Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

International audienceWe present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information

Transcriptome pathways unique to dehydration tolerant relatives of modern wheat

Among abiotic stressors, drought is a major factor responsible for dramatic yield loss in agriculture. In order to reveal differences in global expression profiles of drought tolerant and sensitive wild emmer wheat genotypes, a previously deployed shock-like dehydration process was utilized to compare transcriptomes at two time points in root and leaf tissues using the Affymetrix GeneChip(R) Wheat Genome Array hybridization. The comparison of transcriptomes reveal several unique genes or expression patterns such as differential usage of IP(3)-dependent signal transduction pathways, ethylene- and abscisic acid (ABA)-dependent signaling, and preferential or faster induction of ABA-dependent transcription factors by the tolerant genotype that distinguish contrasting genotypes indicative of distinctive stress response pathways. The data also show that wild emmer wheat is capable of engaging known drought stress responsive mechanisms. The global comparison of transcriptomes in the absence of and after dehydration underlined the gene networks especially in root tissues that may have been lost in the selection processes generating modern bread wheats

A novel malaria vaccine candidate antigen expressed in Tetrahymena thermophila

Development of effective malaria vaccines is hampered by the problem of producing correctly folded Plasmodium proteins for use as vaccine components. We have investigated the use of a novel ciliate expression system, Tetrahymena thermophila, as a P. falciparum vaccine antigen platform. A synthetic vaccine antigen composed of N-terminal and C-terminal regions of merozoite surface protein-1 (MSP-1) was expressed in Tetrahymena thermophila. The recombinant antigen was secreted into the culture medium and purified by monoclonal antibody (mAb) affinity chromatography. The vaccine was immunogenic in MF1 mice, eliciting high antibody titers against both N- and C-terminal components. Sera from immunized animals reacted strongly with P. falciparum parasites from three antigenically different strains by immunofluorescence assays, confirming that the antibodies produced are able to recognize parasite antigens in their native form. Epitope mapping of serum reactivity with a peptide library derived from all three MSP-1 Block 2 serotypes confirmed that the MSP-1 Block 2 hybrid component of the vaccine had effectively targeted all three serotypes of this polymorphic region of MSP-1. This study has successfully demonstrated the use of Tetrahymena thermophila as a recombinant protein expression platform for the production of malaria vaccine antigens

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance

Bioinformatics for the human microbiome project

Microbes inhabit virtually all sites of the human body, yet we know very little about the role they play in our health. In recent years, there has been increasing interest in studying human-associated microbial communities, particularly since microbial dysbioses have now been implicated in a number of human diseases [1]–[3]. Dysbiosis, the disruption of the normal microbial community structure, however, is impossible to define without first establishing what “normal microbial community structure” means within the healthy human microbiome. Recent advances in sequencing technologies have made it feasible to perform large-scale studies of microbial communities, providing the tools necessary to begin to address this question [4], [5]. This led to the implementation of the Human Microbiome Project (HMP) in 2007, an initiative funded by the National Institutes of Health Roadmap for Biomedical Research and constructed as a large, genome-scale community research project [6]. Any such project must plan for data analysis, computational methods development, and the public availability of tools and data; here, we provide an overview of the corresponding bioinformatics organization, history, and results from the HMP (Figure 1).National Institutes of Health (U.S.) (NIH U54HG004969)National Institutes of Health (U.S.) (grant R01HG004885)National Institutes of Health (U.S.) (grant R01HG005975)National Institutes of Health (U.S.) (grant R01HG005969

A subset of platinum-containing chemotherapeutic agents kills cells by inducing ribosome biogenesis stress

Cisplatin and its platinum analogs, carboplatin and oxaliplatin, are some of the most widely used cancer chemotherapeutics. Although cisplatin and carboplatin are used primarily in germ cell, breast and lung malignancies, oxaliplatin is instead used almost exclusively to treat colorectal and other gastrointestinal cancers. Here we utilize a unique, multi-platform genetic approach to study the mechanism of action of these clinically established platinum anti-cancer agents, as well as more recently developed cisplatin analogs. We show that oxaliplatin, unlike cisplatin and carboplatin, does not kill cells through the DNA-damage response. Rather, oxaliplatin kills cells by inducing ribosome biogenesis stress. This difference in drug mechanism explains the distinct clinical implementation of oxaliplatin relative to cisplatin, and it might enable mechanistically informed selection of distinct platinum drugs for distinct malignancies. These data highlight the functional diversity of core components of front-line cancer therapy and the potential benefits of applying a mechanism-based rationale to the use of our current arsenal of anti-cancer drugs

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/human​n. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.National Institutes of Health (U.S.) (U54HG004968

Transmission Shifts Underlie Variability in Population Responses to Yersinia pestis Infection

Host populations for the plague bacterium, Yersinia pestis, are highly variable in their response to plague ranging from near deterministic extinction (i.e., epizootic dynamics) to a low probability of extinction despite persistent infection (i.e., enzootic dynamics). Much of the work to understand this variability has focused on specific host characteristics, such as population size and resistance, and their role in determining plague dynamics. Here, however, we advance the idea that the relative importance of alternative transmission routes may vary causing shifts from epizootic to enzootic dynamics. We present a model that incorporates host and flea ecology with multiple transmission hypotheses to study how transmission shifts determine population responses to plague. Our results suggest enzootic persistence relies on infection of an off-host flea reservoir and epizootics rely on transiently maintained flea infection loads through repeated infectious feeds by fleas. In either case, early-phase transmission by fleas (i.e., transmission immediately following an infected blood meal) has been observed in laboratory studies, and we show that it is capable of driving plague dynamics at the population level. Sensitivity analysis of model parameters revealed that host characteristics (e.g., population size and resistance) vary in importance depending on transmission dynamics, suggesting that host ecology may scale differently through different transmission routes enabling prediction of population responses in a more robust way than using either host characteristics or transmission shifts alone

Possible pro-carcinogenic association of endotoxin on lung cancer among Shanghai women textile workers

Background: Endotoxin (lipopolysaccharide) is a widespread contaminant in many environmental settings. Since the 1970s, there has been generally consistent evidence indicating reduced risks for lung cancer associated with occupational endotoxin exposure. Methods: We updated a case–cohort study nested within a cohort of 267 400 female textile workers in Shanghai, China. We compared exposure histories of 1456 incident lung cancers cases diagnosed during 1989–2006 with those of a reference subcohort of 3022 workers who were free of lung cancer at the end of follow-up. We applied Cox proportional hazards modelling to estimate exposure–response trends, adjusted for age and smoking, for cumulative exposures lagged by 0, 10, and 20 years, and separately for time windows of ⩽15 and \u3e15 years since first exposure. Results: We observed no associations between cumulative exposure and lung cancer, irrespective of lag interval. In contrast, analyses by exposure time windows revealed modestly elevated, but not statistically significant relative risks (∼1.27) at the highest three exposure quintiles for exposures that occurred \u3e15 years since first exposure. Conclusions: The findings do not support a protective effect of endotoxin, but are suggestive of possible lung cancer promotion with increasing time since first exposure