The sperm factor: paternal impact beyond genes

The fact that sperm carry more than the paternal DNA has only been discovered just over a decade ago. With this discovery, the idea that the paternal condition may have direct implications for the fitness of the offspring had to be revisited. While this idea is still highly debated, empirical evidence for paternal effects is accumulating. Male condition not only affects male fertility but also offspring early development and performance later in life. Several factors have been identified as possible carriers of non-genetic information, but we still know little about their origin and function and even less about their causation. I consider four possible non-mutually exclusive adaptive and non-adaptive explanations for the existence of paternal effects in an evolutionary context. In addition, I provide a brief overview of the main non-genetic components found in sperm including DNA methylation, chromatin modifications, RNAs and proteins. I discuss their putative functions and present currently available examples for their role in transferring non-genetic information from the father to the offspring. Finally, I identify some of the most important open questions and present possible future research avenues

DNMT-TET_phmms

These four files contain profile Hidden Markov Models used to identify DNA methyltransferases and TET dioxygenases in insect transcriptomes and genomes. For more information see Provataris P, Meusemann K, Niehuis O, Grath S, Misof B. submitted. Signatures of DNA methylation across insects suggestreduced DNA methylation levels in Holometabola. Genome Biol. Evol

Profile hidden Markov models for the identification of DNMT and TET homologs in insects

Profile hidden Markov models built from DNMT and TET arthropod sequences. Orthologous sequences of each DNMT and TET were downloaded from OrthoDB(http://www.orthodb.org/). 1. DNMT1: DNA methyltransferase 1 2. TRDMT1: tRNA aspartic acid methyltransferase 2 (also known as DNA methyltransferase 2 or DNMT2) 3. DNMT3: DNA methyltransferase 3 4. TET: Ten-eleven translocation methylcytosine dioxygenas

Phmms for insect DNMTs ad TET

Profile hidden Markov models created from DNMT and TET arthropod sequences. Orthologous sequences of each DNMT and TET were downloaded from OrthoDB(http://www.orthodb.org/)

Profile hidden Markov models for the identiication of DNMT and TET homologs in insects

Profile hidden Markov models built from DNMT and TET arthropod sequences. Orthologous sequences of each DNMT and TET were downloaded from OrthoDB(http://www.orthodb.org/). 1. DNMT1: DNA methyltransferase 1 2. TRDMT1: tRNA aspartic acid methyltransferase 2 (also known as DNA methyltransferase 2 or DNMT2) 3. DNMT3: DNA methyltransferase 3 4. TET: Ten-eleven translocation methylcytosine dioxygenas

DNMT-TET_phmms

These four files contain profile Hidden Markov Models used to identify DNA methyltransferases and TET dioxygenases in insect transcriptomes and genomes. For more information see Provataris P, Meusemann K, Niehuis O, Grath S, Misof B. submitted. Signatures of DNA methylation across insects suggestreduced DNA methylation levels in Holometabola. Genome Biol. Evol.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Sawfly genomes reveal evolutionary acquisitions that fostered the mega-radiation of parasitoid and eusocial Hymenoptera

The tremendous diversity of Hymenoptera is commonly attributed to the evolution of parasitoidism in the last common ancestor of parasitoid sawflies (Orussidae) and wasp-waisted Hymenoptera (Apocrita). However, Apocrita and Orussidae differ dramatically in their species richness, indicating that the diversification of Apocrita was promoted by additional traits. These traits have remained elusive due to a paucity of sawfly genome sequences, in particular those of parasitoid sawflies. Here we present comparative analyses of draft genomes of the primarily phytophagous sawfly Athalia rosae and the parasitoid sawfly Orussus abietinus. Our analyses revealed that the ancestral hymenopteran genome exhibited traits that were previously considered unique to eusocial Apocrita (e.g., low transposable element content and activity) and a wider gene repertoire than previously thought (e.g., genes for CO2 detection). Moreover, we discovered that Apocrita evolved a significantly larger array of odorant receptors than sawflies, which could be relevant to the remarkable diversification of Apocrita by enabling efficient detection and reliable identification of hosts