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The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1–9. Deep learning approaches have aided in exploring chemical spaces1,10–15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureuswere enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus(MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Synthetic biology aims to modify cellular behaviors by implementing genetic circuits that respond to changes in cell state. Integrating genetic biosensors into endogenous gene coding sequences using clustered regularly interspaced short palindromic repeats and Cas9 enables interrogation of gene expression dynamics in the appropriate chromosomal context. However, embedding a biosensor into a gene coding sequence may unpredictably alter endogenous gene regulation. To address this challenge, we developed an approach to integrate genetic biosensors into endogenous genes without modifying their coding sequence by inserting into their terminator region single-guide RNAs that activate downstream circuits. Sensor dosage responses can be fine-tuned and predicted through a mathematical model. We engineered a cell stress sensor and actuator in CHO-K1 cells that conditionally activates antiapoptotic protein BCL-2 through a downstream circuit, thereby increasing cell survival under stress conditions. Our gene sensor and actuator platform has potential use for a wide range of applications that include biomanufacturing, cell fate control and cell-based therapeutics.

Acinetobacter baumanniiis a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumanniihas proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumanniiin vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumanniiinfection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.

Intercellular communication orchestrates a multitude of physiologic and pathologic conditions. Algorithms to infer cell–cell communication and predict downstream signalling and regulatory networks are needed to illuminate mechanisms of stem cell differentiation and tissue development. Here, to fill this gap, we developed and applied CellComm to investigate how the aorta–gonad–mesonephros microenvironment dictates haematopoietic stem and progenitor cell emergence. We identified key microenvironmental signals and transcriptional networks that regulate haematopoietic development, including Stat3, Nr0b2, Ybx1and App, and confirmed their roles using zebrafish, mouse and human models. Notably, CellComm revealed extensive crosstalk among signalling pathways and convergence on common transcriptional regulators, indicating a resilient developmental programme that ensures dynamic adaptation to changes in the embryonic environment. Our work provides an algorithm and data resource for the scientific community.

CRISPR-based diagnostics enable specific sensing of DNA and RNA biomarkers associated with human diseases. This is achieved through the binding of guide RNAs to a complementary sequence that activates Cas enzymes to cleave reporter molecules. Currently, most CRISPR-based diagnostics rely on target preamplification to reach sufficient sensitivity for clinical applications. This limits quantification capability and adds complexity to the reaction chemistry. Here we show the combination of a CRISPR–Cas-based reaction with a nanozyme-linked immunosorbent assay, which allows for the quantitative and colorimetric readout of Cas13-mediated RNA detection through catalytic metallic nanoparticles at room temperature (CrisprZyme). We demonstrate that CrisprZyme is easily adaptable to a lateral-flow-based readout and different Cas enzymes and enables the sensing of non-coding RNAs including microRNAs, long non-coding RNAs and circular RNAs. We utilize this platform to identify patients with acute myocardial infarction and to monitor cellular differentiation in vitro and in tissue biopsies from prostate cancer patients. We anticipate that CrisprZyme will serve as a universally applicable signal catalyst for CRISPR-based diagnostics, which will expand the spectrum of targets for preamplification-free, quantitative detection.

The steadfast advance of the synthetic biology field has enabled scientists to use genetically engineered cells, instead of small molecules or biologics, as the basis for the development of novel therapeutics. Cells endowed with synthetic gene circuits can control the localization, timing and dosage of therapeutic activities in response to specific disease biomarkers and thus represent a powerful new weapon in the fight against disease. Here, we conceptualize how synthetic biology approaches can be applied to programme living cells with therapeutic functions and discuss the advantages that they offer over conventional therapies in terms of flexibility, specificity and predictability, as well as challenges for their development. We present notable advances in the creation of engineered cells that harbour synthetic gene circuits capable of biological sensing and computation of signals derived from intracellular or extracellular biomarkers. We categorize and describe these developments based on the cell scaffold (human or microbial) and the site at which the engineered cell exerts its therapeutic function within its human host. The design of cell-based therapeutics with synthetic biology is a rapidly growing strategy in medicine that holds great promise for the development of effective treatments for a wide variety of human diseases.

The implementation of the good epidemiologic practices (GEP) guidelines is reviewed and raises many questions. Will the introduction of GEP, and the associated increase in the necessary quality assurance, quality control, documentation, archiving requirements, and other additional imposts increase the quality of occupational epidemiology? Will the added requirements subtract from the already diminished study dollars? Guidelines that are well intended to help the study process frequently become rules that must be followed. Many problems arise from the implementation of GEPs. For example, release of data sets for public use frequently impinges on the individuals' rights to privacy. These infringements could diminish the pool of participants. Release of data without personal descriptors may not be sufficient to hide the individuals' identities, while it may eliminate the value of the information. Data sharing, access, and archiving requirements present concerns. How will the data be stored, and where? Storage facilities and the storage medium must be sufficient to preserve the integrity of the information. Should studies be low-power, using smaller subject numbers, or high-power, using large cohorts? Rigid adherence to guidelines can diminish the quality of the study, but departure from the guidelines can deny its quality. Guidelines should not, in other words, be transformed into a rigid, irrevocable set of rules, as this can stifle investigations and valid inquiry. Time and usage will decide the benefits or disadvantages of the GEP. (Consumer Summary produced by Reliance Medical Information, Inc.)

The awareness of the effects of stress on school personnel and its effects on student achievement led to the formation of a committee to examine the problem and establish some objectives. A copy of the questionnaire submitted to the personnel of the Worchester, Massachusetts, Public Schools is presented. Job related factors were listed with responses to be checked on a scale ranging from no stress to high stress. Results of the survey appear in tabular form, giving the reactions of teachers, administrators, traveling teachers, aides, and clerical workers. Stress factors consistently rated as highest were: involuntary transfer, reduction in force, discipline, and salary. Comparison is made between this survey and similar surveys conducted in other school systems. Some actions being considered for the reduction of stress are discussed. (JD)

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.

Materials that sense and respond to biological signals in their environment have a broad range of potential applications in drug delivery, medical devices and diagnostics. Nucleic acids are important biological cues that encode information about organismal identity and clinically relevant phenotypes such as drug resistance. We recently developed a strategy to design nucleic acid–responsive materials using the CRISPR-associated nuclease Cas12a as a user-programmable sensor and material actuator. This approach improves on the sensitivity of current DNA-responsive materials while enabling their rapid repurposing toward new sequence targets. Here, we provide a comprehensive resource for the design, synthesis and actuation of CRISPR-responsive hydrogels. First, we provide guidelines for the synthesis of Cas12a guide RNAs (gRNAs) for in vitro applications. We then outline methods for the synthesis of both polyethylene glycol-DNA (PEG-DNA) and polyacrylamide-DNA (PA-DNA) hydrogels, as well as their controlled degradation using Cas12a for the release of cargos, including small molecules, enzymes, nanoparticles and living cells within hours. Finally, we detail the design and assembly of microfluidic paper-based devices that use Cas12a-sensitive hydrogels to convert DNA inputs into a variety of visual and electronic readouts for use in diagnostics. Following the initial validation of the gRNA and Cas12a components (1 d), the synthesis and testing of either PEG-DNA or PA-DNA hydrogels require 3–4 d of laboratory time. Optional extensions, including the release of primary human cells or the design of the paper-based diagnostic, require an additional 2–3 d each.

The quality of epidemiologic study design has rarely been considered as a separate topic in the medical literature. While some studies critique particular components of the design, the methods used in the treatment process, the manner of reporting results, or the data analysis process, there has been a notable lack of review of the total process, including sponsorship of the studies, who conducts the studies, and how the data is amassed, reviewed, and analyzed. Epidemiologic studies and analyses are gaining greater stature and playing more important roles as the foundations of health policy. Recent federal legislation governing environmental matters, issues, toxins, testing, etc., has required epidemiologic studies to maintain good laboratory practices (GLPs). The inclusion of GLPs as a criterion for the acceptance of toxicological studies was feared by the toxicologists as an unnecessary restraint, but these fears proved groundless. These requirements contributed to quality reporting conducted in a manner permitting comparative analyses. The epidemiologic study was the subject of a conference at which the introduction of good epidemiologic practices (GEPs) was proposed. These GEPs are intended to establish minimum, but not sufficient, practices and procedures. They provide for the quality and integrity of data produced, the resultant analyses, and the outcome decisions. The recommended format of epidemiologic study, consistent with the GEP, is outlined and briefly discussed. (Consumer Summary produced by Reliance Medical Information, Inc.)

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Joint profiling of transcriptome and chromatin accessibility within single cells allows for the deconstruction of the complex relationship between transcriptional states and upstream regulatory programs determining different cell fates. Here, we developed an automated method with high sensitivity, assay for single-cell transcriptome and accessibility regions (ASTAR-seq), for simultaneous measurement of whole-cell transcriptome and chromatin accessibility within the same single cell. To show the utility of ASTAR-seq, we profiled 384 mESCs under naive and primed pluripotent states as well as a two-cell like state, 424 human cells of various lineage origins (BJ, K562, JK1, and Jurkat), and 480 primary cord blood cells undergoing erythroblast differentiation. With the joint profiles, we configured the transcriptional and chromatin accessibility landscapes of discrete cell states, uncovered linked sets of cis-regulatory elements and target genes unique to each state, and constructed interactome and transcription factor (TF)–centered upstream regulatory networks for various cell states.

Efforts to construct synthetic biological circuits with more complex functions have often been hindered by the idiosyncratic behavior, limited dynamic range and crosstalk of commonly utilized parts. Here, we employ de novo RNA design to develop two high-performance translational repressors with sensing and logic capabilities. These synthetic riboregulators, termed toehold repressors and three-way junction (3WJ) repressors, detect transcripts with nearly arbitrary sequences, repress gene expression by up to 300-fold and yield orthogonal sets of up to 15 devices. Automated forward engineering is used to improve toehold repressor dynamic range and SHAPE-Seq is applied to confirm the designed switching mechanism of 3WJ repressors in living cells. We integrate the modular repressors into biological circuits that execute universal NAND and NOR logic and evaluate the four-input expression NOT ((A1 AND A2) OR (B1 AND B2)) in Escherichia coli. These capabilities make toehold and 3WJ repressors valuable new tools for biotechnological applications.

Increasing concerns about the rising rates of antibiotic therapy failure and advances in single-cell analyses have inspired a surge of research into antibiotic persistence. Bacterial persister cells represent a subpopulation of cells that can survive intensive antibiotic treatment without being resistant. Several approaches have emerged to define and measure persistence, and it is now time to agree on the basic definition of persistence and its relation to the other mechanisms by which bacteria survive exposure to bactericidal antibiotic treatments, such as antibiotic resistance, heteroresistance or tolerance. In this Consensus Statement, we provide definitions of persistence phenomena, distinguish between triggered and spontaneous persistence and provide a guide to measuring persistence. Antibiotic persistence is not only an interesting example of non-genetic single-cell heterogeneity, it may also have a role in the failure of antibiotic treatments. Therefore, it is our hope that the guidelines outlined in this article will pave the way for better characterization of antibiotic persistence and for understanding its relevance to clinical outcomes.

Antibiotic screens typically rely on growth inhibition to characterize compound bioactivity—an approach that cannot be used to assess the bactericidal activity of antibiotics against bacteria in drug-tolerant states. To address this limitation, we developed a multiplexed assay that uses metabolism-sensitive staining to report on the killing of antibiotic-tolerant bacteria. This method can be used with diverse bacterial species and applied to genome-scale investigations to identify therapeutic targets against tolerant pathogens.

Designer microbial consortia are an emerging frontier in synthetic biology that enable versatile microbiome engineering. However, the utilization of such consortia is hindered by our limited capacity in rapidly creating ecosystems with desired dynamics. Here we present the development of synthetic communities through social interaction engineering that combines modular pathway reconfiguration with model creation. Specifically, we created six two-strain consortia, each possessing a unique mode of interaction, including commensalism, amensalism, neutralism, cooperation, competition and predation. These consortia follow distinct population dynamics with characteristics determined by the underlying interaction modes. We showed that models derived from two-strain consortia can be used to design three- and four-strain ecosystems with predictable behaviors and further extended to provide insights into community dynamics in space. This work sheds light on the organization of interacting microbial species and provides a systematic framework—social interaction programming—to guide the development of synthetic ecosystems for diverse purposes.

Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate.

Induced pluripotent stem cells (iPSCs), which are used to produce transplantable tissues, may particularly benefit older patients, who are more likely to suffer from degenerative diseases. However, iPSCs generated from aged donors (A-iPSCs) exhibit higher genomic instability, defects in apoptosis and a blunted DNA damage response compared with iPSCs generated from younger donors. We demonstrated that A-iPSCs exhibit excessive glutathione-mediated reactive oxygen species (ROS) scavenging activity, which blocks the DNA damage response and apoptosis and permits survival of cells with genomic instability. We found that the pluripotency factor ZSCAN10 is poorly expressed in A-iPSCs and addition of ZSCAN10 to the four Yamanaka factors (OCT4, SOX2, KLF4 and c-MYC) during A-iPSC reprogramming normalizes ROS–glutathione homeostasis and the DNA damage response, and recovers genomic stability. Correcting the genomic instability of A-iPSCs will ultimately enhance our ability to produce histocompatible functional tissues from older patients’ own cells that are safe for transplantation.

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our ‘ribocomputing’ systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.

Poor prognosis in neuroblastoma is associated with genetic amplification of MYCN. MYCN is itself a target of let-7, a tumour suppressor family of microRNAs implicated in numerous cancers. LIN28B, an inhibitor of let-7 biogenesis, is overexpressed in neuroblastoma and has been reported to regulate MYCN. Here we show, however, that LIN28B is dispensable in MYCN-amplified neuroblastoma cell lines, despite de-repression of let-7. We further demonstrate that MYCN messenger RNA levels in amplified disease are exceptionally high and sufficient to sponge let-7, which reconciles the dispensability of LIN28B. We found that genetic loss of let-7 is common in neuroblastoma, inversely associated with MYCN amplification, and independently associated with poor outcomes, providing a rationale for chromosomal loss patterns in neuroblastoma. We propose that let-7 disruption by LIN28B, MYCN sponging, or genetic loss is a unifying mechanism of neuroblastoma development with broad implications for cancer pathogenesis.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

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The RNA-binding proteins LIN28A and LIN28B play critical roles in embryonic development, tumorigenesis, and pluripotency, but their exact functions are poorly understood. Here, we show that, like LIN28A, LIN28B can function effectively with NANOG, OCT4, and SOX2 in reprogramming to pluripotency and that reactivation of both endogenous LIN28A and LIN28B loci are required for maximal reprogramming efficiency. In human fibroblasts, LIN28B is activated early during reprogramming, while LIN28A is activated later during the transition to bona fide induced pluripotent stem cells (iPSCs). In murine cells, LIN28A and LIN28B facilitate conversion from naive to primed pluripotency. Proteomic and metabolomic analysis highlighted roles for LIN28 in maintaining the low mitochondrial function associated with primed pluripotency and in regulating one-carbon metabolism, nucleotide metabolism, and histone methylation. LIN28 binds to mRNAs of proteins important for oxidative phosphorylation and modulates protein abundance. Thus, LIN28A and LIN28B play cooperative roles in regulating reprogramming, naive/primed pluripotency, and stem cell metabolism.

Several programmable transcription factors exist based on the versatile Cas9 protein, yet their relative potency and effectiveness across various cell types and species remain unexplored. Here, we compare Cas9 activator systems and examine their ability to induce robust gene expression in several human, mouse, and fly cell lines. We also explore the potential for improved activation through the combination of the most potent activator systems, and we assess the role of cooperativity in maximizing gene expression.

Incomplete knowledge of the mechanisms at work continues to hamper efforts to maximize reprogramming efficiency. Here, we present a systematic genome-wide RNAi screen to determine the global regulators during the early stages of human reprogramming. Our screen identifies functional repressors and effectors that act to impede or promote the reprogramming process. Repressors and effectors form close interacting networks in pathways, including RNA processing, G protein signaling, protein ubiquitination, and chromatin modification. Combinatorial knockdown of five repressors (SMAD3, ZMYM2, SFRS11, SAE1, and ESET) synergistically resulted in ∼85% TRA-1-60-positive cells. Removal of the novel splicing factor SFRS11 during reprogramming is accompanied by rapid acquisition of pluripotency-specific spliced forms. Mechanistically, SFRS11 regulates exon skipping and mutually exclusive splicing of transcripts in genes involved in cell differentiation, mRNA splicing, and chromatin modification. Our study provides insights into the reprogramming process, which comprises comprehensive and multi-layered transcriptional, splicing, and epigenetic machineries.

Antibiotic resistance is an increasingly serious public health threat. Understanding pathways allowing bacteria to survive antibiotic stress may unveil new therapeutic targets. We explore the role of the bacterial epigenome in antibiotic stress survival using classical genetic tools and single-molecule real-time sequencing to characterize genomic methylation kinetics. We find that Escherichia coli survival under antibiotic pressure is severely compromised without adenine methylation at GATC sites. Although the adenine methylome remains stable during drug stress, without GATC methylation, methyl-dependent mismatch repair (MMR) is deleterious and, fueled by the drug-induced error-prone polymerase Pol IV, overwhelms cells with toxic DNA breaks. In multiple E. coli strains, including pathogenic and drug-resistant clinical isolates, DNA adenine methyltransferase deficiency potentiates antibiotics from the β-lactam and quinolone classes. This work indicates that the GATC methylome provides structural support for bacterial survival during antibiotic stress and suggests targeting bacterial DNA methylation as a viable approach to enhancing antibiotic activity.

Biocontainment systems that couple environmental sensing with circuit-based control of cell viability could be used to prevent escape of genetically modified microbes into the environment. Here we present two engineered safeguard systems known as the 'Deadman' and 'Passcode' kill switches. The Deadman kill switch uses unbalanced reciprocal transcriptional repression to couple a specific input signal with cell survival. The Passcode kill switch uses a similar two-layered transcription design and incorporates hybrid LacI-GalR family transcription factors to provide diverse and complex environmental inputs to control circuit function. These synthetic gene circuits efficiently kill Escherichia coli and can be readily reprogrammed to change their environmental inputs, regulatory architecture and killing mechanism.

Understanding how antibiotics impact bacterial metabolism may provide insight into their mechanisms of action and could lead to enhanced therapeutic methodologies. Here, we profiled the metabolome of Escherichia coliafter treatment with three different classes of bactericidal antibiotics (β-lactams, aminoglycosides, quinolones). These treatments induced a similar set of metabolic changes after 30 min that then diverged into more distinct profiles at later time points. The most striking changes corresponded to elevated concentrations of central carbon metabolites, active breakdown of the nucleotide pool, reduced lipid levels, and evidence of an elevated redox state. We examined potential end-target consequences of these metabolic perturbations and found that antibiotic-treated cells exhibited cytotoxic changes indicative of oxidative stress, including higher levels of protein carbonylation, malondialdehyde adducts, nucleotide oxidation, and double-strand DNA breaks. This work shows that bactericidal antibiotics induce a complex set of metabolic changes that are correlated with the buildup of toxic metabolic by-products.

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Alternative RNA splicing (AS) regulates proteome diversity, including isoform-specific expression of several pluripotency genes. Here, we integrated global gene expression and proteomic analyses and identified a molecular signature suggesting a central role for AS in maintaining human pluripotent stem cell (hPSC) self-renewal. We demonstrate that the splicing factor SFRS2 is an OCT4 target gene required for pluripotency. SFRS2 regulates AS of the methyl-CpG binding protein MBD2, whose isoforms play opposing roles in maintenance of and reprogramming to pluripotency. Although both MDB2a and MBD2c are enriched at the OCT4and NANOGpromoters, MBD2a preferentially interacts with repressive NuRD chromatin remodeling factors and promotes hPSC differentiation, whereas overexpression of MBD2c enhances reprogramming of fibroblasts to pluripotency. The miR-301 and miR-302 families provide additional regulation by targeting SFRS2and MDB2a. These data suggest that OCT4, SFRS2, and MBD2 participate in a positive feedback loop, regulating proteome diversity in support of hPSC self-renewal and reprogramming.