Consequences of defaunation for a tropical tree community

Hunting affects a considerably greater area of the tropical forest biome than deforestation and logging combined. Often even large remote protected areas are depleted of a substantial proportion of their vertebrate fauna. However, understanding of the long‐term ecological consequences of defaunation in tropical forests remains poor. Using tree census data from a large‐scale plot monitored over a 15‐year period since the approximate onset of intense hunting, we provide a comprehensive assessment of the immediate consequences of defaunation for a tropical tree community. Our data strongly suggest that over‐hunting has engendered pervasive changes in tree population spatial structure and dynamics, leading to a consistent decline in local tree diversity over time. However, we do not find any support for suggestions that over‐hunting reduces above‐ground biomass or biomass accumulation rate in this forest. To maintain critical ecosystem processes in tropical forests increased efforts are required to protect and restore wildlife populations.     

Unsupervised statistical clustering of environmental shotgun sequences

Background  

The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed.     

Individual Variations in Serum Melatonin Levels through Time: Implications for Epidemiologic Studies

Melatonin, a marker for the circadian rhythm with serum levels peaking between 2AM and 5AM, is hypothesized to possess anti-cancer properties, making it a mechanistic candidate for the probable carcinogenic effect of circadian rhythm disruption. In order to weigh epidemiologic evidence on the association of melatonin with cancer, we must first understand the laboratory and biological sources of variability in melatonin levels measured in samples. Participants for this methodological study were men enrolled in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO). We measured serum melatonin levels over a five year period in 97 individuals to test if melatonin levels are steady over time. The Pearson correlation coefficient between two measures separated by 1 year was 0.87, while the correlation between two measures separated by 5 years was to 0.70. In an additional cross-sectional study of 292 individuals, we used Analysis of Variance to identify differences in melatonin levels between different lifestyle and environmental characteristics. Serum melatonin levels were slightly higher in samples collected from 130 individuals during the winter, (6.36±0.59 pg/ml) than in samples collected from 119 individuals during the summer (4.83±0.62 pg/ml). Serum melatonin levels were lowest in current smokers (3.02±1.25 pg/ml, p = 0.007) compared to never (6.66±0.66 pg/ml) and former (5.59±0.50 pg/ml) smokers whereas BMI did not significantly affect serum melatonin levels in this study. In conclusion, the high 5 year correlation of melatonin levels implies that single measurements may be used to detect population level associations between melatonin and risk of cancer. Furthermore, our results reiterate the need to record season of sample collection, and individual characteristics in order to maximize study power and prevent confounding.     

Hyperosmotic stress alters the RNA polymerase II interactome and induces readthrough transcription despite widespread transcriptional repression

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Fhit Deficiency-Induced Global Genome Instability Promotes Mutation and Clonal Expansion

Loss of Fhit expression, encoded at chromosome fragile site FRA3B, leads to increased replication stress, genome instability and accumulation of genetic alterations. We have proposed that Fhit is a genome ‘caretaker’ whose loss initiates genome instability in preneoplastic lesions. We have characterized allele copy number alterations and expression changes observed in Fhit-deficient cells in conjunction with alterations in cellular proliferation and exome mutations, using cells from mouse embryo fibroblasts (MEFs), mouse kidney, early and late after establishment in culture, and in response to carcinogen treatment. Fhit ^-/- MEFs escape senescence to become immortal more rapidly than Fhit ^+/+ MEFs; -/- MEFs and kidney cultures show allele losses and gains, while +/+ derived cells show few genomic alterations. Striking alterations in expression of p53, p21, Mcl1 and active caspase 3 occurred in mouse kidney -/- cells during progressive tissue culture passage. To define genomic changes associated with preneoplastic changes in vivo, exome DNAs were sequenced for +/+ and -/- liver tissue after treatment of mice with the carcinogen, 7,12-dimethylbenz[a]anthracene, and for +/+ and -/- kidney cells treated in vitro with this carcinogen. The -/- exome DNAs, in comparison with +/+ DNA, showed small insertions, deletions and point mutations in more genes, some likely related to preneoplastic changes. Thus, Fhit loss provides a ‘mutator’ phenotype, a cellular environment in which mild genome instability permits clonal expansion, through proliferative advantage and escape from apoptosis, in response to pressures to survive.     

OpenAWSEM with Open3SPN2: A fast,flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.     

Fast Approximate Quadratic Programming for Graph Matching

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance.