Rapid genome shrinkage in a self-fertile nematode reveals sperm competition proteins

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

To reveal impacts of sexual mode on genome content, we compared chromosome-scale assemblies of the outcrossing nematode Caenorhabditis nigoni to its self-fertile sibling species, C. briggsae. C. nigoni’s genome resembles outcrossing relatives, but encodes 31% more protein-coding genes than C. briggsae. C. nigoni genes lacking C. briggsae orthologs were disproportionately small and male-biased in expression. These include the male secreted short (mss) gene family, which encodes sperm surface glycoproteins conserved only in outcrossing species. Sperm from mss-null males of outcrossing C. remanei failed to compete with wild-type sperm, despite normal fertility in non-competitive mating. Restoring mss to C. briggsae males was sufficient to enhance sperm competitiveness. Thus, sex has a pervasive influence on genome content that can be used to identify sperm competition factors.

Related collections

Most cited references 66

Record: found
Abstract: not found
Article: not found

Extraordinary Sex Ratios

W Hamilton (1967)

0 comments Cited 486 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

A new generation of homology search tools based on probabilistic inference.

Sean R. Eddy (2009)

Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.

0 comments Cited 465 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

Mark Gerstein, Zhi John Lu, Eric Van Nostrand … (2010)

We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.