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bioRxiv2023

Seed coat formation in Arabidopsis requires a concerted action of JUMONJI histone H3K27me3 demethylases and Brassinosteroid signaling

Seed development in angiosperms starts with double fertilization, where two paternal sperm cells fertilize the maternal gametes. This leads to the formation of the embryo and of the endosperm. These fertilization products are enveloped by the maternally-derived seed coat, the development of which is inhibited prior to fertilization by the Polycomb Repressive Complex 2 (PRC2). This complex deposits the repressive histone mark H3K27me3, whose removal is necessary for seed coat formation. Here, we show that JUMONJI-type (JMJ) histone demethylases are expressed in the seed coats of Arabidopsis thaliana (Arabidopsis) and are necessary for its formation. We propose that JMJ activity is coupled to Brassinosteroid (BR) function, as BR effectors physically recruit JMJ proteins to target loci. Consistent with this, we show that loss of BR biosynthesis and signaling leads to seed coat defects, and that loss of the main BR receptor, BRI1, results in H3K27me3 hypermethylation. Moreover, our data points to BRI1 mediating H3K27me3 removal independently of BRs, while a different receptor, BRL3, likely regulates seed coat formation in a BR-dependent manner. We thus propose a model where seed coat development relies on canonical and non-canonical functions of BR receptors.

plant biology↗

bioRxiv2022

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes three unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, and differential microbial network analysis. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all three research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

bioinformatics↗