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.

Chromatin regulation at the frontier of synthetic biology

Abstract: As synthetic biology approaches are extended to diverse applications throughout medicine, biotechnology and basic biological research, there is an increasing need to engineer yeast, plant and mammalian cells. Eukaryotic genomes are regulated by the diverse biochemical and biophysical states of chromatin, which brings distinct challenges, as well as opportunities, over applications in bacteria. Recent synthetic approaches, including 'epigenome editing', have allowed the direct and functional dissection of many aspects of physiological chromatin regulation. These studies lay the foundation for biomedical and biotechnological engineering applications that could take advantage of the unique combinatorial and spatiotemporal layers of chromatin regulation to create synthetic systems of unprecedented sophistication.

Roles for the transcription elongation factor NusA in both DNA repair and damage tolerance pathways in Escherichia coli

Abstract: We report observations suggesting that the transcription elongation factor NusA promotes a previously unrecognized class of transcription-coupled repair (TCR) in addition to its previously proposed role in recruiting translesion synthesis (TLS) DNA polymerases to gaps encountered during transcription. Earlier, we reported that NusA physically and genetically interacts with the TLS DNA polymerase DinB (DNA pol IV). We find that Escherichia coli nusA11(ts) mutant strains, at the permissive temperature, are highly sensitive to nitrofurazone (NFZ) and 4-nitroquinolone-1-oxide but not to UV radiation. Gene expression profiling suggests that this sensitivity is unlikely to be due to an indirect effect on gene expression affecting a known DNA repair or damage tolerance pathway. We demonstrate that an N2-furfuryl-dG (N2-f-dG) lesion, a structural analog of the principal lesion generated by NFZ, blocks transcription by E. coli RNA polymerase (RNAP) when present in the transcribed strand, but not when present in the nontranscribed strand. Our genetic analysis suggests that NusA participates in a nucleotide excision repair (NER)-dependent process to promote NFZ resistance. We provide evidence that transcription plays a role in the repair of NFZ-induced lesions through the isolation of RNAP mutants that display altered ability to survive NFZ exposure. We propose that NusA participates in an alternative class of TCR involved in the identification and removal of a class of lesion, such as the N2-f-dG lesion, which are accurately and efficiently bypassed by DinB in addition to recruiting DinB for TLS at gaps encountered by RNAP.

Discourse Style, Classroom Interaction and Differential Treatment

Abstract: The implications of ability-tracking for educational inequality are well known and controversial. Few studies, however, have been successful in capturing the interplay between students' discourse style, teacher expectations in particular classroom tasks and achievement outcomes. In what follows I will discuss the apparent influence of tracking on the kind of reading instruction students receive in urban school systems. The discussion will focus on how the discriminatory treatment reported by various researchers is perpetuated in face-to-face classroom interaction. Two schools are reported upon in which differential treatment assumes different forms, but always entails, for one group, a denial of access to practice in comprehending texts. In ethnographic studies seeking to identify the ways in which the structure of public schooling contributes to the perpetuation of social inequality, one of the most provocative findings has been that larger patterns of stratification in terms of race and class are reproduced in the organization of school environments. Research conducted during the last decade has shown that teachers consistently differ in their treatment of students in inner-city versus suburban schools (Leacock, 1969), and further, that within the same school, social class plays an important role in the assignment of students to ability groups (Rist, 1971; and Rist, 1977, and citations therein). Additionally, detailed study of classroom reading groups has shown that much less time is devoted to the actual task of reading in low-ranked groups; that non-verbal interaction in these groups is tightly structured; and, most important, that the process of successful and unsuccessful learning is collaborative (McDermott, 1976). Because all learning results in part from social interaction, a major goal in the studies reported on was to see whether methods of conversational analysis could be profitably applied to the study of classroom interaction. One reason for the focus on conversation was that several recent studies of early language learning have shown that mothers' conversational strategies with children affects the rate of initial language acquisition; that is, social interaction influences the acquisition of basic linguistic

Functional characterization of bacterial sRNAs using a network biology approach

Abstract: Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

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.