DOI: 10.5061/dryad.r2280gbj2
title: Visual opsin gene expression evolution in the adaptive radiation of cichlid fishes of Lake Tanganyika

description: Tuning the visual sensory system to the ambient light is essential for
 survival in many animal species. This is often achieved through
 duplication, functional diversification, and/or differential expression of
 visual opsin genes. Here, we examined 753 new retinal transcriptomes from
 112 species of cichlid fishes from Lake Tanganyika to unravel adaptive
 changes in gene expression at the macro-evolutionary and ecosystem level
 of one of the largest vertebrate adaptive radiations. We found that,
 across the radiation, all seven cone opsins – but not the rhodopsin – rank
 among the most differentially expressed genes in the retina, together with
 other vision-, circadian-rhythm-, and haemoglobin-related genes. We
 propose two new visual palettes characteristic of very shallow- and
 deep-water living species, respectively, and show that visual system
 adaptations along two major ecological axes, macro-habitat and diet, occur
 primarily via gene expression variation in a subset of cone opsin genes.
description: In this study, we sequenced 753 new retinal
 transcriptomes of 112 cichlid species from African Lake Tanganyika to
 (<em>i</em>) identify adaptive changes in the expression of
 rod and cone visual opsin genes, (<em>ii</em>) define and
 reconstruct the evolution of visual palettes on the basis of cone opsin
 expression levels, and (<em>iii</em>) examine rod and cone
 opsin expression levels in relation to macro-habitat, diet, and relative
 eye size. Sampling of fish eyes was
 performed between 2014 and 2020 at 44 locations at Lake Tanganyika
 covering the entire north-south axis of this approximately 670 km long
 lake. For each specimen, the retina of a single eye was dissected,
 homogenised (FastPrep-24; MP Biomedicals), and the total RNA was extracted
 using the Direct-zol RNA kit (Zymo) according to the manufacturer’s
 protocol. Individual libraries were constructed using the Illumina TruSeq
 stranded protocol including RiboZero Gold rRNA depletion (Illumina) and
 sequenced on an Illumina NovaSeq 6000 in PE 100-bp mode. Library
 construction and sequencing were conducted at the Genomics Facility Basel,
 which is jointly operated by the University of Basel and the Department of
 Biosystems Science and Engineering (D-BSSE) of ETH Zurich. Quality
 filtering and adapter removal of Illumina strand-specific paired-end
 sequences were performed using Trimmomatic (v. 0.39) with a 4-bp window
 size, a required window quality of 15, and 80 bp as minimum read length.
 Cleaned reads were mapped against the Nile tilapia reference genome
 (<em>Oreochromis niloticus</em>; RefSeq accession
 GCF_001858045.2, female), which is phylogenetically equidistant to all
 members of the cichlid adaptive radiation in Lake Tanganyika (that is, all
 species in our dataset except <em>O. tanganicae</em> and
 <em>Tylochromis polylepis</em>), using STAR (v. 2.7.3a) with
 --outFilterMultimapNmax 1 --outFilterMatchNminOverLread 0.4
 --outFilterScoreMinOverLread 0.4. To obtain a reasonable estimate of
 mapped reads to exonic features, we filtered out mapped singletons and
 then assigned and counted read pairs within exons using the HTSeq-count
 script from the HTSeq framework (v. 0.11.2). Due to the high gene sequence
 similarity between <em>RH2Aa</em> and
 <em>RH2Ab</em> (percent sequence identity of exons between
 94.81% and 99.40%), we retrieved all read pairs exactly mapping to both
 features and corrected the read count by assigning the remaining read
 pairs to <em>RH2Aa</em> and <em>RH2Ab</em>
 according to their original ratio. The total number of exonic features
 retrieved by HTSeq-count was 41’945 (out of 42’622 annotated genes). We
 filtered the read count dataset to retain both protein-coding RNAs and
 lncRNAs (long non-coding RNAs). This resulted in 38’228 RNAs, from which
 we excluded 9’226 lowly expressed genes (&lt;= five counts in less
 than three samples). The final read count dataset used for the subsequent
 analyses included 29’002 RNAs from which 25’335 were protein-coding RNAs
 (out of 29’532 exonic feature) and 3’667 were lncRNAs (out of 8’696 exonic
 features).
description: A GitHub reposit is available for further information: https://github.com/Ninet93/RNASeq_Ricci_et_al