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Archaea are widespread in the environment and play fundamental roles in diverse ecosystems; however, characterization of their unique biology requires advanced tools. This is particularly challenging when characterizing gene function. Here, we generate randomly barcoded transposon libraries in the model methanogenic archaeon Methanococcus maripaludis and use high-throughput growth methods to conduct fitness assays (RB-TnSeq) across over 100 unique growth conditions. Using our approach, we identified new genes involved in nutrient utilization and response to oxidative stress. We identified novel genes for the usage of diverse nitrogen sources in M. maripaludis including a putative regulator of alanine deamination and molybdate transporters important for nitrogen fixation. Furthermore, leveraging the fitness data, we inferred that M. maripaludis can utilize additional nitrogen sources including ʟ-glutamine, ᴅ-glucuronamide, and adenosine. Under autotrophic growth conditions, we identified a gene encoding a domain of unknown function (DUF166) that is important for fitness and hypothesize that it has an accessory role in carbon dioxide assimilation. Finally, comparing fitness costs of oxygen versus sulfite stress, we identified a previously uncharacterized class of dissimilatory sulfite reductase-like proteins (Dsr-LP; group IIId) that is important during growth in the presence of sulfite. When overexpressed, Dsr-LP conferred sulfite resistance and enabled use of sulfite as the sole sulfur source. The high-throughput approach employed here allowed for generation of a large-scale data set that can be used as a resource to further understand gene function and metabolism in the archaeal domain.IMPORTANCEArchaea are widespread in the environment, yet basic aspects of their biology remain underexplored. To address this, we apply randomly barcoded transposon libraries (RB-TnSeq) to the model archaeon Methanococcus maripaludis. RB-TnSeq coupled with high-throughput growth assays across over 100 unique conditions identified roles for previously uncharacterized genes, including several encoding proteins with domains of unknown function (DUFs). We also expand on our understanding of carbon and nitrogen metabolism and characterize a group IIId dissimilatory sulfite reductase-like protein as a functional sulfite reductase. This data set encompasses a wide range of additional conditions including stress, nitrogen fixation, amino acid supplementation, and autotrophy, thus providing an extensive data set for the archaeal community to mine for characterizing additional genes of unknown function.

Infertility affects ∼12 % of couples, with environmental chemical exposure as a potential contributor. Of the chemicals that are actively manufactured, very few are assessed for reproductive health effects. Rodents are commonly used to evaluate reproductive effects, which is both costly and time consuming. Thus, there is a pressing need for rapid methods to test a broader range of chemicals. Here, we developed a strategy to evaluate large numbers of chemicals for reproductive toxicity via a yeast, S. cerevisiae high-throughput assay to assess gametogenesis as a potential new approach method (NAM). By simultaneously assessing chemicals for growth effects, we can distinguish if a chemical affects gametogenesis only, proliferative growth only or both. We identified a well-known mammalian reproductive toxicant, bisphenol A (BPA) and ranked 19 BPA analogs for reproductive harm. By testing mixtures of BPA and its analogs, we found that BPE and 17 β-estradiol each together with BPA showed synergistic effects that worsened reproductive outcome. We examined an additional 179 environmental chemicals including phthalates, pesticides, quaternary ammonium compounds and per- and polyfluoroalkyl substances and found 57 with reproductive effects. Many of the chemicals were found to be strong reproductive toxicants that have yet to be tested in mammals. Chemicals having affect before meiosis I division vs. meiosis II division were identified for 16 gametogenesis-specific chemicals. Finally, we demonstrate that in general yeast reproductive toxicity correlates well with published reproductive toxicity in mammals illustrating the promise of this NAM to quickly assess chemicals to prioritize the evaluation for human reproductive harm.

Reproduction is a functional outcome that relies on complex cellular, tissue, and organ interactions that span the developmental period to adulthood. Thus, the assessment of its disruption by environmental chemicals would benefit significantly from scalable and innovative approaches to testing using functionally comparable reproductive models such as the nematode C. elegans. We adapted a previously described low-throughput in vivo chromosome segregation assay using C. elegans predictive of reproductive toxicity and leveraged available public data sources (ToxCast, ICE) to screen and characterize 133 physiologically-relevant chemicals in a high-throughput manner. The screening outcome was further validated in a second, independent in vivo assay assessing embryonic viability. In total, 13 chemicals were classified as reproductive toxicants with the two most active chemicals belonging to the large family of Quaternary Ammonium Compounds (QACs) commonly used as disinfectants but with limited available reproductive toxicity data. We compared the results from the C. elegans assay with ToxCast in vitro data compiled from 700+ cell response assays and 300+ signaling pathways-based assays. We did not observe a difference in the bioactivity or in the average potency (AC50) between the top and bottom chemicals. However, the intended target categories were significantly different between the classified chemicals with, in particular, an over-representation of steroid hormone targets for the high Z-score chemicals. Taken together, these results point to the value of in vivo models that scale to high-throughput level for reproductive toxicity assessment and to the need to prioritize the assessment of QACs impacts on reproduction.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Bacterial commensals of the human genitourinary tract, Mycoplasma hominis and Ureaplasma species (parvum and urealyticum) can be sexually transmitted, and may cause nongonococcal urethritis, pelvic inflammatory disease, and infertility. Mycoplasma hominis and Ureaplasma species may also cause severe invasive infections in immunocompromised patients. Current culture-based methods for Mycoplasma/Ureaplasma identification are costly and laborious, with a turnaround time between 1 and 2 weeks. We developed a high-throughput, real-time multiplex PCR assay for the rapid detection of M. hominis and Ureaplasma species in urine, genital swab, body fluid, and tissue. In total, 282 specimens were tested by PCR and compared with historic culture results; a molecular reference method was used to moderate discrepancies. Overall result agreement was 99% for M. hominis (97% positive percentage agreement and 100% negative percentage agreement) and 96% for Ureaplasma species (96% positive percentage agreement and 97% negative percentage agreement). Specimen stability was validated for up to 7 days at room temperature. This multiplex molecular assay was designed for implementation in a high-complexity clinical microbiology laboratory. With this method, >90 samples can be tested in one run, with a turnaround time of 4 to 5 hours from specimen extraction to reporting of results. This PCR test is also more labor effective and cheaper than the conventional culture-based test, thus improving laboratory efficiency and alleviating labor shortages.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Cell-cell interactions in the central nervous system play important roles in neurologic diseases. However, little is known about the specific molecular pathways involved, and methods for their systematic identification are limited. Here, we developed a forward genetic screening platform that combines CRISPR-Cas9 perturbations, cell coculture in picoliter droplets, and microfluidic-based fluorescence-activated droplet sorting to identify mechanisms of cell-cell communication. We used SPEAC-seq (systematic perturbation of encapsulated associated cells followed by sequencing), in combination with in vivo genetic perturbations, to identify microglia-produced amphiregulin as a suppressor of disease-promoting astrocyte responses in multiple sclerosis preclinical models and clinical samples. Thus, SPEAC-seq enables the high-throughput systematic identification of cell-cell communication mechanisms.

The kinase ataxia telangiectasia mutated (ATM) plays a key role in the DNA damage response (DDR). It is thus essential to visualize spatiotemporal dynamics of ATM activity during DDR. Here, we designed a robust ATM activity reporter based on phosphorylation-inducible green fluorescent protein phase separation, dubbed ATM-SPARK (separation of phases-based activity reporter of kinase). Upon ATM activation, it undergoes phase separation via multivalent interactions, forming intensely bright droplets. The reporter visualizes spatiotemporal dynamics of endogenous ATM activity in living cells, and its signal is proportional to the amount of DNA damage. ATM-SPARK also enables high-throughput screening of biological and small-molecule regulators. We identified the protein phosphatase 4 that blocks ATM activity. We also identified BGT226 as a potent ATM inhibitor with a median inhibitory concentration of ~3.8 nanomolars. Furthermore, BGT226 sensitizes cancer cells to the radiomimetic drug neocarzinostatin, suggesting that BGT226 might be combined with radiotherapeutic treatment. ATM-SPARK achieves large dynamic range, bright fluorescence, and simple signal pattern.

With rapid advances in the development of metabolic pathways and synthetic biology toolkits, a persisting challenge in microbial bioproduction is how to optimally rewire metabolic fluxes and accelerate the concomitant high-throughput phenotype screening. Here we developed a biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening approach that combines a titratable mismatch CRISPR interference and a biosensor mediated screening for high-production phenotypes in Escherichia coli. We first developed a programmable mismatch CRISPRi that could afford multiple levels of interference efficacy with a one-pot sgRNA pool (a total of 16 variants for each target gene) harboring two consecutive random mismatches in the seed region of sgRNA spacers. The mismatch CRISPRi was demonstrated to enable almost a full range of gene knockdown when targeting different positions on genes. As a proof-of-principle demonstration of the BATCH screening system, we designed doubly mismatched sgRNA pools targeting 20 relevant genes in E. coli and optimized a PadR-based p-coumaric acid biosensor with broad dynamic range for the eGFP fluorescence guided high-production screening. Using sgRNA variants for the combinatorial knockdown of pfkA and ptsI, the p-coumaric acid titer was increased by 40.6% to o 1308.6 mg/l from glycerol in shake flasks. To further demonstrate the general applicability of the BATCH screening system, we recruited a HpdR-based butyrate biosensor that facilitated the screening of E. coli strains achieving 19.0% and 25.2% increase of butyrate titer in shake flasks with sgRNA variants targeting sucA and ldhA, respectively. This work reported the establishment of a plug-and-play approach that enables multilevel modulation of metabolic fluxes and high-throughput screening of high-production phenotypes.

Bispecific antibodies (BsAbs) represent an emerging class of immunotherapy, but inefficiency in the current discovery has limited their broad clinical availability. Here we report a high throughput, agnostic, single-cell-based functional screening pipeline, comprising molecular and cell engineering for efficient generation of BsAb library cells, followed by functional interrogation at the single-cell level to identify and sort positive clones and downstream sequence identification and functionality characterization. Using a CD19xCD3 bispecific T cell engager (BiTE) as a model, we demonstrate that our single-cell platform possesses a high throughput screening efficiency of up to one and a half million variant library cells per run and can isolate rare functional clones at a low abundance of 0.008%. Using a complex CD19xCD3 BiTE-expressing cell library with approximately 22,300 unique variants comprising combinatorially varied scFvs, connecting linkers and VL/VH orientations, we have identified 98 unique clones, including extremely rare ones (~ 0.001% abundance). We also discovered BiTEs that exhibit novel properties and insights to design variable preferences for functionality. We expect our single-cell platform to not only increase the discovery efficiency of new immunotherapeutics, but also enable identifying generalizable design principles based on an in-depth understanding of the inter-relationships between sequence, structure, and function.

Testing and vaccination have been major components of the strategy for combating the ongoing COVID-19 pandemic. In this study, we have developed a quantitative anti-SARS-CoV-2 spike (S1) IgG antibody assay using a fingerstick dried blood sample. We evaluated the feasibility of using this high-throughput and quantitative anti-SARS-CoV-2 spike (S1) IgG antibody testing assay in vaccinated individuals. Fingerstick blood samples were collected and analyzed from 137 volunteers before and after receiving the Moderna or Pfizer mRNA vaccine. Anti-SARS-CoV-2 S1 IgG antibody could not be detected within the first 7 days after receiving the first vaccine dose, however, the assay reliably detected antibodies from day 14 onwards. In addition, no anti-SARS-CoV-2 nucleocapsid (N) protein IgG antibody was detected in any of the vaccinated or healthy participants, indicating that the anti-SARS-CoV-2 S1 IgG assay is specific for the mRNA vaccine-induced antibodies. The S1 IgG levels detected in fingerstick samples correlated with the levels found in venous blood plasma samples and with the efficacy of venous blood plasma samples in the plaque reduction neutralization test (PRNT). The assay displayed a limit of quantification (LOQ) of 0.59 μg/mL and was found to be linear in the range of 0.51-1000 μg/mL. Finally, its clinical performance displayed a Positive Percent Agreement (PPA) of 100% (95% CI: 0.89-1.00) and a Negative Percent Agreement (NPA) of 100% (95% CI: 0.93-1.00). In summary, the assay described here represents a sensitive, precise, accurate, and simple method for the quantitative detection and monitoring of post-vaccination anti-SARS-CoV-2 spike IgG responses.

Two-dimensional high-throughput data have become increasingly common in functional genomics studies, which raises new challenges in data analysis. Here, we introduce a new statistic called Zeta, initially developed to identify global splicing regulators from a two-dimensional RNAi screen, a high-throughput screen coupled with high-throughput functional readouts, and ZetaSuite, a software package to facilitate general application of the Zeta statistics. We compare our approach with existing methods using multiple benchmarked datasets and then demonstrate the broad utility of ZetaSuite in processing public data from large-scale cancer dependency screens and single-cell transcriptomics studies to elucidate novel biological insights.

ABSTRACT Molecular-based assays demonstrate excellent sensitivity for the detection of vaginitis causes. Here, the high-throughput BD Vaginal Panel for BD COR System (VP-COR) performance was compared to that of the predicate, BD MAX Vaginal Panel for BD MAX System (VP-MAX). Clinical or contrived samples were used to determine the agreement between VP-COR and VP-MAX. Acceptance criteria for VP-COR agreement were as follows: bacterial vaginosis (BV) required a positive percent agreement (PPA) point estimate of ≥95% and a negative percent agreement (NPA) point estimate of ≥98%; Candida group, Candida glabrata, Candida krusei, and Trichomonas vaginalis (TV) required a PPA and NPA point estimate of ≥95% [with lower bound of 95% confidence interval (95% CI) ≥90%]. PPA was 99.5% (95% CI: 97.5–100) and 97.9% (95% CI: 96.5–98.8) for BV contrived (n = 516) and BV clinical (n = 1,050) specimens, respectively. For the Candida group (clinical; n = 724), C. glabrata (contrived; n = 544), C. krusei (contrived; n = 522), and TV (clinical; n = 702), PPA was 99.4% (95% CI: 98.0–99.9), 100% (95% CI: 97.9–100), 100% (95% CI: 97.6–100), and 99.7% (95% CI: 98.3–100), respectively; the lowest lower bound CI value was 97.6%. NPA was >95% for BV contrived and BV clinical specimens. For the Candida group, C. glabrata, C. krusei, and TV, NPA was ≥98.9%; the lowest lower bound CI value was 97.3%. These results demonstrate the equivalent performance of the VP-COR assay when compared to VP-MAX.IMPORTANCEVaginitis is common among women of reproductive age, resulting in around 10 million office visits a year. Diagnosis is often difficult due to its multiple causes—including bacterial vaginosis, vulvovaginal candidiasis, and trichomoniasis—as well as variation in symptom presentation. Typically, cases are identified with a combination of symptomology, medical history, physical examination, and office- or laboratory-based testing. These traditional techniques involve subjective elements and demonstrate varying sensitivity and specificity. Inaccurate or delayed diagnosis leads to continued symptoms, repeat visits, inappropriate treatment, and unnecessary costs. Alternatively, the use of molecular-based assays increases sensitivity for the detection of vaginitis causes. With the validation of the vaginal panel molecular assay on COR (a high-throughput platform), a workflow can be streamlined in high-demand laboratories while providing high sensitivity for vaginitis detection.

In drop-based microfluidics, an aqueous sample is partitioned into drops using individual pump sources that drive water and oil into a drop-making device. Parallelization of drop-making devices is necessary to achieve high-throughput screening of multiple experimental conditions, especially in time-sensitive studies. Here, we present the plate-interfacing parallel encapsulation (PIPE) chip, a microfluidic chip designed to generate 50 to 90 μm diameter drops of up to 96 different conditions in parallel by interfacing individual drop makers with a standard 384-well microtiter plate. The PIPE chip is used to generate two types of optically barcoded drop libraries consisting of two-color fluorescent particle combinations: a library of 24 microbead barcodes and a library of 192 quantum dot barcodes. Barcoded combinations in the drop libraries are rapidly measured within a microfluidic device using fluorescence detection and distinct barcoded populations in the fluorescence drop data are identified using DBSCAN data clustering. Signal analysis reveals that particle size defines the source of dominant noise present in the fluorescence intensity distributions of the barcoded drop populations, arising from Poisson loading for microbeads and shot noise for quantum dots. A barcoded population from a drop library is isolated using fluorescence-activated drop sorting, enabling downstream analysis of drop contents. The PIPE chip can improve multiplexed high-throughput assays by enabling simultaneous encapsulation of barcoded samples stored in a microtiter plate and reducing sample preparation time.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

The single-celled eukaryotic green alga Chlamydomonas reinhardtii has long been a model system for developing genetic tools for algae, and is also considered a potential platform for the production of high-value recombinant proteins. Identifying transformants with high levels of recombinant protein expression has been a challenge in this organism, as random integration of transgenes into the nuclear genome leads to low frequency of cell lines with high gene expression. Here, we describe the design of an optimized vector for the expression of recombinant proteins in Chlamydomonas, that when transformed and screened using a dual antibiotic selection, followed by screening using fluorescence activated cell sorting (FACS), permits rapid identification and isolation of microalgal transformants with high expression of a recombinant protein. This process greatly reduces the time required for the screening process, and can produce large populations of recombinant algae transformants with between 60 and 100% of cells producing the recombinant protein of interest, in as little as 3 weeks, that can then be used for whole population sequencing or individual clone analysis. Utilizing this new vector and high-throughput screening (HTS) process resulted in an order of magnitude improvement over existing methods, which normally produced under 1% of algae transformants expressing the protein of interest. This process can be applied to other algal strains and recombinant proteins to enhance screening efficiency, thereby speeding up the discovery and development of algal-derived recombinant protein products. KEY POINTS: • A protein expression vector using double-antibiotic resistance genes was designed • Double antibiotic selection causes fewer colonies with more positive for phenotype • Coupling the new vector with FACS improves microalgal screening efficiency > 60.

Production of high-energy lipids by microalgae may provide a sustainable energy source that can help tackle climate change. However, microalgae engineered to produce more lipids usually grow slowly, leading to reduced overall yields. Unfortunately, culture vessels used to select cells based on growth while maintaining high biomass production, such as well plates, water-in-oil droplet emulsions, and nanowell arrays, do not provide production-relevant environments that cells experience in scaled-up cultures (e.g., bioreactors or outdoor cultivation farms). As a result, strains that are developed in the laboratory may not exhibit the same beneficial phenotypic behavior when transferred to industrial production. Here, we introduce PicoShells, picoliter-scale porous hydrogel compartments, that enable >100,000 individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and bioproduct accumulation traits using standard flow cytometers. PicoShells consist of a hollow inner cavity where cells are encapsulated and a porous outer shell that allows for continuous solution exchange with the external environment. PicoShells allow for cell growth directly in culture environments, such as shaking flasks and bioreactors. We experimentally demonstrate that Chlorella sp., Saccharomyces cerevisiae, and Chinese hamster ovary cells, used for bioproduction, grow to significantly larger colony sizes in PicoShells than in water-in-oil droplet emulsions (P < 0.05). We also demonstrate that PicoShells containing faster dividing and growing Chlorella clonal colonies can be selected using a fluorescence-activated cell sorter and regrown. Using the PicoShell process, we select a Chlorella population that accumulates chlorophyll 8% faster than does an unselected population after a single selection cycle.

Cell-free protein synthesis can enable the combinatorial screening of many different components and concentrations. However, manual pipetting methods are unfit to handle many cell-free reactions. Here, we describe a microfluidic method that can generate hundreds of unique submicroliter scale reactions. The method is coupled with a high yield cell-free system that can be applied for broad protein screening assays.

Attempts to minimize scarring remain among the most difficult challenges facing surgeons, despite the use of optimal wound closure techniques. Previously, we reported improved healing of dermal excisional wounds in circadian clock neuronal PAS domain 2 (Npas2)-null mice. In this study, we performed high-throughput drug screening to identify a compound that downregulates Npas2 activity. The hit compound (Dwn1) suppressed circadian Npas2 expression, increased murine dermal fibroblast cell migration, and decreased collagen synthesis in vitro. Based on the in vitro results, Dwn1 was topically applied to iatrogenic full-thickness dorsal cutaneous wounds in a murine model. The Dwn1-treated dermal wounds healed faster with favorable mechanical strength and developed less granulation tissue than the controls. The expression of type I collagen, Tgfβ1, and α-smooth muscle actin was significantly decreased in Dwn1-treated wounds, suggesting that hypertrophic scarring and myofibroblast differentiation are attenuated by Dwn1 treatment. NPAS2 may represent an important target for therapeutic approaches to optimal surgical wound management.

The N-terminal region (NTR) of ryanodine receptor (RyR) channels is critical for the regulation of Ca2+ release during excitation-contraction (EC) coupling in muscle. The NTR hosts numerous mutations linked to skeletal (RyR1) and cardiac (RyR2) myopathies, highlighting its potential as a therapeutic target. Here, we constructed two biosensors by labeling the mouse RyR2 NTR at domains A, B, and C with FRET pairs. Using fluorescence lifetime (FLT) detection of intramolecular FRET signal, we developed high-throughput screening (HTS) assays with these biosensors to identify small-molecule RyR modulators. We then screened a small validation library and identified several hits. Hits with saturable FRET dose-response profiles and previously unreported effects on RyR were further tested using [3H]ryanodine binding to isolated sarcoplasmic reticulum vesicles to determine effects on intact RyR opening in its natural membrane. We identified three novel inhibitors of both RyR1 and RyR2 and two RyR1-selective inhibitors effective at nanomolar Ca2+. Two of these hits activated RyR1 only at micromolar Ca2+, highlighting them as potential enhancers of excitation-contraction coupling. To determine whether such hits can inhibit RyR leak in muscle, we further focused on one, an FDA-approved natural antibiotic, fusidic acid (FA). In skinned skeletal myofibers and permeabilized cardiomyocytes, FA inhibited RyR leak with no detrimental effect on skeletal myofiber excitation-contraction coupling. However, in intact cardiomyocytes, FA induced arrhythmogenic Ca2+ transients, a cautionary observation for a compound with an otherwise solid safety record. These results indicate that HTS campaigns using the NTR biosensor can identify compounds with therapeutic potential.

Behavior arises from concerted activity throughout the brain. Consequently, a major focus of modern neuroscience is defining the physiology and behavioral roles of projection neurons linking different brain areas. Single-cell RNA sequencing has facilitated these efforts by revealing molecular determinants of cellular physiology and markers that enable genetically targeted perturbations such as optogenetics, but existing methods for sequencing defined projection populations are low throughput, painstaking, and costly. We developed a straightforward, multiplexed approach, virally encoded connectivity transgenic overlay RNA sequencing (VECTORseq). VECTORseq repurposes commercial retrogradely infecting viruses typically used to express functional transgenes (e.g., recombinases and fluorescent proteins) by treating viral transgene mRNA as barcodes within single-cell datasets. VECTORseq is compatible with different viral families, resolves multiple populations with different projection targets in one sequencing run, and identifies cortical and subcortical excitatory and inhibitory projection populations. Our study provides a roadmap for high-throughput identification of neuronal subtypes based on connectivity.

Differential mobility spectrometry (DMS) is highly useful for shotgun lipidomic analysis because it overcomes difficulties in measuring isobaric species within a complex lipid sample and allows for acyl tail characterization of phospholipid species. Despite these advantages, the resulting workflow presents technical challenges, including the need to tune the DMS before every batch to update compensative voltages settings within the method. The Sciex Lipidyzer platform uses a Sciex 5500 QTRAP with a DMS (SelexION), an LC system configured for direction infusion experiments, an extensive set of standards designed for quantitative lipidomics, and a software package (Lipidyzer Workflow Manager) that facilitates the workflow and rapidly analyzes the data. Although the Lipidyzer platform remains very useful for DMS-based shotgun lipidomics, the software is no longer updated for current versions of Analyst and Windows. Furthermore, the software is fixed to a single workflow and cannot take advantage of new lipidomics standards or analyze additional lipid species. To address this multitude of issues, we developed Shotgun Lipidomics Assistant (SLA), a Python-based application that facilitates DMS-based lipidomics workflows. SLA provides the user with flexibility in adding and subtracting lipid and standard MRMs. It can report quantitative lipidomics results from raw data in minutes, comparable to the Lipidyzer software. We show that SLA facilitates an expanded lipidomics analysis that measures over 1450 lipid species across 17 (sub)classes. Lastly, we demonstrate that the SLA performs isotope correction, a feature that was absent from the original software.

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Parkinson's disease (PD) is a common neurodegenerative disorder without effective disease-modifying therapeutics. Here, we establish a chemogenetic dopamine (DA) neuron ablation model in larval zebrafish with mitochondrial dysfunction and robustness suitable for high-content screening. We use this system to conduct an in vivo DA neuron imaging-based chemical screen and identify the Renin-Angiotensin-Aldosterone System (RAAS) inhibitors as significantly neuroprotective. Knockdown of the angiotensin receptor 1 (agtr1) in DA neurons reveals a cell-autonomous mechanism of neuroprotection. DA neuron-specific RNA-seq identifies mitochondrial pathway gene expression that is significantly restored by RAAS inhibitor treatment. The neuroprotective effect of RAAS inhibitors is further observed in a zebrafish Gaucher disease model and Drosophila pink1-deficient PD model. Finally, examination of clinical data reveals a significant effect of RAAS inhibitors in delaying PD progression. Our findings reveal the therapeutic potential and mechanisms of targeting the RAAS pathway for neuroprotection and demonstrate a salient approach that bridges basic science to translational medicine.

Cyclohexanone monooxygenases (CHMO) consume molecular oxygen and NADPH to catalyze the valuable oxidation of cyclic ketones. However, CHMO usage is restricted by poor stability and stringent specificity for NADPH. Efforts to engineer CHMO have been limited by the sensitivity of the enzyme to perturbations in conformational dynamics and long-range interactions that cannot be predicted. We demonstrate an aerobic, high-throughput growth selection platform in Escherichia coli for oxygenase evolution based on NADH redox balance. We applied this NADH-dependent selection to alter the cofactor specificity of CHMO to accept NADH, a less expensive cofactor than NADPH. We first identified the variant CHMO DTNP (S208D-K326T-K349N-L143P) with a ∼1200-fold relative cofactor specificity switch from NADPH to NADH compared to the wild type through semirational design. Molecular modeling suggests CHMO DTNP activity is driven by cooperative fine-tuning of cofactor contacts. Additional evolution of CHMO DTNP through random mutagenesis yielded the variant CHMO DTNPY with a ∼2900-fold relative specificity switch compared to the wild type afforded by an additional distal mutation, H163Y. These results highlight the difficulty in engineering functionally innovative variants from static models and rational designs, and the need for high throughput selection methods. Our introduced tools for oxygenase engineering accelerate the advancements of characteristics essential for industrial feasibility.

Introduction: Naegleria fowleri is responsible for primary amebic meningoencephalitis (PAM) which has a fatality rate of >97%. Because of the rarity of the disease, pharmaceutical companies do not pursue new drug discovery for PAM. Yet, it is possible that the infection is underreported and finding a better drug would have an impact on people suffering from this deadly infection.Areas covered: This paper reports the efforts undertaken by different academic groups over the last 20 years to test different compounds against N. fowleri. The drug discovery research encompassed synthesis of new compounds, development and use of high-throughput screening methods and attempts to repurpose clinically developed or FDA-approved compounds for the treatment of PAM.Expert opinion: In absence of economic investment to develop new drugs for PAM, repurposing the FDA-approved drugs has been the best strategy so far to identify new leads against N. fowleri. Increasing use of high-throughput phenotypic screening has the potential to accelerate the identification of new leads, either in monotherapy or in combination treatment. Since phase II clinical trial is not possible for PAM, it is critical to demonstrate in vivo efficacy of a clinically safe compound to translate the discovery from lab to the clinic.

Here, we describe the use of polarized patient enteroid-derived monolayers (EDMs) to assess the impact of e-cigarettes on the human gut barrier. These EDMs can be adapted to culture in a 96-well plate for high-throughput screening. We model the effect of e-cigarettes by combining pathogens, enteroids, and e-cigarette vapor-infused media and assess gut barrier integrity, bacterial internalization, and inflammatory response of the gut epithelium. This protocol can be used to assess the effects of e-cigarette components on gut functions. For complete details on the use and execution of this protocol, please refer to Sharma et al. (2021).

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen.

In this paper, we applied an innovative nuclear magnetic resonance (NMR)-guided screening and ligand design approach, named focused high-throughput screening by NMR (fHTS by NMR), to derive potent, low-molecular-weight ligands capable of mimicking interactions elicited by ephrin ligands on the receptor tyrosine kinase EphA4. The agents bind with nanomolar affinity, trigger receptor activation in cellular assays with motor neurons, and provide remarkable motor neuron protection from amyotrophic lateral sclerosis (ALS) patient-derived astrocytes. Structural studies on the complex between EphA4 ligand-binding domain and a most active agent provide insights into the mechanism of the agents at a molecular level. Together with preliminary in vivo pharmacology studies, the data form a strong foundation for the translation of these agents for the treatment of ALS and potentially other human diseases.

Oncogenic forms of KRAS proteins are known to be drivers of pancreatic, colorectal, and lung cancers. The goal of this study is to identify chemical leads that inhibit oncogenic KRAS signaling. We first developed an isogenic panel of mouse embryonic fibroblast (MEF) cell lines that carry wild-type RAS, oncogenic KRAS, and oncogenic BRAF. We validated these cell lines by screening against a tool compound library of 1402 annotated inhibitors in an adenosine triphosphate (ATP)-based cell viability assay. Subsequently, this MEF panel was used to conduct a high-throughput phenotypic screen in a cell viability assay with a proprietary compound library. All 126 compounds that exhibited a selective activity against mutant KRAS were selected and prioritized based on their activities in secondary assays. Finally, five chemical clusters were chosen. They had specific activity against SW620 and LS513 over Colo320 colorectal cancer cell lines. In addition, they had no effects on BRAFV600E, MEK1, extracellular signal-regulated kinase 2 (ERK2), phosphoinositide 3-kinase alpha (PI3Kα), AKT1, or mammalian target of rapamycin (mTOR) as tested in in vitro enzymatic activity assays. Biophysical assays demonstrated that these compounds did not bind directly to KRAS. We further identified the mechanism of action and showed that three of them have CDK9 inhibitory activity. In conclusion, we have developed and validated an isogenic MEF panel that was used successfully to identify RAS oncogenic or wild-type allele-specific vulnerabilities. Furthermore, we identified sensitivity of oncogenic KRAS-expressing cells to CDK9 inhibitors, which warrants future studies of treating KRAS-driven cancers with CDK9 inhibitors.

Self-assembling peptides have shown tremendous potential in the fields of material sciences, nanoscience, and medicine. Because of the vast combinatorial space of even short peptides, identification of self-assembling sequences remains a challenge. Herein, we develop an experimental method to rapidly screen a huge array of peptide sequences for self-assembling property, using the one-bead one-compound (OBOC) combinatorial library method. In this approach, peptides on beads are N-terminally capped with nitro-1,2,3-benzoxadiazole, a hydrophobicity-sensitive fluorescence molecule. Beads displaying self-assembling peptides would fluoresce under aqueous environment. Using this approach, we identify eight pentapeptides, all of which are able to self-assemble into nanoparticles or nanofibers. Some of them are able to interact with and are taken up efficiently by HeLa cells. Intracellular distribution varied among these non-toxic peptidic nanoparticles. This simple screening strategy has enabled rapid identification of self-assembling peptides suitable for the development of nanostructures for various biomedical and material applications.

[Figure: see text].

Pseudomonas putida KT2440 is an emerging biomanufacturing host amenable for use with renewable carbon streams including aromatics such as para-coumarate. We used a pooled transposon library disrupting nearly all (4,778) non-essential genes to characterize this microbe under common stirred-tank bioreactor parameters with quantitative fitness assays. Assessing differential fitness values by monitoring changes in mutant strain abundance identified 33 gene mutants with improved fitness across multiple stirred-tank bioreactor formats. Twenty-one deletion strains from this subset were reconstructed, including GacA, a regulator, TtgB, an ABC transporter, and PP_0063, a lipid A acyltransferase. Thirteen deletion strains with roles in varying cellular functions were evaluated for conversion of para-coumarate, to a heterologous bioproduct, indigoidine. Several mutants, such as the ΔgacA strain improved fitness in a bioreactor by 35 fold and showed an 8-fold improvement in indigoidine production (4.5 g/L, 0.29 g/g, 23% of maximum theoretical yield) from para-coumarate as the carbon source.

Trichomoniasis is the most common nonviral sexually transmitted disease in humans, but treatment options are limited. Here, we report a resorufin-based drug sensitivity assay for high-throughput microplate-based screening under hypoxic conditions. A 5203-compound enamine kinase library and several specialized compound series were tested for the inhibition of Trichomonas growth at 10 μM with Z' values of >0.5. Hits were rescreened in serial dilution to establish an IC50 concentration. A series of 7-substituted 7-deazaadenosine analogues emerged as highly potent anti-T. vaginalis agents, with EC50 values in the low double digit nanomolar range. These analogues exhibited excellent selectivity indices. Follow-up medicinal chemistry efforts identified an optimal ribofuranose and C7 substituent. Several nucleosides rapidly cleared cultures of T. vaginalis at a concentrations of just 2 × EC50. Preliminary in vivo evaluation in a murine trichomoniasis model (Tritrichomonas foetus) revealed promising activity upon topical administration, validating purine nucleoside analogues as a new class of antitrichomonal agents.

The ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. Here, we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. We screen a best-in-class drug repurposing library, ReFRAME, against two high-throughput, high-content imaging infection assays: one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells. From nearly 12,000 compounds, we identify 49 (in HeLa-ACE2) and 41 (in Calu-3) compounds capable of selectively inhibiting SARS-CoV-2 replication. Notably, most screen hits are cell-line specific, likely due to different virus entry mechanisms or host cell-specific sensitivities to modulators. Among these promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 possess desirable in vitro activity, pharmacokinetic and human safety profiles, and both reduce SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. Furthermore, MK-4482 effectively blocks SARS-CoV-2 infection in a hamster model. Overall, we identify direct-acting antivirals as the most promising compounds for drug repurposing, additional compounds that may have value in combination therapies, and tool compounds for identification of viral host cell targets.

RNA binding proteins (RBPs) are a large and diverse class of proteins that regulate all aspects of RNA biology. As RBP dysregulation has been implicated in a number of human disorders, including cancers and neurodegenerative disease, small molecule chemical probes that target individual RBPs represent useful tools for deciphering RBP function and guiding the production of new therapeutics. While RBPs are often thought of as tough-to-drug, the discovery of a number of small molecules that target RBPs has spurred considerable recent interest in new strategies for RBP chemical probe discovery. Here we review current and emerging technologies for high throughput RBP-small molecule screening that we expect will help unlock the full therapeutic potential of this exciting protein class.

Heparan sulfate (HS) proteoglycans bind extracellular proteins that participate in cell signaling, attachment and endocytosis. These interactions depend on the arrangement of sulfated sugars in the HS chains generated by well-characterized biosynthetic enzymes; however, the regulation of these enzymes is largely unknown. We conducted genome-wide CRISPR-Cas9 screens with a small-molecule ligand that binds to HS. Screening of A375 melanoma cells uncovered additional genes and pathways impacting HS formation. The top hit was the epigenetic factor KDM2B, a histone demethylase. KDM2B inactivation suppressed multiple HS sulfotransferases and upregulated the sulfatase SULF1. These changes differentially affected the interaction of HS-binding proteins. KDM2B-deficient cells displayed decreased growth rates, which was rescued by SULF1 inactivation. In addition, KDM2B deficiency altered the expression of many extracellular matrix genes. Thus, KDM2B controls proliferation of A375 cells through the regulation of HS structure and serves as a master regulator of the extracellular matrix.

Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis. For complete details on the use and execution of this protocol, please refer to Chen et al. (2020).

Candida albicans is a fungal component of the human gut microbiota and an opportunistic pathogen. C. albicans transcription factors (TFs), Wor1 and Efg1, are master regulators of an epigenetic switch required for fungal mating that also control colonization of the mammalian gut. We show that additional mating regulators, WOR2, WOR3, WOR4, AHR1, CZF1, and SSN6, also influence gut commensalism. Using Calling Card-seq to record Candida TF DNA-binding events in the host, we examine the role and relationships of these regulators during murine gut colonization. By comparing in-host transcriptomes of regulatory mutants with enhanced versus diminished commensal fitness, we also identify a set of candidate commensalism effectors. These include Cht2, a GPI-linked chitinase whose gene is bound by Wor1, Czf1, and Efg1 in vivo, that we show promotes commensalism. Thus, the network required for a C. albicans sexual switch is biochemically active in the host intestine and repurposed to direct commensalism.

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here, we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over nonencapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher's fundamental theorem of natural selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function but also promote activity and evolutionary adaptation during the origin of life.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Bromodomain testis (BRDT), a member of the bromodomain and extraterminal (BET) subfamily that includes the cancer targets BRD2, BRD3, and BRD4, is a validated contraceptive target. All BET subfamily members have two tandem bromodomains (BD1 and BD2). Knockout mice lacking BRDT-BD1 or both bromodomains are infertile. Treatment of mice with JQ1, a BET BD1/BD2 nonselective inhibitor with the highest affinity for BRD4, disrupts spermatogenesis and reduces sperm number and motility. To assess the contribution of each BRDT bromodomain, we screened our collection of DNA-encoded chemical libraries for BRDT-BD1 and BRDT-BD2 binders. High-enrichment hits were identified and resynthesized off-DNA and examined for their ability to compete with JQ1 in BRDT and BRD4 bromodomain AlphaScreen assays. These studies identified CDD-1102 as a selective BRDT-BD2 inhibitor with low nanomolar potency and >1,000-fold selectivity over BRDT-BD1. Structure-activity relationship studies of CDD-1102 produced a series of additional BRDT-BD2/BRD4-BD2 selective inhibitors, including CDD-1302, a truncated analog of CDD-1102 with similar activity, and CDD-1349, an analog with sixfold selectivity for BRDT-BD2 versus BRD4-BD2. BROMOscan bromodomain profiling confirmed the great affinity and selectivity of CDD-1102 and CDD-1302 on all BET BD2 versus BD1 with the highest affinity for BRDT-BD2. Cocrystals of BRDT-BD2 with CDD-1102 and CDD-1302 were determined at 2.27 and 1.90 Å resolution, respectively, and revealed BRDT-BD2 specific contacts that explain the high affinity and selectivity of these compounds. These BD2-specific compounds and their binding to BRDT-BD2 are unique compared with recent reports and enable further evaluation of their nonhormonal contraceptive potential in vitro and in vivo.

Organelles are responsible for biochemical and cellular processes that sustain life and their dysfunction causes diseases from cancer to neurodegeneration. While researchers are continuing to appreciate new roles of organelles in disease, the rapid development of specifically targeted fluorescent probes that report on the structure and function of organelles will be critical to accelerate drug discovery. Here, we highlight four organelles that collectively exemplify the progression of phenotypic discovery, starting with mitochondria, where many functional probes have been described, then continuing with lysosomes and Golgi and concluding with nascently described membraneless organelles. We introduce emerging probe designs to explore organelle-specific morphology and dynamics and highlight recent case studies using high-content analysis to stimulate further development of probes and approaches for organellar high-throughput screening.

The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.

Abstract As agricultural production is reaching its limits regarding outputs and land use, the need to further improve crop yield is greater than ever. The limited translatability from in vitro lab results into more natural growth conditions in soil remains problematic. Although considerable progress has been made in developing soil‐growth assays to tackle this bottleneck, the majority of these assays use pots or whole trays, making them not only space‐ and resource‐intensive, but also hampering the individual treatment of plants. Therefore, we developed a flexible and compact screening system named PhenoWell® in which individual seedlings are grown in wells filled with soil allowing single‐plant treatments. The system makes use of an automated image‐analysis pipeline that extracts multiple growth parameters from individual seedlings over time, including projected rosette area, relative growth rate, compactness, and stockiness. Macronutrient, hormone, salt, osmotic, and drought stress treatments were tested in the PhenoWell® system. The system is also optimized for maize with results that are consistent with Arabidopsis while different in amplitude. We conclude that the PhenoWell® system enables a high‐throughput, precise, and uniform application of a small amount of solution to individually soil‐grown plants, which increases the replicability and reduces variability and compound usage.

Computational models based on recent maps of the RBC proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to RBC storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic, or environmental exposures ("exposome") may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and posttransfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and over-the-counter drugs in blood units donated by 250 healthy volunteers in the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell-Omics (REDS-III RBC-Omics) Study. Based on high-throughput drug screenings of 1366 FDA-approved drugs, we report that approximately 65% of the tested drugs had an impact on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR), suggesting that these drugs have a direct, conserved, and substantial impact on erythrocyte metabolism. As a proof of principle, here we show that the antacid ranitidine - though rarely detected in the blood donor population - has a strong effect on RBC markers of storage quality in vitro. We thus show that supplementation of blood units stored in bags with ranitidine could - through mechanisms involving sphingosine 1-phosphate-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin - improve erythrocyte metabolism and storage quality.

Cutaneous T-cell lymphoma is a form of non-Hodgkin lymphoma that manifests initially in the skin and disseminates systemically as the disease progresses. Mycosis fungoides and Sézary syndrome are the most common subtypes of cutaneous T-cell lymphoma. Advanced mycosis fungoides and Sézary syndrome are life threatening with few treatment options. We searched for new agents by high-throughput screening of selected targeted compounds and identified high-value targets, including phosphatidylinositol 3-kinase (PI3K) and cyclin-dependent kinases. To validate these hits from the screen, we developed patient-derived xenograft mouse models that recapitulated the cardinal features of mycosis fungoides and Sézary syndrome and maintained histologic and molecular characteristics of their clinical counterparts. Importantly, we established a blood-based biomarker assay using tumor cell-free DNA to measure systemic tumor burden longitudinally in living mice during drug therapy. A PI3K inhibitor, BKM120, was tested in our patient-derived xenograft model leading to disease attenuation and prolonged survival. Isoform-specific small interfering RNA knockdowns and isoform-selective PI3K inhibitors identified PI3K-δ as required for tumor proliferation. Additional studies showed a synergistic combination of PI3K-α/δ inhibitors with histone deacetylase inhibitors. The strong preclinical efficacy of this potent combination against multiple patient-derived xenograft models makes it an excellent candidate for further clinical development.

G protein-coupled receptors (GPCRs) are a group of seven-transmembrane receptor proteins that have proven to be successful drug targets. Antibodies are becoming an increasingly promising modality to target these receptors due to their unique properties, such as exquisite specificity, long half-life, and fewer side effects, and their improved pharmacokinetic and pharmacodynamic profiles compared to peptides and small molecules, which results from their more favorable biodistribution. To date, there are only two US Food and Drug Administration-approved GPCR antibody drugs, namely erenumab and mogamulizumab, and this highlights the challenges encountered in identifying functional antibodies against GPCRs. Utilizing Twists precision DNA writing technologies, we have created a GPCR-focused phage display library with 1 × 1010 diversity. Specifically, we mined endogenous GPCR binding ligand and peptide sequences and incorporated these binding motifs into the heavy chain complementarity-determining region 3 in a synthetic antibody library. Glucagon-like peptide-1 receptor (GLP-1 R) is a class B GPCR that acts as the receptor for the incretin GLP-1, which is released to regulate insulin levels in response to food intake. GLP-1 R agonists have been widely used to increase insulin secretion to lower blood glucose levels for the treatment of type 1 and type 2 diabetes, whereas GLP-1 R antagonists have applications in the treatment of severe hypoglycemia associated with bariatric surgery and hyperinsulinomic hypoglycemia. Here we present the discovery and creation of both antagonistic and agonistic GLP-1 R antibodies by panning this GPCR-focused phage display library on a GLP-1 R-overexpressing Chinese hamster ovary cell line and demonstrate their in vitro and in vivo functional activity.

Regulating protein production and degradation is critical to maintaining cellular homeostasis. The proteasome is a key player in keeping proteins at the proper levels. However, proteasome activity can be altered in certain disease states, such as blood cancers and neurodegenerative diseases. Cancers often exhibit enhanced proteasomal activity, as protein synthesis is increased in these cells compared with normal cells. Conversely, neurodegenerative diseases are characterized by protein accumulation, leading to reduced proteasome activity. As a result, the proteasome has emerged as a target for therapeutic intervention. The potential of the proteasome as a therapeutic target has come from studies involving chemical stimulators and inhibitors, and the development of a suite of assays and probes that can be used to monitor proteasome activity with purified enzyme and in live cells.

Microbial interactions are expected to be major determinants of microbiome structure and function. Although fungi are found in diverse microbiomes, their interactions with bacteria remain largely uncharacterized. In this work, we characterize interactions in 16 different bacterial-fungal pairs, examining the impacts of 8 different fungi isolated from cheese rind microbiomes on 2 bacteria (Escherichia coli and a cheese-isolated Pseudomonas psychrophila). Using random barcode transposon-site sequencing with an analysis pipeline that allows statistical comparisons between different conditions, we observed that fungal partners caused widespread changes in the fitness of bacterial mutants compared to growth alone. We found that all fungal species modulated the availability of iron and biotin to bacterial species, which suggests that these may be conserved drivers of bacterial-fungal interactions. Species-specific interactions were also uncovered, a subset of which suggested fungal antibiotic production. Changes in both conserved and species-specific interactions resulted from the deletion of a global regulator of fungal specialized metabolite production. This work highlights the potential for broad impacts of fungi on bacterial species within microbiomes.