Identification of long noncoding RNAs (lncRNAs) and co-transcriptional analysis of mRNAs and lncRNAs in transcriptomes of Anopheles gambiae

Jiannong Xu ^1* Kai Hu ^2* Michelle Marie Riehle ^3* Vedbar Singh Khadka ⁴

• ¹ Department of Biology, New Mexico State University, Las Cruces, United States
• ² Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States
• ³ Department of Microbiology & Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
• ⁴ Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Mānoa, Honolulu, Hawaii, United States

Anopheles gambiae is a primary malaria vector mosquito in Africa. RNA-seq based transcriptome analysis has been widely used to study gene expressions underlying mosquito life traits such as development, reproduction, immunity, metabolism, and behavior. While it is widely appreciated that long non-coding RNAs (lncRNAs) are expressed ubiquitously in transcriptomes across metazoans, lncRNAs remain relatively underexplored in An. gambiae, including their identity, expression profiles, and biological functions. The lncRNA genes were poorly annotated in the current reference of the PEST genome of An. gambiae. In this study, a set of publicly available RNA-seq datasets was leveraged to identify lncRNAs across diverse contexts, including whole mosquitoes, mosquito cells or tissues including hemocytes, midguts, and salivary glands, as well as under different physiological conditions including sugar-feeding, blood-feeding, bacterial challenges, and Plasmodium infections. A Transcript Discovery module implemented in CLC genomics workbench was used to identify lncRNAs from selected published RNA-seq datasets. Across this pool of transcriptomes, 2684 unique lncRNA genes, comprising 4082 transcripts, were identified. Following their identification, these lncRNA genes were integrated into the mosquito transcriptome annotation, which was then used as a reference to analyze both mRNAs and lncRNAs for transcriptional dynamics in different conditions. Unsurprisingly and similar to what has been reported for mRNAs, lncRNAs exhibited context-dependent expression patterns. Co-expression networks constructed using weighted gene co-expression network analysis (WGCNA) highlighted the interconnections among lncRNAs and mRNAs, which provides potential functional networks in which these lncRNAs are involved. Furthermore, we identified polysome-associated lncRNAs within polysome-captured transcripts, suggesting the involvement of lncRNAs in translation regulation and coding capacity for micropeptides. The analysis of a ChIP-seq dataset unveiled a correlation of transcriptional activities between lncRNAs and observed epigenetic signatures. Overall, our study demonstrated that lncRNAs are transcribed alongside mRNAs in various biological contexts. The genome-wide annotation of lncRNA genes and integration into the PEST reference genome enables the co-analysis of mRNA and lncRNA simultaneously, which will enhance our understanding of their functions, shedding light on their regulatory roles in An. gambiae biology.