of the factors that interact with and process dynamic nucleic acid structures remains limited. Here, we developed snapELISA (structure-specific nucleic acid-binding protein ELISA), a rapid high-throughput platform to interrogate and compare up to 2688 parallel nucleic acid structure-protein interactions in vitro. We applied this system to both triplex-forming oligonucleotide-induced DNA triplexes and DNA-bound PNA heterotriplexes to describe the identification of previously known and novel interactors for both structures. For PNA heterotriplex recognition analyses, snapELISA identified factors implicated in nucleotide excision repair (XPA, XPC), single-strand annealing repair (RAD52), and recombination intermediate structure binding (TOP3A, BLM, MUS81). We went on to validate selected factor localization to genome-targeted PNA structures within clinically relevant loci in human cells. Surprisingly, these results demonstrated XRCC5 localization to PNA triplex-forming sites in the genome, suggesting the presence of a double-strand break intermediate. These results describe a powerful comparative approach for identifying structure-specific nucleic acid interactions and expand our understanding of the mechanisms of triplex structure recognition and repair.

Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance

Binding to binding site clusters has yet to be characterized in depth, and the functional relevance of low-affinity clusters remains uncertain. We characterized transcription factor binding to low-affinity clusters in vitro and found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Furthermore, small clusters with binding sites an order of magnitude lower in affinity give rise to high mean occupancies at physiologically-relevant transcription factor concentrations. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters in a synthetic and native gene regulatory network in S. cerevisiae. In both systems, clusters of low-affinity binding sites generated transcriptional output comparable to single or even multiple consensus sites. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters.

Evolution of transcription factor binding through sequence variations and turnover of binding sites

Variations in noncoding regulatory sequences play a central role in evolution. Interpreting such variations, however, remains difficult even in the context of defined attributes such as transcription factor (TF) binding sites. Here, we systematically link variations in cis-regulatory sequences to TF binding by profiling the allele-specific binding of 27 TFs expressed in a yeast hybrid, in which two related genomes are present within the same nucleus. TFs localize preferentially to sites containing their known consensus motifs but occupy only a small fraction of the motif-containing sites available within the genomes. Differential binding of TFs to the orthologous alleles was well explained by variations that alter motif sequence, whereas differences in chromatin accessibility between alleles were of little apparent effect. Motif variations that abolished binding when present in only one allele were still bound when present in both alleles, suggesting evolutionary compensation, with a potential role for sequence conservation at the motif's vicinity. At the level of the full promoter, we identify cases of binding-site turnover, in which binding sites are reciprocally gained and lost, yet most interspecific differences remained uncompensated. Our results show the flexibility of TFs to bind imprecise motifs and the fast evolution of TF binding sites between related species.

Microfluidic tool for rapid functional characterization of CRISPR complexes

RNA guided nucleases are regarded as the future genome editing technologies. As such, they need to meet strong safety margins. Two major challenges in incorporating CRISPR technologies into the clinical world are off-target activity and editing efficiency. The common way to tackle such issues is to measure the binding and cleavage kinetics of the CRISPR enzyme. This can be challenging since, for example, DNA is not released from the CAS9 protein post cleavage. Here a promising new microfluidic approach to characterizing Enzymatic Interaction and Function of CRISPR complexes on a microfluidic platform (EnzyMIF) is presented. The method can rapidly detect the kd, koff, km and kcat for various RNA guided nucleases. In this work, two single guide RNAs with significantly different in-cell cleavage efficiency, RAG2 and RAG1, are used as proof-of-concept. The EnzyMIF assay results provide biochemical characterization of these guide RNAs that can explain the difference in cleavage using both wild type (WT) CAS9 and HiFi CAS9. Notably, it is shown that EnzyMIF characterization correlates with cell culture genomic editing efficiency results. It is suggested that EnzyMIF can predict the quality of cleavage rapidly and quantitatively.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Inferring primase-DNA specific recognition using a data driven approach

DNA–protein interactions play essential roles in all living cells. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is both challenging and important, since it may contribute to finding the means to regulate metabolic pathways involving DNA–protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA–protein interactions. Effective binding of T7 primase to its specific DNA recognition sequences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

uPIC-M: Efficient and Scalable Preparation of Clonal Single Mutant Libraries for High-Throughput Protein Biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate-limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal-Mutant (uPIC-M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC-M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC-M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

Precursor biosynthesis regulation of lignin, suberin and cutin

The extracellular matrix of plants can contain the hydrophobic biopolymers lignin, suberin and/or cutin, which provide mechanical strength and limit water loss and pathogen invasion. Due to their remarkable chemical resistance, these polymers have a high potential in various biotechnological applications and can replace petrol-based resources, for example, in the packing industry. However, despite the importance of these polymers, the regulation of their precursor biosynthesis is far from being fully understood. This is particularly true for suberin and cutin, which hinders efforts to engineer their formation in plants and produce customised biopolymers. This review brings attention to knowledge gaps in the current research and highlights some of the most recent findings on transcription factors that regulate lignin, suberin and cutin precursor biosynthesis. Finally, we also briefly discuss how some of the remaining knowledge gaps can be closed.

MRBLE-pep Measurements Reveal Accurate Binding Affinities for B56, a PP2A Regulatory Subunit

Signal transduction pathways rely on dynamic interactions between protein globular domains and short linear motifs (SLiMs). The weak affinities of these interactions are essential to allow fast rewiring of signaling pathways and downstream responses but also pose technical challenges for interaction detection and measurement. We recently developed a technique (MRBLE-pep) that leverages spectrally encoded hydrogel beads to measure binding affinities between a single protein of interest and 48 different peptide sequences in a single small volume. In prior work, we applied it to map the binding specificity landscape between calcineurin and the PxIxIT SLiM (Nguyen, H. Q. et al. Elife 2019, 8). Here, using peptide sequences known to bind the PP2A regulatory subunit B56α, we systematically compare affinities measured by MRBLE-pep or isothermal calorimetry (ITC) and confirm that MRBLE-pep accurately quantifies relative affinity over a wide dynamic range while using a fraction of the material required for traditional methods such as ITC.

Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

A high-throughput microfluidic nanoimmunoassay for detecting anti-SARS-CoV-2 antibodies in serum or ultralow-volume blood samples

Novel technologies are needed to facilitate large-scale detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) specific antibodies in human blood samples. Such technologies are essential to support seroprevalence studies and vaccine clinical trials, and to monitor quality and duration of immunity. We developed a microfluidic nanoimmunoassay (NIA) for the detection of anti-SARS-CoV-2 IgG antibodies in 1,024 samples per device. The method achieved a specificity of 100% and a sensitivity of 98% based on the analysis of 289 human serum samples. To eliminate the need for venipuncture, we developed low-cost, ultralow-volume whole blood sampling methods based on two commercial devices and repurposed a blood glucose test strip. The glucose test strip permits the collection, shipment, and analysis of 0.6 μL of whole blood easily obtainable from a simple finger prick. The NIA platform achieves high throughput, high sensitivity, and specificity based on the analysis of 289 human serum samples, and negligible reagent consumption. We furthermore demonstrate the possibility to combine NIA with decentralized and simple approaches to blood sample collection. We expect this technology to be applicable to current and future SARS-CoV-2 related serological studies and to protein biomarker analysis in general.

High-Throughput Affinity Measurements of Transcription Factor and DNA Mutations Reveal Affinity and Specificity Determinants

Transcription factors (TFs) bind regulatory DNA to control gene expression, and mutations to either TFs or DNA can alter binding affinities to rewire regulatory networks and drive phenotypic variation. While studies have profiled energetic effects of DNA mutations extensively, we lack similar information for TF variants. Here, we present STAMMP (simultaneous transcription factor affinity measurements via microfluidic protein arrays), a high-throughput microfluidic platform enabling quantitative characterization of hundreds of TF variants simultaneously. Measured affinities for ∼210 mutants of a model yeast TF (Pho4) interacting with 9 oligonucleotides (>1,800 K_ds) reveal that many combinations of mutations to poorly conserved TF residues and nucleotides flanking the core binding site alter but preserve physiological binding, providing a mechanism by which combinations of mutations in cis and trans could modulate TF binding to tune occupancies during evolution. Moreover, biochemical double-mutant cycles across the TF-DNA interface reveal molecular mechanisms driving recognition, linking sequence to function. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Quantitative analysis of transcription factor binding and expression using calling cards reporter arrays

We report a tool, Calling Cards Reporter Arrays (CCRA), that measures transcription factor (TF) binding and the consequences on gene expression for hundreds of synthetic promoters in yeast. Using Cbf1p and MAX, we demonstrate that the CCRA method is able to detect small changes in binding free energy with a sensitivity comparable to in vitro methods, enabling the measurement of energy landscapes in vivo. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. We find that the cooperativity between Cbf1p dimers varies sinusoidally with a period of 10.65 bp and energetic cost of 1.37 K_BT for sites that are positioned ‘out of phase’. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p and Gcr2p, that act together as a ‘TF collective’, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function.

Design of orthogonal regulatory systems for modulating gene expression in plants

Agricultural biotechnology strategies often require the precise regulation of multiple genes to effectively modify complex plant traits. However, most efforts are hindered by a lack of characterized tools that allow for reliable and targeted expression of transgenes. We have successfully engineered a library of synthetic transcriptional regulators that modulate expression strength in planta. By leveraging orthogonal regulatory systems from Saccharomyces spp., we have developed a strategy for the design of synthetic activators, synthetic repressors, and synthetic promoters and have validated their use in Nicotiana benthamiana and Arabidopsis thaliana. This characterization of contributing genetic elements that dictate gene expression represents a foundation for the rational design of refined synthetic regulators. Our findings demonstrate that these tools provide variation in transcriptional output while enabling the concerted expression of multiple genes in a tissue-specific and environmentally responsive manner, providing a basis for generating complex genetic circuits that process endogenous and environmental stimuli.

The New Age of Cell-Free Biology

The cell-free molecular synthesis of biochemical systems is a rapidly growing field of research. Advances in the Human Genome Project, DNA synthesis, and other technologies have allowed the in vitro construction of biochemical systems, termed cell-free biology, to emerge as an exciting domain of bioengineering. Cell-free biology ranges from the molecular to the cell-population scales, using an ever-expanding variety of experimental platforms and toolboxes. In this review, we discuss the ongoing efforts undertaken in the three major classes of cell-free biology methodologies, namely protein-based, nucleic acids–based, and cell-free transcription–translation systems, and provide our perspectives on the current challenges as well as the major goals in each of the subfields.

Adaptive Laboratory Evolution and Reverse Engineering of Single-Vitamin Prototrophies in Saccharomyces cerevisiae

Many strains of Saccharomyces cerevisiae, a popular platform organism in industrial biotechnology, carry the genetic information required for synthesis of biotin, thiamine, pyridoxine, para-aminobenzoic acid, pantothenic acid, nicotinic acid, and inositol. However, omission of these B vitamins typically leads to suboptimal growth. This study demonstrates that, for each individual B vitamin, it is possible to achieve fast vitamin-independent growth by adaptive laboratory evolution (ALE). Identification of mutations responsible for these fast-growing phenotypes by whole-genome sequencing and reverse engineering showed that, for each compound, a small number of mutations sufficed to achieve fast growth in its absence. These results form an important first step toward development of S. cerevisiae strains that exhibit fast growth on inexpensive, fully supplemented mineral media that only require complementation with a carbon source, thereby reducing costs, complexity, and contamination risks in industrial yeast fermentation processes.

Quantitative physiological studies on Saccharomyces cerevisiae commonly use synthetic media (SM) that contain a set of water-soluble growth factors that, based on their roles in human nutrition, are referred to as B vitamins. Previous work demonstrated that in S. cerevisiae CEN.PK113-7D, requirements for biotin were eliminated by laboratory evolution. In the present study, this laboratory strain was shown to exhibit suboptimal specific growth rates when either inositol, nicotinic acid, pyridoxine, pantothenic acid, para-aminobenzoic acid (pABA), or thiamine was omitted from SM. Subsequently, this strain was evolved in parallel serial-transfer experiments for fast aerobic growth on glucose in the absence of individual B vitamins. In all evolution lines, specific growth rates reached at least 90% of the growth rate observed in SM supplemented with a complete B vitamin mixture. Fast growth was already observed after a few transfers on SM without myo-inositol, nicotinic acid, or pABA. Reaching similar results in SM lacking thiamine, pyridoxine, or pantothenate required more than 300 generations of selective growth. The genomes of evolved single-colony isolates were resequenced, and for each B vitamin, a subset of non-synonymous mutations associated with fast vitamin-independent growth was selected. These mutations were introduced in a non-evolved reference strain using CRISPR/Cas9-based genome editing. For each B vitamin, the introduction of a small number of mutations sufficed to achieve a substantially increased specific growth rate in non-supplemented SM that represented at least 87% of the specific growth rate observed in fully supplemented complete SM.

IMPORTANCE Many strains of Saccharomyces cerevisiae, a popular platform organism in industrial biotechnology, carry the genetic information required for synthesis of biotin, thiamine, pyridoxine, para-aminobenzoic acid, pantothenic acid, nicotinic acid, and inositol. However, omission of these B vitamins typically leads to suboptimal growth. This study demonstrates that, for each individual B vitamin, it is possible to achieve fast vitamin-independent growth by adaptive laboratory evolution (ALE). Identification of mutations responsible for these fast-growing phenotypes by whole-genome sequencing and reverse engineering showed that, for each compound, a small number of mutations sufficed to achieve fast growth in its absence. These results form an important first step toward development of S. cerevisiae strains that exhibit fast growth on inexpensive, fully supplemented mineral media that only require complementation with a carbon source, thereby reducing costs, complexity, and contamination risks in industrial yeast fermentation processes.

Biophysical Techniques for Target Validation and Drug Discovery in Transcription-Targeted Therapy

In the post-genome era, pathologies become associated with specific gene expression profiles and defined molecular lesions can be identified. The traditional therapeutic strategy is to block the identified aberrant biochemical activity. However, an attractive alternative could aim at antagonizing key transcriptional events underlying the pathogenesis, thereby blocking the consequences of a disorder, irrespective of the original biochemical nature. This approach, called transcription therapy, is now rendered possible by major advances in biophysical technologies. In the last two decades, techniques have evolved to become key components of drug discovery platforms, within pharmaceutical companies as well as academic laboratories. This review outlines the current biophysical strategies for transcription manipulation and provides examples of successful applications. It also provides insights into the future development of biophysical methods in drug discovery and personalized medicine.

The relation between crosstalk and gene regulation form revisited

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. The basic level of regulation is mediated by different types of DNA-binding proteins, where each type regulates particular gene(s). We distinguish between two basic forms of regulation: positive—if a gene is activated by the binding of its regulatory protein, and negative—if it is active unless bound by its regulatory protein. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. How does the form of regulation, positive or negative, affect the extent of regulatory crosstalk? To address this question, we used a mathematical model integrating many genes and many regulators. As intuition suggests, we found that in most of the parameter space, crosstalk increased with the availability of regulators. We propose, that crosstalk is usually reduced when networks are designed such that minimal regulation is needed, which we call the ‘idle’ design. In other words: a frequently needed gene will use negative regulation and conversely, a scarcely needed gene will employ positive regulation. In both cases, the requirement for the regulators is minimized. In addition, we demonstrate how crosstalk can be calculated from available datasets and discuss the technical challenges in such calculation, specifically data incompleteness.

Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers

Peptide arrays measure the binding intensity of a specific protein to thousands of amino acid peptides. By using peptides that cover all k-mers, a comprehensive picture of the binding spectrum is obtained. Researchers would like to measure binding to the longest k-mer possible but are constrained by the number of peptides that can fit into a single microarray. A key challenge is designing a minimum number of peptides that cover all k-mers. Here, we suggest a novel idea to reduce the length of the sequence covering all k-mers by utilizing a unique property of the peptide synthesis process. Since the synthesis can start from both ends of the peptide template, it is enough to cover each k-mer or its reverse and to use the same template twice: in forward and reverse. Then, the computational problem is to generate a minimum length sequence that for each k-mer either contains the k-mer or its reverse. In this study, we present a new algorithm, called ReverseCAKE, to generate such a sequence. ReverseCAKE runs in time linear in the output size and is guaranteed to produce a sequence that is longer by at most Θ(nlogn) characters compared with the optimum n. The obtained saving factor by ReverseCAKE approaches the theoretical lower bound as k increases. In addition, we formulated the problem as an integer linear program and empirically observed that the solutions obtained by ReverseCAKE are near-optimal. Through this work, we enable more effective design of peptide microarrays.

The Role of Cross-Pathway Control Regulator CpcA in the Growth and Extracellular Enzyme Production of Penicillium oxalicum

CpcA is a conserved transcriptional activator for the cross-pathway control of amino acid biosynthetic genes in filamentous fungi. Previous studies of this regulator mainly revealed its function under amino acid starvation condition, where amino acid biosynthetic inhibitors were added in the culture. In this study, the biological function of CpcA in Penicillium oxalicum was investigated under different cultivation conditions. Disruption of cpcA led to decreased cell growth either in the presence or absence of histidine biosynthetic inhibitor, and the phenotype could be rescued by the addition of exogenous amino acid sources. In addition, CpcA was required for the rapid production of cellulase when cells were cultured on cellulose. Transcript abundance measurement showed that a set of amino acid biosynthetic genes as well as two major cellulase genes were significantly down-regulated in cpcA deletion mutant relative to wild type. Taken together, the results revealed the biological role of CpcA in supporting normal growth and extracellular enzyme production of P. oxalicum under amino acid non-starvation condition.

Microfluidic epigenomic mapping technologies for precision medicine

Epigenomic mapping of tissue samples generates critical insights into genome-wide regulations of gene activities and expressions during normal development and disease processes. Epigenomic profiling using a low number of cells produced by patient and mouse samples presents new challenges to biotechnologists. In this review, we first discuss the rationale and premise behind profiling epigenomes for precision medicine. We then examine the existing literature on applying microfluidics to facilitate low-input and high-throughput epigenomic profiling, with emphasis on technologies enabling interfacing with next-generation sequencing. We detail assays on studies of histone modifications, DNA methylation, 3D chromatin structures and non-coding RNAs. Finally, we discuss what the future may hold in terms of method development and translational potential.

Exclusive Temporal Stimulation of IL-10 Expression in LPS-Stimulated Mouse Macrophages by cAMP Inducers and Type I Interferons

Expression of the key anti-inflammatory cytokine IL-10 in lipopolysaccharide (LPS)-stimulated macrophages is mediated by a delayed autocrine/paracrine loop of type I interferons (IFN) to ensure timely attenuation of inflammation. We have previously shown that cAMP synergizes with early IL-10 expression by LPS, but is unable to amplify the late type I IFN-dependent activity. We now examined the mechanism of this synergistic transcription in mouse macrophages at the promoter level, and explored the crosstalk between type I IFN signaling and cAMP, using the β-adrenergic receptor agonist, isoproterenol, as a cAMP inducer. We show that silencing of the type I IFN receptor enables isoproterenol to synergize with LPS also at the late phase, implying that autocrine type I IFN activity hinders synergistic augmentation of LPS-stimulated IL-10 expression by cAMP at the late phase. Furthermore, IL-10 expression in LPS-stimulated macrophages is exclusively stimulated by either IFNα or isoproterenol. We identified a set of two proximate and inter-dependent cAMP response element (CRE) sites that cooperatively regulate early IL-10 transcription in response to isoproterenol-stimulated CREB and that further synergize with a constitutive Sp1 site. At the late phase, up-regulation of Sp1 activity by LPS-stimulated type I IFN is correlated with loss of function of the CRE sites, suggesting a mechanism for the loss of synergism when LPS-stimulated macrophages switch to type I IFN-dependent IL-10 expression. This report delineates the molecular mechanism of cAMP-accelerated IL-10 transcription in LPS-stimulated murine macrophages that can limit inflammation at its onset.

Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping

Sequence libraries that cover all k-mers enable universal, unbiased measurements of binding to both oligonucleotides and peptides. While the number of k-mers grows exponentially in k, space on all experimental platforms is limited. Here, we shrink k-mer library sizes by using joker characters, which represent all characters in the alphabet simultaneously. We present the JokerCAKE (joker covering all k-mers) algorithm for generating a short sequence such that each k-mer appears at least p times with at most one joker character per k-mer. By running our algorithm on a range of parameters and alphabets, we show that JokerCAKE produces near-optimal sequences. Moreover, through comparison with data from hundreds of DNA-protein binding experiments and with new experimental results for both standard and JokerCAKE libraries, we establish that accurate binding scores can be inferred for high-affinity k-mers using JokerCAKE libraries. JokerCAKE libraries allow researchers to search a significantly larger sequence space using the same number of experimental measurements and at the same cost.

Microfluidic Devices Containing ZnO Nanorods with Tunable Surface Chemistry and Wetting-Independent Water Mobility

Interest in polydimethylsiloxane (PDMS) microfluidic devices has grown dramatically in recent years, particularly in the context of improved performance lab-on-a-chip devices with decreasing channel size enabling more devices on ever smaller chips. As channels become smaller, the resistance to flow increases and the device structure must be able to withstand higher internal pressures. We report herein the fabrication of microstructured surfaces that promote water mobility independent of surface static wetting properties. The key tool in this approach is the growth of ZnO nanorods on the bottom face of the microfluidic device. We show that water flow in these devices is similar whether the textured nanorod-bearing surface is hydrophilic or superhydrophobic; that is, the device tolerates a wide range of surface wetting properties without changing the water flow within the device. This is not the case for smooth surfaces with different wetting properties, wherein hydrophilic surfaces result in slower flow rates. The ability to create monolayer-coated ZnO nanorods in a PDMS microfluidic device also allows for a variety of surface modifications within standard mass-produced devices. The inorganic ZnO nanorods can be coated with alkyl phosphonate monolayers. These monolayers can be used to convert hydrophilic surfaces into hydrophobic and even superhydrophobic surfaces that provide a platform for further surface modification. We also report photopatterned biomolecule immobilization within the channels on the monolayer-coated ZnO rods.

A high-throughput integrated microfluidics method enables tyrosine autophosphorylation discovery

Autophosphorylation of receptor and non-receptor tyrosine kinases is a common molecular switch with broad implications for pathogeneses and therapy of cancer and other human diseases. Technologies for large-scale discovery and analysis of autophosphorylation are limited by the inherent difficulty to distinguish between phosphorylation and autophosphorylation in vivo and by the complexity associated with functional assays of receptors kinases in vitro. Here, we report a method for the direct detection and analysis of tyrosine autophosphorylation using integrated microfluidics and freshly synthesized protein arrays. We demonstrate the efficacy of our platform in detecting autophosphorylation activity of soluble and transmembrane tyrosine kinases, and the dependency of in vitro autophosphorylation assays on membranes. Our method, Integrated Microfluidics for Autophosphorylation Discovery (IMAD), is high-throughput, requires low reaction volumes and can be applied in basic and translational research settings. To our knowledge, it is the first demonstration of posttranslational modification analysis of membrane protein arrays.

Multilayered skyscraper microchips fabricated by hybrid "all-in-one" femtosecond laser processing

Multilayered microfluidic channels integrated with functional microcomponents are the general trend of future biochips, which is similar to the history of Si-integrated circuits from the planer to the three-dimensional (3D) configuration, since they offer miniaturization while increasing the integration degree and diversifying the applications in the reaction, catalysis, and cell cultures. In this paper, an optimized hybrid processing technology is proposed to create true multilayered microchips, by which "all-in-one" 3D microchips can be fabricated with a successive procedure of 3D glass micromachining by femtosecond-laser-assisted wet etching (FLAE) and the integration of microcomponents into the fabricated microchannels by two-photon polymerization (TPP). To create the multilayered microchannels at different depths in glass substrates (the top layer was embedded at 200 μm below the surface, and the underlying layers were constructed with a 200-μm spacing) with high uniformity and quality, the laser power density (13~16.9 TW/cm²) was optimized to fabricate different layers. To simultaneously complete the etching of each layer, which is also important to ensure the high uniformity, the control layers (nonlaser exposed regions) were prepared at the upper ends of the longitudinal channels. Solvents with different dyes were used to verify that each layer was isolated from the others. The high-quality integration was ensured by quantitatively investigating the experimental conditions in TPP, including the prebaking time (18~40 h), laser power density (2.52~2.94 TW/cm²) and developing time (0.8~4 h), all of which were optimized for each channel formed at different depths. Finally, the eight-layered microfluidic channels integrated with polymer microstructures were successfully fabricated to demonstrate the unique capability of this hybrid technique.

Diversification of DNA binding specificities enabled SREBP transcription regulators to expand the repertoire of cellular functions that they govern in fungi

The Sterol Regulatory Element Binding Proteins (SREBPs) are basic-helix-loop-helix transcription regulators that control the expression of sterol biosynthesis genes in higher eukaryotes and some fungi. Surprisingly, SREBPs do not regulate sterol biosynthesis in the ascomycete yeasts (Saccharomycotina) as this role was handed off to an unrelated transcription regulator in this clade. The SREBPs, nonetheless, expanded in fungi such as the ascomycete yeasts Candida spp., raising questions about their role and evolution in these organisms. Here we report that the fungal SREBPs diversified their DNA binding preferences concomitantly with an expansion in function. We establish that several branches of fungal SREBPs preferentially bind non-palindromic DNA sequences, in contrast to the palindromic DNA motifs recognized by most basic-helix-loop-helix proteins (including SREBPs) in higher eukaryotes. Reconstruction and biochemical characterization of the likely ancestor protein suggest that an intrinsic DNA binding promiscuity in the family was resolved by alternative mechanisms in different branches of fungal SREBPs. Furthermore, we show that two SREBPs in the human commensal yeast Candida albicans drive a transcriptional cascade that inhibits a morphological switch under anaerobic conditions. Preventing this morphological transition enhances C. albicans colonization of the mammalian intestine, the fungus' natural niche. Thus, our results illustrate how diversification in DNA binding preferences enabled the functional expansion of a family of eukaryotic transcription regulators.

Specificity landscapes unmask submaximal binding site preferences of transcription factors

We have developed Differential Specificity and Energy Landscape (DiSEL) analysis to comprehensively compare DNA-protein interactomes (DPIs) obtained by high-throughput experimental platforms and cutting edge computational methods. While high-affinity DNA binding sites are identified by most methods, DiSEL uncovered nuanced sequence preferences displayed by homologous transcription factors. Pairwise analysis of 726 DPIs uncovered homolog-specific differences at moderate- to low-affinity binding sites (submaximal sites). DiSEL analysis of variants of 41 transcription factors revealed that many disease-causing mutations result in allele-specific changes in binding site preferences. We focused on a set of highly homologous factors that have different biological roles but "read" DNA using identical amino acid side chains. Rather than direct readout, our results indicate that DNA noncontacting side chains allosterically contribute to sculpt distinct sequence preferences among closely related members of transcription factor families.

Go with the Flow-Microfluidics Approaches for Amyloid Research

The rapid development of cost-efficient microfluidic devices has received tremendous attention from scientists of diverse fields. The growing potential of utilizing microfluidic platforms has further advanced the ability to integrate existing technology into microfluidic devices. Thus, allowing scientists to approach questions in fundamental fields, such as amyloid research, using new and otherwise unachievable conditions. Amyloids are associated with neurodegeneration and are in the forefront of many research efforts worldwide. The newly emerged microfluidic technology can serve as a novel research tool providing a platform for developing new methods in this field. In this review, we summarize the recent progress in amyloid research using microfluidic approaches. These approaches are driven from various fields, including physical chemistry, electrochemistry, biochemistry, and cell biology. Moreover, the new insights into novel microfluidic approaches for amyloid research reviewed here can be easily modified for other research interests.

Joker de Bruijn: Covering k-Mers Using Joker Characters

Sequence libraries that cover all k-mers enable universal and unbiased measurements of nucleotide and peptide binding. The shortest sequence to cover all k-mers is a de Bruijn sequence of length \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\vert \Sigma { \vert ^k} + k - 1$$ \end{document}. Researchers would like to increase k to measure interactions at greater detail, but face a challenging problem: the number of k-mers grows exponentially in k, while the space on the experimental device is limited. In this study, we introduce a novel advance to shrink k-mer library sizes by using joker characters, which represent all characters in the alphabet. Theoretically, the use of joker characters can reduce the library size tremendously, but it should be limited as the introduced degeneracy lowers the statistical robustness of measurements. In this work, we consider the problem of generating a minimum-length sequence that covers a given set of k-mers using joker characters. The number and positions of the joker characters are provided as input. We first prove that the problem is NP-hard. We then present the first solution to the problem, which is based on two algorithmic innovations: (1) a greedy heuristic and (2) an integer linear programming (ILP) formulation. We first run the heuristic to find a good feasible solution, and then run an ILP solver to improve it. We ran our algorithm on DNA and amino acid alphabets to cover all k-mers for different values of k and k-mer multiplicity. Results demonstrate that it produces sequences that are very close to the theoretical lower bound.

High-throughput identification of noncoding functional SNPs via type IIS enzyme restriction

Genome-wide association studies (GWAS) have identified many disease-associated noncoding variants, but cannot distinguish functional single-nucleotide polymorphisms (fSNPs) from others that reside incidentally within risk loci. To address this challenge, we developed an unbiased high-throughput screen that employs type IIS enzymatic restriction to identify fSNPs that allelically modulate the binding of regulatory proteins. We coupled this approach, termed SNP-seq, with flanking restriction enhanced pulldown (FREP) to identify regulation of CD40 by three disease-associated fSNPs via four regulatory proteins, RBPJ, RSRC2 and FUBP-1/TRAP150. Applying this approach across 27 loci associated with juvenile idiopathic arthritis, we identified 148 candidate fSNPs, including two that regulate STAT4 via the regulatory proteins SATB2 and H1.2. Together, these findings establish the utility of tandem SNP-seq/FREP to bridge the gap between GWAS and disease mechanism.

Accurate and sensitive quantification of protein-DNA binding affinity

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

An Open-Source, Programmable Pneumatic Setup for Operation and Automated Control of Single- and Multi-Layer Microfluidic Devices

Microfluidic technologies have been used across diverse disciplines (e.g. high-throughput biological measurement, fluid physics, laboratory fluid manipulation) but widespread adoption has been limited in part due to the lack of openly disseminated resources that enable non-specialist labs to make and operate their own devices. Here, we report the open-source build of a pneumatic setup capable of operating both single and multilayer (Quake-style) microfluidic devices with programmable scripting automation. This setup can operate both simple and complex devices with 48 device valve control inputs and 18 sample inputs, with modular design for easy expansion, at a fraction of the cost of similar commercial solutions. We present a detailed step-by-step guide to building the pneumatic instrumentation, as well as instructions for custom device operation using our software, Geppetto, through an easy-to-use GUI for live on-chip valve actuation and a scripting system for experiment automation. We show robust valve actuation with near real-time software feedback and demonstrate use of the setup for high-throughput biochemical measurements on-chip. This open-source setup will enable specialists and novices alike to run microfluidic devices easily in their own laboratories.

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing

Biophysical models of transcriptional regulation rely on energetic measurements of the binding affinities between transcription factors (TFs) and target DNA binding sites. Historically, assays capable of measuring TF-DNA binding affinities have been relatively low-throughput (measuring ~10³ sequences in parallel) and have required significant specialized equipment, limiting their use to a handful of laboratories. Recently, we developed an experimental assay and analysis pipeline that allows measurement of binding energies between a single TF and up to 10⁶ DNA species in a single experiment (Binding Energy Topography by sequencing, or BET-seq) (Le et al., 2018). BET-seq employs the Mechanically Induced Trapping of Molecular Interactions (MITOMI) platform to purify DNA bound to a TF at equilibrium followed by high coverage sequencing to reveal relative differences in binding energy for each sequence. While we have previously used BET-seq to refine the binding affinity landscapes surrounding high-affinity DNA consensus target sites, we anticipate this technique will be applied in future work toward measuring a wide variety of TF-DNA landscapes. Here, we provide detailed instructions and general considerations for DNA library design, performing BET-seq assays, and analyzing the resulting data.

A Microfluidic Biodisplay

Synthetically engineered cells are powerful and potentially useful biosensors, but it remains problematic to deploy such systems due to practical difficulties and biosafety concerns. To overcome these hurdles, we developed a microfluidic device that serves as an interface between an engineered cellular system, environment, and user. We created a biodisplay consisting of 768 individually programmable biopixels and demonstrated that it can perform multiplexed, continuous sampling. The biodisplay detected 10 μg/L sodium-arsenite in tap water using a research grade fluorescent microscope, and reported arsenic contamination down to 20 μg/L with an easy to interpret "skull and crossbones" symbol detectable with a low-cost USB microscope or by eye. The biodisplay was designed to prevent release of chemical or biological material to avoid environmental contamination. The microfluidic biodisplay thus provides a practical solution for the deployment and application of engineered cellular systems.