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.

Mortality study update of acrylamide workers

Abstract: Objective: We examined the long term health effects of occupational exposure to acrylamide (AMD) among production and polymerization workers. Methods: An earlier study of 371 AMD workers was expanded to include employees hired since 1979. In this updated study, 696 AMD workers were followed from 1955 through 2001 to ascertain vital status and cause of death. Exposure to AMD was retrospectively assessed based on personal samples from the 1970’s onwards and area samples over the whole study period. Results: Fewer of the AMD workers died (N=141) compared to an expected number of 172.1 (SMR= 81.9, 95% CI: 69.0-96.6). No cause specific SMR for any of the investigated types of cancer was dose related. We did, however, find more pancreatic cancer deaths than expected (SMR=222.2, 95%CI: 72.1- 518.5). With respect to non-malignant disease, more diabetes deaths were observed than expected (SMR=288.7, 95% CI: 138.4-531.0). The excess persisted in an analysis with an internal reference population from the internal analysis. We conclude that the elevated SMR for diabetes mortality probably is not related to regional influences or differences in coding practices. Conclusion: This study provides little evidence for a cancer risk from occupational exposure to AMD at production facilities. However, the increased rates of pancreatic cancer in our study and another larger study of AMD production workers indicate that caution is needed to rule out a cancer risk. We believe that the excess of diabetes mortality in our study is most likely not related to AMD exposure because a larger study of AMD workers reported a deficit in this cause of death.

Engineering advanced logic and distributed computing in human CAR immune cells

Abstract: The immune system is a sophisticated network of different cell types performing complex biocomputation at single-cell and consortium levels. The ability to reprogram such an interconnected multicellular system holds enormous promise in treating various diseases, as exemplified by the use of chimeric antigen receptor (CAR) T cells as cancer therapy. However, most CAR designs lack computation features and cannot reprogram multiple immune cell types in a coordinated manner. Here, leveraging our split, universal, and programmable (SUPRA) CAR system, we develop an inhibitory feature, achieving a three-input logic, and demonstrate that this programmable system is functional in diverse adaptive and innate immune cells. We also create an inducible multi-cellular NIMPLY circuit, kill switch, and a synthetic intercellular communication channel. Our work highlights that a simple split CAR design can generate diverse and complex phenotypes and provide a foundation for engineering an immune cell consortium with user-defined functionalities.

Congenital muscular dystrophies: toward molecular therapeutic interventions.

Abstract: Congenital muscular dystrophies (CMDs) are a clinically and genetically heterogeneous group of neuromuscular disorders that typically present at birth or in early infancy with hypotonia, weakness, and histologic evidence of a dystrophic myopathy. CMD biochemical types include various abnormalities of alpha-dystroglycan O-mannosyl glycosylation as well as defects in integrin matrix receptors, the extracellular matrix proteins laminin-alpha(2) and collagen VI, nuclear proteins such as lamin A/C, and a protein of the endoplasmic reticulum, selenoprotein N. Current therapies are directed mostly at supportive care; however, recent advances in biotechnology and increased knowledge of the pathophysiology underlying the various CMD types have helped identify potential therapeutic strategies directed at genetic, molecular, and biochemical pathways involved in these disorders. In this article, we review our current understanding of the molecular pathogenesis of several CMD types and how these mechanisms may be therapeutically targeted.

Flatworm-specific transcriptional regulators promote the specification of tegumental progenitors in Schistosoma mansoni

Abstract: Schistosomiasis is a devastating disease that infects more than 200 million people and kills 200 thousand people every year. The disease is caused by parasitic flatworms known as schistosomes. These worms can live in the bloodstream for decades, even if the host has a healthy immune system. This ability to evade the immune system is thought to be partly due to the worm’s special ‘skin’, a tissue referred to as the tegument that all parasitic flatworms have. The tegument is a massive cell that covers the entire surface of the worm, and is thought to be an adaptation that enabled flatworms to become parasites. Despite the important role that the tegument appears to play in the biology of parasitic flatworms, very little is actually known about how this tissue is made and maintained. The tegument likely experiences a great deal of damage because it serves as the interface between the parasite and the host. Can the parasite repair the tissue as it becomes damaged? If the parasite relies upon this renewal, then preventing schistosomes from repairing their tegument could be a new way to treat schistosomiasis. Wendt et al. developed a new technique to fluorescently label a schistosome’s tegument. This revealed that the parasite does continuously repair and replace its tegument. To better understand this process, Wendt et al. identified genes that were active in the cells responsible for making the tegument. Two of these genes appear to regulate tegument production, and these genes can be found in both parasitic and non-parasitic flatworms. Further studies of these genes could shed light specifically onto how parasitism arose in flatworms. In addition, a better understanding of how the tegument develops and functions could identify new drug targets that could be used against the many diseases caused by parasitic flatworms.

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.