Zawisza-Alvarez, M. | Search bioRxiv

bioRxiv2026

Elucidating Bat SuperImmunity: Insights from the Bent-Wing Bat Genome Assembly and Multi-Omics Analysis

Bats have evolved a range of unique adaptations, including powered flight, echolocation, and exceptional longevity. They have also attracted considerable scientific attention, particularly since the COVID-19 pandemic, due to their association with a broad diversity of viruses and their potential role in zoonotic transmission and emerging infectious diseases. Despite hosting numerous viruses, bats often exhibit limited clinical manifestations of infection, suggesting the evolution of distinctive mechanisms of immune regulation and tolerance. Understanding the molecular basis of these mechanisms has become a major focus of current research, with genome-wide analyses providing important insights into the genetic adaptations underlying bat immunity. Nevertheless, the regulatory complexity of bat genomes, particularly the contribution of non-coding RNAs and post-transcriptional RNA editing, remains comparatively underexplored. Here, we present an integrated multi-omic framework for the common bent-wing bat (Miniopterus schreibersii), combining a contiguous chromosome-scale genome assembly with multi-tissue transcriptomic profiling, long non-coding RNA (lncRNA) identification and synteny analysis, endogenous viral elements (EVE) mapping, and same-individual RNA editomic analyses. This study provides a comprehensive resource for investigating genome organization, transcriptional regulation, evolutionary relationships, and post-transcriptional modifications in this species. In particular, it offers an opportunity to explore the potential contribution of non-coding regulatory elements and RNA editing to the molecular mechanisms underlying viral interactions and immune regulation in bats. Collectively, these resources provide a valuable foundation for advancing our understanding of bat genome evolution and the regulatory mechanisms potentially associated with their distinctive responses to viral infections.

genomics↗

bioRxiv2023

Exploring functional conservation in silico: a new machine learning approach to RNA-editing

Around 50 years from now, molecular biology opened the path to understand changes in forms, adaptations, complexity, or the basis of human diseases, through myriads of reports on gene birth, gene duplication, gene expression regulation, and splicing regulation, among other relevant mechanisms behind gene function. Here, with the advent of big data and artificial intelligence (AI), we focus on an elusive and intriguing mechanism of gene function regulation, RNA editing, in which a single nucleotide from an RNA molecule is changed with a remarkable impact in the increase of the complexity of transcriptome and proteome. We present a new generation approach to assess the functional conservation of the RNA-editing targeting mechanism using two AI learning algorithms, random forest (RF) and bidirectional long short-term memory (biLSTM) neural networks with attention layer. These algorithms combined with RNA-editing data coming from databases and variant calling from same-individual RNA and DNA-seq experiments from different species, allowed us to predict RNA-editing events using both primary sequence and secondary structure. Then, we devised a method for assessing conservation or divergence in the molecular mechanisms of editing completely in silico: the cross-training analysis. This novel method not only helps to understand the conservation of the editing mechanism through evolution but could set the basis for understanding how it is involved in several human diseases.

genetics↗