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Regulatory networks allow organisms to match adaptive behavior to the complex and dynamic contingencies of their native habitats. Upon a sudden transition to a novel environment, the mismatch between the native behavior and the new niche provides selective

Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections 1 . However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell 2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference 3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease 4 , and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections., (© 2024. The Author(s).)

Despite the critical importance of essential genes, systems-level investigations of their contribution to antibiotic sensitivity have been limited. Using CRISPR Adaptation-mediated Library Manufacturing (CALM), we generated ultra-dense CRISPR interference (CRISPRi) libraries in methicillin-sensitive and -resistant strains of Staphylococcus aureus, which allowed us to quantify gene fitness on a global scale across ten clinically relevant antibiotics. This led to the identification of a comprehensive set of known and novel biological processes modulating bacterial fitness in the antibiotics. Notably, we found that essential genes from diverse processes dominated antibiotic-gene interactions, including a large number of synergistic interactions between bactericidal antibiotics and processes such as cell wall synthesis/cell division (CC), DNA replication/DNA recombination (DD), protein export, and coenzyme A biosynthesis. Simultaneous genetic perturbations of diverse CC and DD processes aggravated bacterial fitness, revealing a widespread synergy between the two highly coordinated processes. In contrast, perturbation of transcriptional, translational, and select energy processes antagonized the effects of bactericidal antibiotics. Finally, we show that small molecule inhibitors recapitulated synergistic antibiotic-gene interactions, providing a rational foundation for developing novel combinatorial antimicrobial therapies.

Recent research has indicated the presence of heterochromatin-like regions of extended protein occupancy and transcriptional silencing of bacterial genomes. We utilized an integrative approach to track chromatin structure and transcription in E. coli K-12 across a wide range of nutrient conditions. In the process, we identified multiple loci which act similarly to facultative heterochromatin in eukaryotes, normally silenced but permitting expression of genes under specific conditions. We also found a strong enrichment of small regulatory RNAs (sRNAs) among the set of differentially expressed transcripts during nutrient stress. Using a newly developed bioinformatic pipeline, the transcription factors regulating sRNA expression were bioinformatically predicted, with experimental follow-up revealing novel relationships for 36 sRNA-transcription factors candidates. Direct regulation of sRNA expression was confirmed by mutational analysis for five sRNAs of metabolic interest: IsrB, CsrB and CsrC, GcvB, and GadY. Our integrative analysis thus reveals additional layers of complexity in the nutrient stress response in E. coli and provides a framework for revealing similar poorly understood regulatory logic in other organisms., Competing Interests: CONFLICT OF INTEREST The authors declare no conflicts of interest.

Competing Interests: Conflict-of-Interest Disclosures: Amy Abernethy reports personal fees from Verily/Alphabet, relationships with Georgiamune and EQRx, and personal investments in Iterative Health and One Health, outside the submitted work. Elliot Chaikof reports grants from the National Institutes of Health, outside the submitted work. George Q. Daley reports holding equity from Redona Therapeutics and from iTCells, outside the submitted work. Juan Enriquez reports investments with Excel Venture Management, outside the submitted work; investments in various life science technologies, including leading-edge brain technologies, and co-authoring a book on the impact of emerging brain technologies. Scott Gottlieb reports personal fees from Pfi zer, Inc, Illumina, Inc, Aetion, Tempus Labs, National Resilience, Inc, Cell-Carta, Parker Institute for Cancer Immunotherapy, Mount Sinai Health System, New Enterprise Associates, and American Enterprise Institute outside the submitted work. Sohail Tavazoie reports personal fees from Inspirna, outside the submitted work. Jacob S. Sherkow reports employment with the University of Illinois, grants from National Institutes of Health, and personal fees from Expert Consulting services, outside the submitted work.

Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis -regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

Patients with primary mitochondrial oxidative phosphorylation (OxPhos) defects present with fatigue and multi-system disorders, are often lean, and die prematurely, but the mechanistic basis for this clinical picture remains unclear. By integrating data from 17 cohorts of patients with mitochondrial diseases (n = 690) we find evidence that these disorders increase resting energy expenditure, a state termed hypermetabolism. We examine this phenomenon longitudinally in patient-derived fibroblasts from multiple donors. Genetically or pharmacologically disrupting OxPhos approximately doubles cellular energy expenditure. This cell-autonomous state of hypermetabolism occurs despite near-normal OxPhos coupling efficiency, excluding uncoupling as a general mechanism. Instead, hypermetabolism is associated with mitochondrial DNA instability, activation of the integrated stress response (ISR), and increased extracellular secretion of age-related cytokines and metabokines including GDF15. In parallel, OxPhos defects accelerate telomere erosion and epigenetic aging per cell division, consistent with evidence that excess energy expenditure accelerates biological aging. To explore potential mechanisms for these effects, we generate a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations. Taken together, these findings highlight the need to understand how OxPhos defects influence the energetic cost of living, and the link between hypermetabolism and aging in cells and patients with mitochondrial diseases., (© 2023. The Author(s).)

Utilization of specific codons varies between organisms. Cancer represents a model for understanding DNA sequence evolution and could reveal causal factors underlying codon evolution. We found that across human cancer, arginine codons are frequently mutated to other codons. Moreover, arginine limitation-a feature of tumor microenvironments-is sufficient to induce arginine codon-switching mutations in human colon cancer cells. Such DNA codon switching events encode mutant proteins with arginine residue substitutions. Mechanistically, arginine limitation caused rapid reduction of arginine transfer RNAs and the stalling of ribosomes over arginine codons. Such selective pressure against arginine codon translation induced an adaptive proteomic shift toward low-arginine codon-containing genes, including specific amino acid transporters, and caused mutational evolution away from arginine codons-reducing translational bottlenecks that occurred during arginine starvation. Thus, environmental availability of a specific amino acid can influence DNA sequence evolution away from its cognate codons and generate altered proteins.

Thymidine starvation causes rapid cell death. This enigmatic process known as thymineless death (TLD) is the underlying killing mechanism of diverse antimicrobial and antineoplastic drugs. Despite decades of investigation, we still lack a mechanistic understanding of the causal sequence of events that culminate in TLD. Here, we used a diverse set of unbiased approaches to systematically determine the genetic and regulatory underpinnings of TLD in Escherichia coli. In addition to discovering novel genes in previously implicated pathways, our studies revealed a critical and previously unknown role for intracellular acidification in TLD. We observed that a decrease in cytoplasmic pH is a robust early event in TLD across different genetic backgrounds. Furthermore, we show that acidification is a causal event in the death process, as chemical and genetic perturbations that increase intracellular pH substantially reduce killing. We also observe a decrease in intracellular pH in response to exposure to the antibiotic gentamicin, suggesting that intracellular acidification may be a common mechanistic step in the bactericidal effects of other antibiotics., Competing Interests: The authors have declared that no competing interests exist.

Metastatic colonization is the primary cause of death from colorectal cancer (CRC). We employed genome-scale in vivo short hairpin RNA (shRNA) screening and validation to identify 26 promoters of CRC liver colonization. Among these genes, we identified a cluster that contains multiple targetable genes, including ITPR3, which promoted liver-metastatic colonization and elicited similar downstream gene expression programs. ITPR3 is a caffeine-sensitive inositol 1,4,5-triphosphate (IP3) receptor that releases calcium from the endoplasmic reticulum and enhanced metastatic colonization by inducing expression of RELB, a transcription factor that is associated with non-canonical NF-κB signaling. Genetic, cell biological, pharmacologic, and clinical association studies revealed that ITPR3 and RELB drive CRC colony formation by promoting cell survival upon substratum detachment or hypoxic exposure. RELB was sufficient to drive colonization downstream of ITPR3. Our findings implicate the ITPR3/calcium/RELB axis in CRC metastatic colony formation and uncover multiple clinico-pathologically associated targetable proteins as drivers of CRC metastatic colonization., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

We have produced gene expression profiles of all 302 neurons of the C. elegans nervous system that match the single-cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses distinct codes of ∼23 neuropeptide genes and ∼36 neuropeptide receptors, delineating a complex and expansive "wireless" signaling network. To demonstrate the utility of this comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression and (2) reveal adhesion proteins with potential roles in process placement and synaptic specificity. Our expression data are available at https://cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity, and function throughout the C. elegans nervous system., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display-high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein-DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation., Competing Interests: The authors have declared that no competing interests exist.

Genome-scale CRISPR interference (CRISPRi) is widely utilized to study cellular processes in a variety of organisms. Despite the dominance of Saccharomyces cerevisiae as a model eukaryote, an inducible genome-wide CRISPRi library in yeast has not yet been presented. Here, we present a genome-wide, inducible CRISPRi library, based on spacer design rules optimized for S. cerevisiae. We have validated this library for genome-wide interrogation of gene function across a variety of applications, including accurate discovery of haploinsufficient genes and identification of enzymatic and regulatory genes involved in adenine and arginine biosynthesis. The comprehensive nature of the library also revealed refined spacer design parameters for transcriptional repression, including location, nucleosome occupancy and nucleotide features. CRISPRi screens using this library can identify genes and pathways with high precision and a low false discovery rate across a variety of experimental conditions, enabling rapid and reliable assessment of genetic function and interactions in S. cerevisiae.

The yeast Saccharomyces cerevisiae has been the subject of many studies aimed at understanding mechanisms of adaptation to environmental stresses. Most of these studies have focused on adaptation to sub-lethal stresses, upon which a stereotypic transcriptional program called the environmental stress response (ESR) is activated. However, the genetic and regulatory factors that underlie the adaptation and survival of yeast cells to stresses that cross the lethality threshold have not been systematically studied. Here, we utilized a combination of gene expression profiling, deletion-library fitness profiling, and experimental evolution to systematically explore adaptation of S. cerevisiae to acute exposure to threshold lethal ethanol concentrations-a stress with important biotechnological implications. We found that yeast cells activate a rapid transcriptional reprogramming process that is likely adaptive in terms of post-stress survival. We also utilized repeated cycles of lethal ethanol exposure to evolve yeast strains with substantially higher ethanol tolerance and survival. Importantly, these strains displayed bulk growth-rates that were indistinguishable from the parental wild-type strain. Remarkably, these hyper-ethanol tolerant strains had reprogrammed their pre-stress gene expression states to match the likely adaptive post-stress response in the wild-type strain. Our studies reveal critical determinants of yeast survival to lethal ethanol stress and highlight potentially general principles that may underlie evolutionary adaptation to lethal stresses in general., Competing Interests: The authors have declared that no competing interests exist.

Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems 1-13 , the application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance 14-16 , lack of mRNA polyadenylation and thick cell walls 17 . Here, we present prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)-a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing 11,12,18 to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single-cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates of more than 200 mRNAs per cell for exponentially growing Escherichia coli. These characteristics enable robust discrimination of cell states corresponding to different phases of growth. When applied to wild-type Staphylococcus aureus, PETRI-seq revealed a rare subpopulation of cells undergoing prophage induction. We anticipate that PETRI-seq will have broad utility in defining single-cell states and their dynamics in complex microbial communities.

Genome-wide CRISPR screens enable systematic interrogation of gene function. However, guide RNA libraries are costly to synthesize, and their limited diversity compromises the sensitivity of CRISPR screens. Using the Streptococcus pyogenes CRISPR-Cas adaptation machinery, we developed CRISPR adaptation-mediated library manufacturing (CALM), which turns bacterial cells into "factories" for generating hundreds of thousands of crRNAs covering 95% of all targetable genomic sites. With an average gene targeted by more than 100 distinct crRNAs, these highly comprehensive CRISPRi libraries produced varying degrees of transcriptional repression critical for uncovering novel antibiotic resistance determinants. Furthermore, by iterating CRISPR adaptation, we rapidly generated dual-crRNA libraries representing more than 100,000 dual-gene perturbations. The polarized nature of spacer adaptation revealed the historical contingency in the stepwise acquisition of genetic perturbations leading to increasing antibiotic resistance. CALM circumvents the expense, labor, and time required for synthesis and cloning of gRNAs, allowing generation of CRISPRi libraries in wild-type bacteria refractory to routine genetic manipulation., Competing Interests: Declaration of Interests We declare the following provisional patent related to the manuscript: “Generation of Genome-wide CRISPR RNA Libraries Using CRISPR Adaptation in Bacteria.”, (Copyright © 2020 Elsevier Inc. All rights reserved.)

Antibiotic persistence, the noninherited tolerance of a subpopulation of bacteria to high levels of antibiotics, is a bet-hedging phenomenon with broad clinical implications. Indeed, the isolation of bacteria with substantially increased persistence rates from chronic infections suggests that evolution of hyperpersistence is a significant factor in clinical therapy resistance. However, the pathways that lead to hyperpersistence and the underlying cellular states have yet to be systematically studied. Here, we show that laboratory evolution can lead to increase in persistence rates by orders of magnitude for multiple independently evolved populations of Escherichia coli and that the driving mutations are highly enriched in translation-related genes. Furthermore, two distinct adaptive mutations converge on concordant transcriptional changes, including increased population heterogeneity in the expression of several genes. Cells with extreme expression of these genes showed dramatic differences in persistence rates, enabling isolation of subpopulations in which a substantial fraction of cells are persisters. Expression analysis reveals coherent regulation of specific pathways that may be critical to establishing the hyperpersistence state. Hyperpersister mutants can thus enable the systematic molecular characterization of this unique physiological state, a critical prerequisite for developing antipersistence strategies. IMPORTANCE Bacterial persistence is a fascinating phenomenon in which a small subpopulation of bacteria becomes phenotypically tolerant to lethal antibiotic exposure. There is growing evidence that populations of bacteria in chronic clinical infections develop a hyperpersistent phenotype, enabling a substantially larger subpopulation to survive repeated antibiotic treatment. The mechanisms of persistence and modes of increasing persistence rates remain largely unknown. Here, we utilized experimental evolution to select for Escherichia coli mutants that have more than a thousandfold increase in persistence rates. We discovered that a variety of individual mutations to translation-related processes are causally involved. Furthermore, we found that these mutations lead to population heterogeneity in the expression of specific genes. We show that this can be used to isolate populations in which the majority of bacteria are persisters, thereby enabling systems-level characterization of this fascinating and clinically significant microbial phenomenon., (Copyright © 2020 Khare and Tavazoie.)

Posttranscriptional regulation plays a crucial role in shaping gene expression. During the maternal-to-zygotic transition (MZT), thousands of maternal transcripts are regulated. However, how different cis -elements and trans -factors are integrated to determine mRNA stability remains poorly understood. Here, we show that most transcripts are under combinatorial regulation by multiple decay pathways during zebrafish MZT. By using a massively parallel reporter assay, we identified cis -regulatory sequences in the 3' UTR, including U-rich motifs that are associated with increased mRNA stability. In contrast, miR-430 target sequences, UAUUUAUU AU-rich elements (ARE), CCUC, and CUGC elements emerged as destabilizing motifs, with miR-430 and AREs causing mRNA deadenylation upon genome activation. We identified trans -factors by profiling RNA-protein interactions and found that poly(U)-binding proteins are preferentially associated with 3' UTR sequences and stabilizing motifs. We show that this activity is antagonized by C-rich motifs and correlated with protein binding. Finally, we integrated these regulatory motifs into a machine learning model that predicts reporter mRNA stability in vivo., (© 2019 Vejnar et al.; Published by Cold Spring Harbor Laboratory Press.)

Proneural genes are among the most early-acting genes in nervous system development, instructing blast cells to commit to a neuronal fate. Drosophila Atonal and Achaete-Scute complex (AS-C) genes, as well as their vertebrate orthologs, are basic helix-loop-helix (bHLH) transcription factors with such proneural activity. We show here that a C. elegans AS-C homolog, hlh-4, functions in a fundamentally different manner. In the embryonic, larval, and adult nervous systems, hlh-4 is expressed exclusively in a single nociceptive neuron class, ADL, and its expression in ADL is maintained via transcriptional autoregulation throughout the life of the animal. However, in hlh-4 null mutants, the ADL neuron is generated and still appears neuronal in overall morphology and expression of panneuronal and pansensory features. Rather than acting as a proneural gene, we find that hlh-4 is required for the ADL neuron to function properly, to adopt its correct morphology, to express its unusually large repertoire of olfactory receptor-encoding genes, and to express other known features of terminal ADL identity, including neurotransmitter phenotype, neuropeptides, ion channels, and electrical synapse proteins. hlh-4 is sufficient to induce ADL identity features upon ectopic expression in other neuron types. The expression of ADL terminal identity features is directly controlled by HLH-4 via a phylogenetically conserved E-box motif, which, through bioinformatic analysis, we find to constitute a predictive feature of ADL-expressed terminal identity markers. The lineage that produces the ADL neuron was previously shown to require the conventional, transient proneural activity of another AS-C homolog, hlh-14, demonstrating sequential activities of distinct AS-C-type bHLH genes in neuronal specification. Taken together, we have defined here an unconventional function of an AS-C-type bHLH gene as a terminal selector of neuronal identity and we speculate that such function could be reflective of an ancestral function of an "ur-" bHLH gene.

Cells adapt to familiar changes in their environment by activating predefined regulatory programs that establish adaptive gene expression states. These hard-wired pathways, however, may be inadequate for adaptation to environments never encountered before. Here, we reveal evidence for an alternative mode of gene regulation that enables adaptation to adverse conditions without relying on external sensory information or genetically predetermined cis -regulation. Instead, individual genes achieve optimal expression levels through a stochastic search for improved fitness. By focusing on improving the overall health of the cell, the proposed stochastic tuning mechanism discovers global gene expression states that are fundamentally new and yet optimized for novel environments. We provide experimental evidence for stochastic tuning in the adaptation of Saccharomyces cerevisiae to laboratory-engineered environments that are foreign to its native gene-regulatory network. Stochastic tuning operates locally at individual gene promoters, and its efficacy is modulated by perturbations to chromatin modification machinery., Competing Interests: PF, JY, AM, ST No competing interests declared, (© 2018, Freddolino et al.)

Microorganisms exist almost exclusively in interactive multispecies communities, but genetic determinants of the fitness of interacting bacteria, and accessible adaptive pathways, remain uncharacterized. Here, using a two-species system, we studied the antagonism of Pseudomonas aeruginosa against Escherichia coli. Our unbiased genome-scale approach enabled us to identify multiple factors that explained the entire antagonism observed. We discovered both forms of ecological competition-sequestration of iron led to exploitative competition, while phenazine exposure engendered interference competition. We used laboratory evolution to discover adaptive evolutionary trajectories in our system. In the presence of P. aeruginosa toxins, E. coli populations showed parallel molecular evolution and adaptive convergence at the gene-level. The multiple resistance pathways discovered provide novel insights into mechanisms of toxin entry and activity. Our study reveals the molecular complexity of a simple two-species interaction, an important first-step in the application of systems biology to detailed molecular dissection of interactions within native microbiomes.

N(6)-methyladenosine (m(6)A) is the most abundant internal modification of messenger RNA. While the presence of m(6)A on transcripts can impact nuclear RNA fates, a reader of this mark that mediates processing of nuclear transcripts has not been identified. We find that the RNA-binding protein HNRNPA2B1 binds m(6)A-bearing RNAs in vivo and in vitro and its biochemical footprint matches the m(6)A consensus motif. HNRNPA2B1 directly binds a set of nuclear transcripts and elicits similar alternative splicing effects as the m(6)A writer METTL3. Moreover, HNRNPA2B1 binds to m(6)A marks in a subset of primary miRNA transcripts, interacts with the microRNA Microprocessor complex protein DGCR8, and promotes primary miRNA processing. Also, HNRNPA2B1 loss and METTL3 depletion cause similar processing defects for these pri-miRNA precursors. We propose HNRNPA2B1 to be a nuclear reader of the m(6)A mark and to mediate, in part, this mark's effects on primary microRNA processing and alternative splicing. PAPERCLIP., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Aberrant regulation of RNA stability has an important role in many disease states. Deregulated post-transcriptional modulation, such as that governed by microRNAs targeting linear sequence elements in messenger RNAs, has been implicated in the progression of many cancer types. A defining feature of RNA is its ability to fold into structures. However, the roles of structural mRNA elements in cancer progression remain unexplored. Here we performed an unbiased search for post-transcriptional modulators of mRNA stability in breast cancer by conducting whole-genome transcript stability measurements in poorly and highly metastatic isogenic human breast cancer lines. Using a computational framework that searches RNA sequence and structure space, we discovered a family of GC-rich structural cis-regulatory RNA elements, termed sRSEs for structural RNA stability elements, which are significantly overrepresented in transcripts displaying reduced stability in highly metastatic cells. By integrating computational and biochemical approaches, we identified TARBP2, a double-stranded RNA-binding protein implicated in microRNA processing, as the trans factor that binds the sRSE family and similar structural elements--collectively termed TARBP2-binding structural elements (TBSEs)--in transcripts. TARBP2 is overexpressed in metastatic cells and metastatic human breast tumours and destabilizes transcripts containing TBSEs. Endogenous TARBP2 promotes metastatic cell invasion and colonization by destabilizing amyloid precursor protein (APP) and ZNF395 transcripts, two genes previously associated with Alzheimer's and Huntington's disease, respectively. We reveal these genes to be novel metastasis suppressor genes in breast cancer. The cleavage product of APP, extracellular amyloid-α peptide, directly suppresses invasion while ZNF395 transcriptionally represses a pro-metastatic gene expression program. The expression levels of TARBP2, APP and ZNF395 in human breast carcinomas support their experimentally uncovered roles in metastasis. Our findings establish a non-canonical and direct role for TARBP2 in mammalian gene expression regulation and reveal that regulated RNA destabilization through protein-mediated binding of mRNA structural elements can govern cancer progression.

Posttranscriptional regulatory programs governing diverse aspects of RNA biology remain largely uncharacterized. Understanding the functional roles of RNA cis-regulatory elements is essential for decoding complex programs that underlie the dynamic regulation of transcript stability, splicing, localization, and translation. Here, we describe a combined experimental/computational technology to reveal a catalog of functional regulatory elements embedded in 3' UTRs of human transcripts. We used a bidirectional reporter system coupled with flow cytometry and high-throughput sequencing to measure the effect of short, noncoding, vertebrate-conserved RNA sequences on transcript stability and translation. Information-theoretic motif analysis of the resulting sequence-to-gene-expression mapping revealed linear and structural RNA cis-regulatory elements that positively and negatively modulate the posttranscriptional fates of human transcripts. This combined experimental/computational strategy can be used to systematically characterize the vast landscape of posttranscriptional regulatory elements controlling physiological and pathological cellular state transitions., (Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.)

Divergent phenotypes for distantly related strains of bacteria, such as differing antibiotic resistances or organic solvent tolerances, are of keen interest both from an evolutionary perspective and for the engineering of novel microbial organisms and consortia in synthetic biology applications. A prerequisite for any practical application of this phenotypic diversity is knowledge of the genetic determinants for each trait of interest. Sequence divergence between strains is often so extensive as to make brute-force approaches to identifying the loci contributing to a given trait impractical. Here we describe a global linkage analysis approach, GLINT, for rapid discovery of the causal genetic variants underlying phenotypic divergence between distantly related strains of Escherichia coli. This general strategy will also be usable, with minor modifications, for revealing genotype-phenotype associations between naturally occurring strains of other bacterial species.

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework.

Microglia are resident immune cells of the CNS that are activated by infection, neuronal injury, and inflammation. Here, we utilize flow cytometry and deep RNA sequencing of acutely isolated spinal cord microglia to define their activation in vivo. Analysis of resting microglia identified 29 genes that distinguish microglia from other CNS cells and peripheral macrophages/monocytes. We then analyzed molecular changes in microglia during neurodegenerative disease activation using the SOD1(G93A) mouse model of amyotrophic lateral sclerosis (ALS). We found that SOD1(G93A) microglia are not derived from infiltrating monocytes, and that both potentially neuroprotective and toxic factors, including Alzheimer's disease genes, are concurrently upregulated. Mutant microglia differed from SOD1(WT), lipopolysaccharide-activated microglia, and M1/M2 macrophages, defining an ALS-specific phenotype. Concurrent messenger RNA/fluorescence-activated cell sorting analysis revealed posttranscriptional regulation of microglia surface receptors and T cell-associated changes in the transcriptome. These results provide insights into microglia biology and establish a resource for future studies of neuroinflammation., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments., Competing Interests: The authors have declared that no competing interests exist.

Phenotypic heterogeneity displayed by a clonal bacterial population permits a small fraction of cells to survive prolonged exposure to antibiotics. Although first described over 60 y ago, the molecular mechanisms underlying this behavior, termed persistence, remain largely unknown. To systematically explore the genetic basis of persistence, we selected a library of transposon-mutagenized Escherichia coli cells for survival to multiple rounds of lethal ampicillin exposure. Application of microarray-based genetic footprinting revealed a large number of loci that drastically elevate persistence frequency through null mutations and domain disruptions. In one case, the C-terminal disruption of methionyl-tRNA synthetase (MetG) results in a 10,000-fold higher persistence frequency than wild type. We discovered a mechanism by which null mutations in transketolase A (tktA) and glycerol-3-phosphate (G3P) dehydrogenase (glpD) increase persistence through metabolic flux alterations that increase intracellular levels of the growth-inhibitory metabolite methylglyoxal. Systematic double-mutant analyses revealed the genetic network context in which such persistent mutants function. Our findings reveal a large mutational target size for increasing persistence frequency, which has fundamental implications for the emergence of antibiotic tolerance in the clinical setting.

To fulfill systems biology's promise of providing fundamental new insights will require the development of quantitative and predictive models of whole cells. In this issue, Karr et al. present the first integrated and dynamic computational model of a bacterium that accounts for all of its components and their interactions., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Regulatory networks allow organisms to match adaptive behavior to the complex and dynamic contingencies of their native habitats. Upon a sudden transition to a novel environment, the mismatch between the native behavior and the new niche provides selective pressure for adaptive evolution through mutations in elements that control gene expression. In the case of core components of cellular regulation and metabolism, with broad control over diverse biological processes, such mutations may have substantial pleiotropic consequences. Through extensive phenotypic analyses, we have characterized the systems-level consequences of one such mutation (rho*) in the global transcriptional terminator Rho of Escherichia coli. We find that a single amino acid change in Rho results in a massive change in the fitness landscape of the cell, with widely discrepant fitness consequences of identical single locus perturbations in rho* versus rho(WT) backgrounds. Our observations reveal the extent to which a single regulatory mutation can transform the entire fitness landscape of the cell, causing a massive change in the interpretation of individual mutations and altering the evolutionary trajectories which may be accessible to a bacterial population., Competing Interests: The authors have declared that no competing interests exist.

Decoding post-transcriptional regulatory programs in RNA is a critical step towards the larger goal of developing predictive dynamical models of cellular behaviour. Despite recent efforts, the vast landscape of RNA regulatory elements remains largely uncharacterized. A long-standing obstacle is the contribution of local RNA secondary structure to the definition of interaction partners in a variety of regulatory contexts, including--but not limited to--transcript stability, alternative splicing and localization. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (for example, human cardiac troponin T) or affects other aspects of RNA biology. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence. Here we present a computational framework based on context-free grammars and mutual information that systematically explores the immense space of small structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behaviour. By applying this framework to genome-wide human mRNA stability data, we reveal eight highly significant elements with substantial structural information, for the strongest of which we show a major role in global mRNA regulation. Through biochemistry, mass spectrometry and in vivo binding studies, we identified human HNRPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1, also known as HNRNPA2B1) as the key regulator that binds this element and stabilizes a large number of its target genes. We created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach could also be used to reveal the structural elements that modulate other aspects of RNA behaviour.

We have recently identified seven mutations in commonly used stocks of the sequenced Escherichia coli strain MG1655 which do not appear in the reference sequence. The mutations are likely to cause loss of function of the glpR and crl genes, which may have serious implications for physiological experiments using the affected strains.

Microbial regulatory strategies have long been understood in terms of the homeostatic framework, in which a response is interpreted as a restoring force counteracting the immediate intracellular consequences of a change in the environment. In this review, we summarize the breadth of recent discoveries of cellular behavior extending beyond the homeostatic framework. We argue that the nonrandom structure of native habitats makes environmental fluctuations inherently multidimensional. Beyond its utility for accurate perception of immediate events, the temporal regularity of this multidimensional correlation structure allows microbes to make predictions about the trajectory of their sensory environment. We describe recently discovered examples of such predictive behavior, their physiological benefits, and the underlying evolutionary forces shaping them. These observations compel us to go beyond homeostasis and consider a predictive-dynamic framework in which cellular behavior is orchestrated in response to the meaning of an environmental perturbation, not only its direct and immediate fitness consequences.

In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.

Obesity is characterized by an expansion of white adipose tissue mass that results from an increase in the size and the number of adipocytes. However, the mechanisms responsible for the formation of adipocytes during development and the molecular mechanisms regulating their increase and maintenance in adulthood are poorly understood. Here, we report the use of leptin-luciferase BAC transgenic mice to track white adipose tissue (WAT) development and guide the isolation and molecular characterization of adipocytes during development using DNA microarrays. These data reveal distinct transcriptional programs that are regulated during murine WAT development in vivo. By using a de novo cis-regulatory motif discovery tool (FIRE), we identify two early gene clusters whose promoters show significant enrichment for NRF2/ETS transcription factor binding sites. We further demonstrate that Ets transcription factors, but not Nrf2, are regulated during early adipogenesis and that Ets2 is essential for the normal progression of the adipocyte differentiation program in vitro. These data identify ETS2 as a functionally important transcription factor in adipogenesis and its possible role in regulating adipose tissue mass in adults can now be tested. Our approach also provides the basis for elucidating the function of other gene networks during WAT development in vivo. Finally these data confirm that although gene expression during adipogenesis in vitro recapitulates many of the patterns of gene expression in vivo, there are additional developmental transitions in pre and post-natal adipose tissue that are not evident in cell culture systems.

The emergence of pathogenic bacteria resistant to multiple antimicrobial agents is turning into a major crisis in human and veterinary medicine. This necessitates a serious re-evaluation of our approaches toward antibacterial drug discovery and use. Concurrent advances in genomics including whole-genome sequencing, genotyping, and gene expression profiling have the potential to transform our basic understanding of antimicrobial pathways and lead to the discovery of novel targets and therapeutics., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Bacteria in biofilms have higher antibiotic tolerance than their planktonic counterparts. A major outstanding question is the degree to which the biofilm-specific cellular state and its constituent genetic determinants contribute to this hyper-tolerant phenotype. Here, we used genome-wide functional profiling of a complex, heterogeneous mutant population of Pseudomonas aeruginosa MPAO1 in biofilm and planktonic growth conditions with and without tobramycin to systematically quantify the contribution of each locus to antibiotic tolerance under these two states. We identified large sets of mutations that contribute to antibiotic tolerance predominantly in the biofilm or planktonic setting only, offering global insights into the differences and similarities between biofilm and planktonic antibiotic tolerance. Our mixed population-based experimental design recapitulated the complexity of natural biofilms and, unlike previous studies, revealed clinically observed behaviors including the emergence of quorum sensing-deficient mutants. Our study revealed a substantial contribution of the cellular state to the antibiotic tolerance of biofilms, providing a rational foundation for the development of novel therapeutics against P. aeruginosa biofilm-associated infections.

Successful strain engineering involves perturbing key nodes within the cellular network. How the -network's connectivity affects the phenotype of interest and the ideal nodes to modulate, however, are frequently not readily apparent. To guide the generation of a list of candidate nodes for detailed investigation, designers often examine the behavior of a representative set of strains, such as a library of transposon insertion mutants, in the environment of interest. Here, we first present design principles for creating a maximally informative competitive selection. Then, we describe how to globally quantify the change in distribution of strains within a transposon library in response to a competitive selection by amplifying the DNA adjacent to the transposons and hybridizing it to a microarray. Finally, we detail strategies for analyzing the resulting hybridization data to identify genes and pathways that contribute both negatively and positively to fitness in the desired environment.

The increasing ability to generate large-scale, quantitative proteomic data has brought with it the challenge of analyzing such data to discover the sequence elements that underlie systems-level protein behavior. Here we show that short, linear protein motifs can be efficiently recovered from proteome-scale datasets such as sub-cellular localization, molecular function, half-life, and protein abundance data using an information theoretic approach. Using this approach, we have identified many known protein motifs, such as phosphorylation sites and localization signals, and discovered a large number of candidate elements. We estimate that ~80% of these are novel predictions in that they do not match a known motif in both sequence and biological context, suggesting that post-translational regulation of protein behavior is still largely unexplored. These predicted motifs, many of which display preferential association with specific biological pathways and non-random positioning in the linear protein sequence, provide focused hypotheses for experimental validation.