James J. Collins

Bio: James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publications receiving 89476 citations. Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.

Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production

Abstract: The ever-increasing incidence of antibiotic-resistant infections combined with a weak pipeline of new antibiotics has created a global public health crisis. Accordingly, novel strategies for enhancing our antibiotic arsenal are needed. As antibiotics kill bacteria in part by inducing reactive oxygen species (ROS), we reasoned that targeting microbial ROS production might potentiate antibiotic activity. Here we show that ROS production can be predictably enhanced in Escherichia coli, increasing the bacteria's susceptibility to oxidative attack. We developed an ensemble approach of genome-scale, metabolic models capable of predicting ROS production in E. coli. The metabolic network was systematically perturbed and its flux distribution analyzed to identify targets predicted to increase ROS production. Targets that were predicted in silico were experimentally validated and further shown to confer increased susceptibility to oxidants. Validated targets also increased susceptibility to killing by antibiotics. This work establishes a systems-based method to tune ROS production in bacteria and demonstrates that increased microbial ROS production can potentiate killing by oxidants and antibiotics.

Deconstructing transcriptional heterogeneity in pluripotent stem cells

Abstract: SUMMARY Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to characterize transcriptional heterogeneity in PSCs by single-cell

A bottom-up approach to gene regulation

Abstract: The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the ‘bottom up’. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

Symmetry in locomotor central pattern generators and animal gaits.

Abstract: Animal locomotion is controlled, in part, by a central pattern generator (CPG), which is an intraspinal network of neurons capable of generating a rhythmic output1,2,3,4. The spatio-temporal symmetries of the quadrupedal gaits walk, trot and pace5,6,7,8 lead to plausible assumptions about the symmetries of locomotor CPGs9,10,11. These assumptions imply that the CPG of a quadruped should consist of eight nominally identical subcircuits, arranged in an essentially unique matter. Here we apply analogous arguments to myriapod CPGs. Analyses based on symmetry applied to these networks lead to testable predictions, including a distinction between primary and secondary gaits, the existence of a new primary gait called ‘jump’, and the occurrence of half-integer wave numbers in myriapod gaits. For bipeds, our analysis also predicts two gaits with the out-of-phase symmetry of the walk and two gaits with the in-phase symmetry of the hop. We present data that support each of these predictions. This work suggests that symmetry can be used to infer a plausible class of CPG network architectures from observed patterns of animal gaits.

Oxidation of the Guanine Nucleotide Pool Underlies Cell Death by Bactericidal Antibiotics

Abstract: A Specific Oxidative Catastrophe Three different classes of antibiotics induce bacterial cell death by the production of hydroxyl radicals. Hydroxyl radicals are powerful oxidizing agents in living cells and will oxidize the nucleic acid base, guanine, to form 8-oxoguanine, which is potentially mutagenic because it can pair with both cytosine and adenine and form lethal double-strand DNA breaks. Foti et al. (p. 315) discovered that overproduction of the nucleotide sanitizer MutT, which hydrolyzes 8-oxo-dGTP to 8-oxo-dGMP, gives striking protection against cell death. Several antibiotics kill bacteria by causing oxidative damage to guanine nucleotides, which then damage nucleic acids. A detailed understanding of the mechanisms that underlie antibiotic killing is important for the derivation of new classes of antibiotics and clinically useful adjuvants for current antimicrobial therapies. Our efforts to understand why DinB (DNA polymerase IV) overproduction is cytotoxic to Escherichia coli led to the unexpected insight that oxidation of guanine to 8-oxo-guanine in the nucleotide pool underlies much of the cell death caused by both DinB overproduction and bactericidal antibiotics. We propose a model in which the cytotoxicity of beta-lactams and quinolones predominantly results from lethal double-strand DNA breaks caused by incomplete repair of closely spaced 8-oxo-deoxyguanosine lesions, whereas the cytotoxicity of aminoglycosides might additionally result from mistranslation due to the incorporation of 8-oxo-guanine into newly synthesized RNAs.

Collective dynamics of small-world networks

Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Statistical mechanics of complex networks

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.