A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer

We introduce a Markov model for the evolution of a gene family along a phylogeny. The model includes parameters for the rates of horizontal gene transfer, gene duplication, and gene loss, in addition to branch lengths in the phylogeny. The likelihood for the changes in the size of a gene family across different organisms can be calculated in O(N+hM^2) time and O(N+M^2) space, where N is the number of organisms, $h$ is the height of the phylogeny, and M is the sum of family sizes. We apply the model to the evolution of gene content in Preoteobacteria using the gene families in the COG (Clusters of Orthologous Groups) database

GO4genome: A Prokaryotic Phylogeny Based on Genome Organization

Determining the phylogeny of closely related prokaryotes may fail in an analysis of rRNA or a small set of sequences. Whole-genome phylogeny utilizes the maximally available sample space. For a precise determination of genome similarity, two aspects have to be considered when developing an algorithm of whole-genome phylogeny: (1) gene order conservation is a more precise signal than gene content; and (2) when using sequence similarity, failures in identifying orthologues or the in situ replacement of genes via horizontal gene transfer may give misleading results. GO4genome is a new paradigm, which is based on a detailed analysis of gene function and the location of the respective genes. For characterization of genes, the algorithm uses gene ontology enabling a comparison of function independent of evolutionary relationship. After the identification of locally optimal series of gene functions, their length distribution is utilized to compute a phylogenetic distance. The outcome is a classification of genomes based on metabolic capabilities and their organization. Thus, the impact of effects on genome organization that are not covered by methods of molecular phylogeny can be studied. Genomes of strains belonging to Escherichia coli, Shigella, Streptococcus, Methanosarcina, and Yersinia were analyzed. Differences from the findings of classical methods are discussed

Phytochemicals as antibiotic alternatives to promote growth and enhance host health

There are heightened concerns globally on emerging drug-resistant superbugs and the lack of new antibiotics for treating human and animal diseases. For the agricultural industry, there is an urgent need to develop strategies to replace antibiotics for food-producing animals, especially poultry and livestock. The 2nd International Symposium on Alternatives to Antibiotics was held at the World Organization for Animal Health in Paris, France, December 12-15, 2016 to discuss recent scientific developments on strategic antibiotic-free management plans, to evaluate regional differences in policies regarding the reduction of antibiotics in animal agriculture and to develop antibiotic alternatives to combat the global increase in antibiotic resistance. More than 270 participants from academia, government research institutions, regulatory agencies, and private animal industries from >25 different countries came together to discuss recent research and promising novel technologies that could provide alternatives to antibiotics for use in animal health and production; assess challenges associated with their commercialization; and devise actionable strategies to facilitate the development of alternatives to antibiotic growth promoters (AGPs) without hampering animal production. The 3-day meeting consisted of four scientific sessions including vaccines, microbial products, phytochemicals, immune-related products, and innovative drugs, chemicals and enzymes, followed by the last session on regulation and funding. Each session was followed by an expert panel discussion that included industry representatives and session speakers. The session on phytochemicals included talks describing recent research achievements, with examples of successful agricultural use of various phytochemicals as antibiotic alternatives and their mode of action in major agricultural animals (poultry, swine and ruminants). Scientists from industry and academia and government research institutes shared their experience in developing and applying potential antibiotic-alternative phytochemicals commercially to reduce AGPs and to develop a sustainable animal production system in the absence of antibiotics.Fil: Lillehoj, Hyun. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Liu, Yanhong. University of California; Estados UnidosFil: Calsamiglia, Sergio. Universitat Autònoma de Barcelona; EspañaFil: Fernandez Miyakawa, Mariano Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Patobiología; ArgentinaFil: Chi, Fang. Amlan International; Estados UnidosFil: Cravens, Ron L.. Amlan International; Estados UnidosFil: Oh, Sungtaek. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Gay, Cyril G.. United States Department of Agriculture. Agricultural Research Service; Argentin

Is My Network Module Preserved and Reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation

Discovering local patterns of co - evolution: computational aspects and biological examples

<p>Abstract</p> <p>Background</p> <p>Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems.</p> <p>Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.</p> <p>Results</p> <p>In this work, we describe a new set of biological problems that are related to finding patterns of <it>local </it>co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above.</p> <p>We use our approach to trace the co-evolution of fungal, eukaryotic, and mammalian genes at high resolution across the different parts of the corresponding phylogenetic trees. Specifically, we discover regions in the fungi tree that are enriched with positive evolution. We show that metabolic genes exhibit a remarkable level of co-evolution and different patterns of co-evolution in various biological datasets.</p> <p>In addition, we find that protein complexes that are related to gene expression exhibit non-homogenous levels of co-evolution across different parts of the <it>fungi </it>evolutionary line. In the case of mammalian evolution, signaling pathways that are related to <it>neurotransmission </it>exhibit a relatively higher level of co-evolution along the <it>primate </it>subtree.</p> <p>Conclusions</p> <p>We show that finding local patterns of co-evolution is a computationally challenging task and we offer novel algorithms that allow us to solve this problem, thus opening a new approach for analyzing the evolution of biological systems.</p

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available

Effect of Liposome Characteristics and Dose on the Pharmacokinetics of Liposomes Coated with Poly(amino acid)s

Long-circulating liposomes, such as PEG-liposomes, are frequently studied for drug delivery and diagnostic purposes. In our group, poly(amino acid) (PAA)-based coatings for long-circulating liposomes have been developed. These coatings provide liposomes with similar circulation times as compared to PEG-liposomes, but have the advantage of being enzymatically degradable. For PEG-liposomes it has been reported that circulation times are relatively independent of their physicochemical characteristics. In this study, the influence of factors such as PAA grafting density, cholesterol inclusion, surface charge, particle size, and lipid dose on the circulation kinetics of PAA-liposomes was evaluated after intravenous administration in rats. Prolonged circulation kinetics of PAA-liposomes can be maintained upon variation of liposome characteristics and the lipid dose given. However, the use of relatively high amounts of strongly charge-inducing lipids and a too large mean size is to be avoided. In conclusion, PAA-liposomes represent a versatile drug carrier system for a wide variety of applications

Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression

The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA (mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins (mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted (ρ0) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ0 cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions

Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures – results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation