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.

Reply to Blair et al.: Dose-dependent effects and oxidative stress responses in antibiotic tolerance in Salmonella typhimurium

Abstract: We appreciate the interest taken in our recent work (1) by Blair et al. (2), who are “perplexed” that we did not observe induction of ramA expression in Salmonella typhimurium treated with 50–125 μM indole. It is important to note, however, that induction of the Salmonella typhimurium ramA gene by indole is dose dependent (3) and that the indole concentrations used in our study are an order of magnitude lower than those that induce ramA. The study cited by Blair et al. as being contradictory to our results, in fact, shows that ramA is induced at indole concentrations greater than 1 mM, and negligible induction of ramA is observed when strains were grown in media containing 500 μM (3) (see figure 1B in ref. 3; lower concentrations were not presented). We previously observed, using full-genome transcriptional analysis, that nontoxic concentrations of indole do not significantly induce drug efflux pumps in Escherichia coli (4). This finding is consistent with those of an earlier study showing that high concentrations of indole (2 mM) induce efflux pumps but lower concentrations (0.5–1 mM) do not (5). As noted above, we did not observe induction of ramA, the main regulator of efflux pumps in S. typhimurium, at the relatively low physiological concentrations of indole we considered.

SETDB1 acts as a topological accessory to Cohesin via an H3K9me3-independent, genomic shunt for regulating cell fates

Abstract: Abstract SETDB1 is a key regulator of lineage-specific genes and endogenous retroviral elements (ERVs) through its deposition of repressive H3K9me3 mark. Apart from its H3K9me3 regulatory role, SETDB1 has seldom been studied in terms of its other potential regulatory roles. To investigate this, a genomic survey of SETDB1 binding in mouse embryonic stem cells across multiple libraries was conducted, leading to the unexpected discovery of regions bereft of common repressive histone marks (H3K9me3, H3K27me3). These regions were enriched with the CTCF motif that is often associated with the topological regulator Cohesin. Further profiling of these non-H3K9me3 regions led to the discovery of a cluster of non-repeat loci that were co-bound by SETDB1 and Cohesin. These regions, which we named DiSCs (domains involving SETDB1 and Cohesin) were seen to be proximal to the gene promoters involved in embryonic stem cell pluripotency and lineage development. Importantly, it was found that SETDB1-Cohesin co-regulate target gene expression and genome topology at these DiSCs. Depletion of SETDB1 led to localized dysregulation of Cohesin binding thereby locally disrupting topological structures. Dysregulated gene expression trends revealed the importance of this cluster in ES cell maintenance as well as at gene ‘islands’ that drive differentiation to other lineages. The ‘unearthing’ of the DiSCs thus unravels a unique topological and transcriptional axis of control regulated chiefly by SETDB1.

Screening-level risk assessment for styrene-acrylonitrile (SAN) trimer detected in soil and groundwater

Abstract: A screening-level risk assessment was conducted for styrene-acrylonitrile (SAN) Trimer detected at the Reich Farm Superfund site in Toms River, NJ. Consistent with a screening-level approach, on-site and off-site exposure scenarios were evaluated using assumptions that are expected to overestimate actual exposures and hazards at the site. Environmental sampling data collected for soil and groundwater were used to estimate exposure point concentrations. Several exposure scenarios were evaluated to assess potential on-site and off-site exposures, using parameter values for exposures to soil (oral, inhalation of particulates, and dermal contact) and groundwater (oral, dermal contact) to reflect central tendency exposure (CTE) and reasonable maximum exposure (RME) conditions. Three reference dose (RfD) values were derived for SAN Trimer for short-term, subchronic, and chronic exposures, based upon its effects on the liver in exposed rats. Benchmark (BMD) methods were used to assess the relationship between exposure and response, and to characterize appropriate points of departure (POD) for each RfD. An uncertainty factor of 300 was applied to each POD to yield RfD values of 0.1, 0.04, and 0.03 mg/kg-d for short-term, subchronic, and chronic exposures, respectively. Because a chronic cancer bioassay for SAN Trimer in rats (NTP 2011a) does not provide evidence of carcinogenicity, a cancer risk assessment is not appropriate for this chemical. Potential health hazards to human health were assessed using a hazard index (HI) approach, which considers the ratio of exposure dose (i.e., average daily dose, mg/kg-d) to toxicity dose (RfD, mg/kg-d) for each scenario. All CTE and RME HI values are well below 1 (where the average daily dose is equivalent to the RfD), indicating that there is no concern for potential noncancer effects in exposed populations even under the conservative assumptions of this screening-level assessment.

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.