Author
James J. Collins
Other affiliations: Baylor College of Medicine, University at Albany, SUNY, State University of New York System ...read more
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.
Papers published on a yearly basis
Papers
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TL;DR: It is illustrated here that network modeling of complex multidimensional cancer genomic data can generate a framework in which to explore the biology of cancers, leading to discovery of new pathogenetic insights as well as potential prognostic biomarkers.
Abstract: Leveraging The Cancer Genome Atlas (TCGA) multidimensional data in glioblastoma, we inferred the putative regulatory network between microRNA and mRNA using the Context Likelihood of Relatedness modeling algorithm. Interrogation of the network in context of defined molecular subtypes identified 8 microRNAs with a strong discriminatory potential between proneural and mesenchymal subtypes. Integrative in silico analyses, a functional genetic screen, and experimental validation identified miR-34a as a tumor suppressor in proneural subtype glioblastoma. Mechanistically, in addition to its direct regulation of platelet-derived growth factor receptor-alpha ( PDGFRA ), promoter enrichment analysis of context likelihood of relatedness–inferred mRNA nodes established miR-34a as a novel regulator of a SMAD4 transcriptional network. Clinically, miR-34a expression level is shown to be prognostic, where miR-34a low-expressing glioblastomas exhibited better overall survival. This work illustrates the potential of comprehensive multidimensional cancer genomic data combined with computational and experimental models in enabling mechanistic exploration of relationships among different genetic elements across the genome space in cancer.
Significance: We illustrate here that network modeling of complex multidimensional cancer genomic data can generate a framework in which to explore the biology of cancers, leading to discovery of new pathogenetic insights as well as potential prognostic biomarkers. Specifically in glioblastoma, within the context of the global network, promoter enrichment analysis of network edges uncovered a novel regulation of TGF-β signaling via a Smad4 transcriptomic network by miR-34a. Cancer Discov; 2(8); 736–49. ©2012 AACR.
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105 citations
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TL;DR: It is discovered that the mood stabilizer and anticonvulsant valproic acid (VA) extended C. elegans lifespan, and the mechanism of action of VA is distinct from that of trimethadione, and it is demonstrated that lifespan‐extending drugs can be combined to produce additive effects.
Abstract: Aging is an important biological phenomenon and a major contributor to human disease and disability, but no drugs have been demonstrated to delay human aging. Caenorhabditis elegans is a valuable model for studies of animal aging, and the analysis of drugs that extend the lifespan of this animal can elucidate mechanisms of aging and might lead to treatments for age-related disease. By testing drugs that are Food and Drug Administration approved for human use, we discovered that the mood stabilizer and anticonvulsant valproic acid (VA) extended C. elegans lifespan. VA also delayed age-related declines of body movement, indicating that VA delays aging. Valproic acid is a small carboxylic acid that is the most frequently prescribed anticonvulsant drug in humans. A structure-activity analysis demonstrated that the related compound valpromide also extends lifespan. Valproic acid treatment may modulate the insulin/IGF-1 growth factor signaling pathway, because VA promoted dauer larvae formation and DAF-16 nuclear localization. To investigate the mechanism of action of VA in delaying aging, we analyzed the effects of combining VA with other compounds that extend the lifespan of C. elegans. Combined treatment of animals with VA and the heterocyclic anticonvulsant trimethadione caused a lifespan extension that was significantly greater than treatment with either of these drugs alone. These data suggest that the mechanism of action of VA is distinct from that of trimethadione, and demonstrate that lifespan-extending drugs can be combined to produce additive effects.
104 citations
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TL;DR: It is demonstrated that subjects can act as ”responders” to galvanic vestibular stimulation, and the finding that the coherency was highest for the 1- to 2-Hz stochastic vestibul stimulation signal may be due to the intrinsic dynamics of the quasi-static postural control system.
Abstract: Galvanic vestibular stimulation serves to modulate the continuous firing level of the peripheral vestibular afferents. It has been shown that the application of sinusoidally varying, bipolar galvanic currents to the vestibular system can lead to sinusoidally varying postural sway. Our objective was to test the hypothesis that stochastic galvanic vestibular stimulation can lead to coherent stochastic postural sway. Bipolar binaural stochastic galvanic vestibular stimulation was applied to nine healthy young subjects. Three different stochastic vestibular stimulation signals, each with a different frequency content (0–1 Hz, 1–2 Hz, and 0–2 Hz), were used. The stimulation level (range 0.4–1.5 mA, peak to peak) was determined on an individual basis. Twenty 60-s trials were conducted on each subject – 15 stimulation trials (5 trials with each stimulation signal) and 5 control (no stimulation) trials. During the trials, subjects stood in a relaxed, upright position with their head facing forward. Postural sway was evaluated by using a force platform to measure the displacements of the center of pressure (COP) under each subject’s feet. Cross-spectral measures were used to quantify the relationship between the applied stimulus and the resulting COP time series. We found significant coherency between the stochastic vestibular stimulation signal and the resulting mediolateral COP time series in the majority of trials in 8 of the 9 subjects tested. The coherency results for each stimulation signal were reproducible from trial to trial, and the highest degree of coherency was found for the 1- to 2-Hz stochastic vestibular stimulation signal. In general, for the nine subjects tested, we did not find consistent significant coherency between the stochastic vestibular stimulation signals and the anteroposterior COP time series. This work demonstrates that, in subjects who are facing forward, bipolar binaural stochastic galvanic stimulation of the vestibular system leads to coherent stochastic mediolateral postural sway, but it does not lead to coherent stochastic anteroposterior postural sway. Our finding that the coherency was highest for the 1- to 2-Hz stochastic vestibular stimulation signal may be due to the intrinsic dynamics of the quasi-static postural control system. In particular, it may result from the effects of the vestibular stimulus simply being superimposed upon the quiet-standing COP displacements. By utilizing stochastic stimulation signals, we ensured that the subjects could not predict a change in the vestibular stimulus. Thus, our findings indicate that subjects can act as ”responders” to galvanic vestibular stimulation.
104 citations
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TL;DR: This study shows that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits, and demonstrates that assemblies can be adjusted to control circuit dynamics.
Abstract: Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic circuits.
104 citations
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TL;DR: In this paper, the authors performed transcriptome analysis of stem cells and their proximal progenitors to improve the molecular understanding of HSCs and to identify genes involved in transcriptional regulation, including a group of putative transcriptional repressors.
Abstract: Hematopoietic stem cells (HSCs) maintain blood homeostasis and are the functional units of bone marrow transplantation. To improve the molecular understanding of HSCs and their proximal progenitors, we performed transcriptome analysis within the context of the ImmGen Consortium data set. Gene sets that define steady-state and mobilized HSCs, as well as hematopoietic stem and progenitor cells (HSPCs), were determined. Genes involved in transcriptional regulation, including a group of putative transcriptional repressors, were identified in multipotent progenitors and HSCs. Proximal promoter analyses combined with ImmGen module analysis identified candidate regulators of HSCs. Enforced expression of one predicted regulator, Hlf, in diverse HSPC subsets led to extensive self-renewal activity ex vivo. These analyses reveal unique insights into the mechanisms that control the core properties of HSPCs.
101 citations
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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.
39,297 citations
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。
18,940 citations
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TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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.
18,415 citations
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TL;DR: In this paper, Imagined communities: Reflections on the origin and spread of nationalism are discussed. And the history of European ideas: Vol. 21, No. 5, pp. 721-722.
13,842 citations
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TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
9,324 citations