Transcriptional and mutational signatures of the Drosophila ageing germline

Comparative Single Cell Analysis of Transcriptional Bursting Reveals the Role of Genome Organization on de novo Transcript Origination

Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing (scRNA-Seq) studies. By quantifying a surprisingly strong correlation between species-and cell type-specific divergence in three closely related Drosophila species, we apply a simple statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25-30 million years of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time, contrasting with traditional hourglass models of whole-organism development. With these cross-species cell type classifications, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first demonstrate how mechanistic control of pre-meiotic transcription is achieved by altering transcriptional burst size while post-meiotic control is exerted via altered bursting frequency. We then report how global differences in autosomal vs. X chromosomal transcription likely arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of the transcriptional bursting properties of neighboring genes during key stages of spermatogenesis.

Insect Insights at the Single-Cell Level: Technologies and Applications

Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.

Paternal age, risk of congenital anomalies, and birth outcomes: a population-based cohort study

The objective of the study was to explore the impact of paternal age on the risk of congenital anomalies and birth outcomes in infants born in the USA between 2016 and 2021. This retrospective cohort study used data from the National Vital Statistics System (NVSS) database, a data set containing information on live birth in the USA between 2016 and 2021. Newborns were divided into four groups based on their paternal age (< 25, 25-34, 35-44, and > 44 years) and using the 25-34 age group as a reference. The primary outcomes were congenital anomalies involving structural anomalies and chromosome anomalies. Secondary outcomes were preterm birth, low birth weight, severe neonatal perinatal asphyxia, and admission to neonatal intensive care units (NICU). A multivariable logistic regression model was used to analyze the association between paternal age and outcomes. Overall, 17,764,695 live births were included in the final analyses. After adjusting confounding factors, advanced paternal age > 44 years was associated with increased odds of congenital anomalies (adjusted odds ratio (aOR) = 1.17, 95%CI 1.12-1.21) compared with the 25-34 age group, mainly for the chromosomal anomalies (aOR = 1.59, 95%CI 1.40-1.78) but not the structure anomalies (aOR = 1.03, 95%CI 0.97-1.09). The risk of preterm delivery, low birth weight, and NICU hospitalization in their infants was increased by advanced parental age as well.  Conclusion: Advanced paternal age increases the risk of congenital anomalies, especially chromosomal anomalies in their offspring, implying prenatal genetic counseling is required. What is Known: • There's a rising trend of advanced paternal age, which is associated with an increased likelihood of premature birth and low birth weight in their offspring. However, the exploration between paternal age and congenital abnormalities in offspring was limited and contradictory. What is New: • Infants with a paternal age > 44 years were more likely to be born with congenital anomalies, especially chromosomal anomalies.

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.