Mechanisms for maintaining genomic integrity during chromosome segregation in budding yeast

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references.Maintaining genomic integrity is crucial for an organism's fitness and survival. Regulation of chromosome segregation requires complex surveillance mechanisms that vary for different loci within the genome. This thesis focuses on two complexes, monopolin (made up of Lrs4, Csml and Maml) and condensin, a protein complex required for chromosome condensation, and their roles in chromosome segregation during mitosis and meiosis. During mitosis, Lrs4-Csml and condensiin reside in the nucleolus where they regulate the maintenance and segregation of the budding yeast ribosomal DNA array, a highly repetitive and transcriptionally active locus. Here I show that Lrs4 and Csml bind the RENT complex at the non-transcribed space region 1 within the rDNA array and via cohesin or condensin inhibit unequal exchange between sister chromatids. This complex is released during anaphase, during which Lrs4 and Csml localize to kinetochores, where they play a role in mitotic chromosome segregation. Although their role in meiotic chromosome Here we show that Lrs4 and Csml collaborate with condensins at kinetochores to control mitotic and meiotic chromosome segregation. During meiosis, diploid cells must first segregate homologous chromosomes before sister chromatids can separate. Lrs4-Csml and condensin are required during the first meiotic division to bring about the co-segregation of sister chromatids towards one pole and for the binding of monopolin subunit Maml. In summary, I show here that condensins and Lrs4-Csml are required at various chromosomal locations to provide linkages between sister chromatids to promote high fidelity chromosome segregation.by Ilana L. Brito.Ph.D

Tracking Strains in the Microbiome: Insights from Metagenomics and Models

Transmission usually refers to the movement of pathogenic organisms. Yet, commensal microbes that inhabit the human body also move between individuals and environments. Surprisingly little is known about the transmission of these endogenous microbes, despite increasing realizations of their importance for human health. The health impacts arising from the transmission of commensal bacteria range widely, from the prevention of autoimmune disorders to the spread of antibiotic resistance genes. Despite this importance, there are outstanding basic questions: what is the fraction of the microbiome that is transmissible? What are the primary mechanisms of transmission? Which organisms are the most highly transmissible? Higher resolution genomic data is required to accurately link microbial sources (such as environmental reservoirs or other individuals) with sinks (such as a single person's microbiome). New computational advances enable strain-level resolution of organisms from shotgun metagenomic data, allowing the transmission of strains to be followed over time and after discrete exposure events. Here, we highlight the latest techniques that reveal strain-level resolution from raw metagenomic reads and new studies that are tracking strains across people and environments. We also propose how models of pathogenic transmission may be applied to study the movement of commensals between microbial communities. Keywords: microbiome; metagenomics; models; biological; strain diversity; genotyping techniques; bacterial genomic

Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes revealed through bacterial Hi-C

Linking antibiotic resistance (AR) in the gut microbiome with their bacterial hosts remains challenging. Here, the authors apply bacterial Hi-C to map mobile genetic elements in metagenomes, and illustrate that genes are present in more diverse taxa in neutropenic patients than healthy subjects

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning

Analyses of metagenomic datasets that are sequenced to a depth of billions or trillions of bases can uncover hundreds of microbial genomes, but naive assembly of these data is computationally intensive, requiring hundreds of gigabytes to terabytes of RAM. We present latent strain analysis (LSA), a scalable, de novo pre-assembly method that separates reads into biologically informed partitions and thereby enables assembly of individual genomes. LSA is implemented with a streaming calculation of unobserved variables that we call eigengenomes. Eigengenomes reflect covariance in the abundance of short, fixed-length sequences, or k-mers. As the abundance of each genome in a sample is reflected in the abundance of each k-mer in that genome, eigengenome analysis can be used to partition reads from different genomes. This partitioning can be done in fixed memory using tens of gigabytes of RAM, which makes assembly and downstream analyses of terabytes of data feasible on commodity hardware. Using LSA, we assemble partial and near-complete genomes of bacterial taxa present at relative abundances as low as 0.00001%. We also show that LSA is sensitive enough to separate reads from several strains of the same species.Rasmussen Family FoundationNational Human Genome Research Institute (U.S.) (Grant U54HG003067)Massachusetts Institute of Technology. Center for Environmental Health SciencesColumbia Earth Institut