Chu, C. P. | Search bioRxiv

bioRxiv2024

The sugar beet root maggot, Tetanops myopaeformis transcriptome sample statistics in relation to it experiencing susceptible and resistant reactions with sugar beet, Beta vulgaris ssp. vulgaris

The sugar beet root maggot (SBRM), Tetanops myopaeformis (von Roder), is a devastating insect pathogen of sugar beet (SB), Beta vulgaris ssp, vulgaris (B. vulgaris), one of only two plants in the world from which significant global raw sugar is produced, $1 billion, U.S., $4.6 B, globally. Experiments reveal the SBRM larval transcriptome experiencing two different susceptible or resistant responses by sugar beet SBRM larvae were sampled at time = 0 hours post infection [hpi]), prior to being introduced to B. vulgaris and after infection on F1016 and F1024 (resistant), and F1010 and L19 (susceptible) for 24, 48, and 72 hpi when the larvae were removed for transcriptomic analysis. The transcriptomic analyses included determining the number of reads per sample, mapping the transcripts to the recently sequenced SBRM TmSBRM_v1.0 draft genome, identifying genes that relate to the resistant and susceptible responses. Moreover, the RNA-seq experiments provide data for generating differential expression analyses between the various sample types, thus, yielding an understanding SBRM biology, the development of new control strategies for this pathogen, relationship to model genetic organisms like Drosophila melanogaster, relationship to pathogenic non-model organisms, and aid in agronomic improvement of sugar beet for stakeholders.

genomics↗

bioRxiv2024

Limits to the inference of gene regulation from bulk tissue expression data

MotivationThousands of studies have used co-expression analysis of bulk tissue samples to probe gene regulation. However, the extent that intracellular regulatory signals are present in these data is unclear. Specifically, we lack clarity of the factors that promote or impede the propagation of intracellular regulatory signals from the single cell level to the bulk tissue level. To bring these issues into focus, we developed a novel computational simulator, grounded in real data, to explore the theoretical relationship between events in single cells and bulk tissue expression profiles, and clarify the conditions required for the propagation of intracellular regulatory signals in complex tissues such as the brain. ResultsOur simulator first generates single cell expression profiles and subsequently samples and aggregates these single cells to produce bulk tissue expression profiles. Using this framework, we found that there are very specific and unlikely conditions under which intracellular dynamic regulatory signals can be propagated to the bulk tissue level. For the most part, such regulatory relationships, however strong at the single cell level, are unlikely to be detectable. Our results provide a quantitative explanation for why regulatory network inference from co-expression has proved challenging - even with the assistance of other data modalities - and gives the scientific community a set of tools to further explore these issues in both single-cell and bulk tissue data. Availability and implementationAll relevant data are within the manuscript and supplementary files. The code for all data analyses and generation of figures are available on GitHub (https://github.com/PavlidisLab/coex-simulation). A copy of the data has been deposited in Borealis, the Canadian Dataverse Repository (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/2CWXY6).

bioinformatics↗