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.

Cytoplasmic condensation induced by membrane damage is associated with antibiotic lethality.

Abstract: Bactericidal antibiotics kill bacteria by perturbing various cellular targets and processes. Disruption of the primary antibiotic-binding partner induces a cascade of molecular events, leading to overproduction of reactive metabolic by-products. It remains unclear, however, how these molecular events contribute to bacterial cell death. Here, we take a single-cell physical biology approach to probe antibiotic function. We show that aminoglycosides and fluoroquinolones induce cytoplasmic condensation through membrane damage and subsequent outflow of cytoplasmic contents as part of their lethality. A quantitative model of membrane damage and cytoplasmic leakage indicates that a small number of nanometer-scale membrane defects in a single bacterium can give rise to the cellular-scale phenotype of cytoplasmic condensation. Furthermore, cytoplasmic condensation is associated with the accumulation of reactive metabolic by-products and lipid peroxidation, and pretreatment of cells with the antioxidant glutathione attenuates cytoplasmic condensation and cell death. Our work expands our understanding of the downstream molecular events that are associated with antibiotic lethality, revealing cytoplasmic condensation as a phenotypic feature of antibiotic-induced bacterial cell death.

Review and meta-analysis of studies of acrylonitrile workers

Abstract: Twenty-five epidemiologic studies of acrylonitrile workers were reviewed and subjected to meta-analytic techniques in this study to assess the findings for 10 cancer sites. The analyses indicate that workers with acrylonitrile exposure have essentially null findings for most cancers, including lung [meta-relative risk (mRR) 0.9, 95% confidence interval (95% CI) 0.9-1.1], brain (mRR 1.2, 95% CI 0.8-1.7), and prostate (mRR 1.0, 95% CI 0.7-1.4) cancers. Bladder cancer rates were elevated (mRR 1.8, 95% CI 1.0-3.4), but the excess was not dose-related and was limited to plants with aromatic amines. Therefore, the bladder cancer excess is unlikely to be related to acrylonitrile exposure. Some evidence of publication bias was found in the examined literature, but the bias did not have a significant impact on risk estimates for individual cancers. It was concluded that the available studies do not support a causal relation between acrylonitrile exposure and cancer.

Assessing muscle stiffness from quiet stance in Parkinson's disease.

Abstract: In previous studies, we developed a postural stiffness measure that is extracted from foot center-of-pressure (COP) trajectories from quietly standing individuals and is based on an analytical mechanical model of posture control. Here we apply this measure to patients with Parkinson's disease (PD). We correlated the postural stiffness measure with different clinical rating scales, obtained from patients. Kendall's rank correlation was highly significant between the stiffness measure and rigidity, bradykinesia, posture impairment, gait, and leg agility, respectively, as rated by the Unified Parkinson's Disease Rating Scale. These results provide further evidence that a higher intrinsic muscle stiffness may contribute to the aforementioned clinically defined symptoms. From a clinical standpoint, this work indicates that the proposed postural stiffness measure may be useful as an assessment tool for the evaluation of PD patients subsequent to pharmacological and surgical treatment.

Cis/trans riboregulators

Abstract: The present invention provides nucleic acid molecules, DNA constructs, plasmids, and methods for post-transcriptional regulation of gene expression using RNA molecules to both repress and activate translation of an open reading frame. Repression of gene expression is achieved through the presence of a regulatory nucleic acid element (the cis-repressive RNA or crRNA) within the 5' untranslated region (5' UTR) of an mRNA molecule. The nucleic acid element forms a hairpin (stem/loop) structure through complementary base pairing. The hairpin blocks access to the mRNA transcript by the ribosome, thereby preventing translation. In particular, in embodiments of the invention designed to operate in prokaryotic cells, the stem of the hairpin secondary structure sequesters the ribosome binding site (RBS). In embodiments of the invention designed to operate in eukaryotic cells, the stem of the hairpin is positioned upstream of the start codon, anywhere within the 5' UTR of an mRNA. A small RNA (trans-activating RNA, or taRNA), expressed in trans, interacts with the crRNA and alters the hairpin structure. This alteration allows the ribosome to gain access to the region of the transcript upstream of the start codon, thereby activating transcription from its previously repressed state.

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.