Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception

Here we consider the possibility that a fundamental 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 parsimonious computational framework. Beyond its utility as a potential model of cortical computation, artificial networks based on this principle have remarkable capacity for internalizing dynamical systems, making them useful in a variety of application domains including time-series prediction and machine intelligence

Global discovery of adaptive mutations.

Although modern DNA sequencing enables rapid identification of genetic variation, characterizing the phenotypic consequences of individual mutations remains a labor-intensive task. Here we describe array-based discovery of adaptive mutations (ADAM), a technology that searches an entire bacterial genome for mutations that contribute to selectable phenotypic variation between an evolved strain and its parent. We found that ADAM identified adaptive mutations in laboratory-evolved Escherichia coli strains with high sensitivity and specificity

Fast and systematic genome-wide discovery of conserved regulatory elements using a non-alignment based approach

We describe a powerful new approach for discovering globally conserved regulatory elements between two genomes. The method is fast, simple and comprehensive, without requiring alignments. Its application to pairs of yeasts, worms, flies and mammals yields a large number of known and novel putative regulatory elements. Many of these are validated by independent biological observations, have spatial and/or orientation biases, are co-conserved with other elements and show surprising conservation across large phylogenetic distances

Ab initio genotype–phenotype association reveals intrinsic modularity in genetic networks

Microbial species express an astonishing diversity of phenotypic traits, behaviors, and metabolic capacities. However, our molecular understanding of these phenotypes is based almost entirely on studies in a handful of model organisms that together represent only a small fraction of this phenotypic diversity. Furthermore, many microbial species are not amenable to traditional laboratory analysis because of their exotic lifestyles and/or lack of suitable molecular genetic techniques. As an adjunct to experimental analysis, we have developed a computational information-theoretic framework that produces high-confidence gene–phenotype predictions using cross-species distributions of genes and phenotypes across 202 fully sequenced archaea and eubacteria. In addition to identifying the genetic basis of complex traits, our approach reveals the organization of these genes into generic preferentially co-inherited modules, many of which correspond directly to known enzymatic pathways, molecular complexes, signaling pathways, and molecular machines

HNRNPA2B1 Is a Mediator of m6A-Dependent Nuclear RNA Processing Events

SummaryN6-methyladenosine (m6A) is the most abundant internal modification of messenger RNA. While the presence of m6A 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 m6A-bearing RNAs in vivo and in vitro and its biochemical footprint matches the m6A consensus motif. HNRNPA2B1 directly binds a set of nuclear transcripts and elicits similar alternative splicing effects as the m6A writer METTL3. Moreover, HNRNPA2B1 binds to m6A 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 m6A mark and to mediate, in part, this mark’s effects on primary microRNA processing and alternative splicing.PaperCli

Predicting Gene Expression from Sequence

AbstractWe describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73% of genes in Saccharomyces cerevisiae, utilizing microarray expression data and sequences in the 800 bp upstream of genes. Application to Caenorhabditis elegans identifies predictive regulatory elements and combinatorial rules that control the phased temporal expression of transcription factors, histones, and germline specific genes. Successful prediction requires diverse and complex rules utilizing AND, OR, and NOT logic, with significant constraints on motif strength, orientation, and relative position. This system generates a large number of mechanistic hypotheses for focused experimental validation, and establishes a predictive dynamical framework for understanding cellular behavior from genomic sequence

Fitness Landscape of Antibiotic Tolerance in Pseudomonas aeruginosa Biofilms

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

Genetic Architecture of Intrinsic Antibiotic Susceptibility

BACKGROUND:Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug's chemical structure and a bacterium's cellular network affect the types of mutations acquired. METHODOLOGY/PRINCIPAL FINDINGS:To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli's intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance. CONCLUSIONS/SIGNIFICANCE:Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect

Systematic discovery of structural elements governing stability of mammalian messenger RNAs.

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