A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6

Decoding functional proteome information in model organisms using protein language models

Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning-based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein language models are more precise and informative than deep learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomic experiments. The results obtained indicate that these language models are likely to be suitable for large-scale annotation and downstream analyses, and we recommend a guide for their use.

BRANEnet: embedding multilayer networks for omics data integration

Background

Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.

Results

We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04955-w.

Diversification of heat shock transcription factors expanded thermal stress responses during early plant evolution

The copy numbers of many plant transcription factor (TF) genes substantially increased during terrestrialization. This allowed TFs to acquire new specificities and thus create gene regulatory networks (GRNs) with new biological functions to help plants adapt to terrestrial environments. Through characterizing heat shock factor (HSF) genes MpHSFA1 and MpHSFB1 in the liverwort Marchantia polymorpha, we explored how heat-responsive GRNs widened their functions in M. polymorpha and Arabidopsis thaliana. An interspecies comparison of heat-induced transcriptomes and the evolutionary rates of HSFs demonstrated the emergence and subsequent rapid evolution of HSFB prior to terrestrialization. Transcriptome and metabolome analyses of M. polymorpha HSF-null mutants revealed that MpHSFA1 controls canonical heat responses such as thermotolerance and metabolic changes. MpHSFB1 also plays essential roles in heat responses, as well as regulating developmental processes including meristem branching and antheridiophore formation. Analysis of cis-regulatory elements revealed development- and stress-related TFs that function directly or indirectly downstream of HSFB. Male gametophytes of M. polymorpha showed higher levels of thermotolerance than female gametophytes, which could be explained by different expression levels of MpHSFA1U and MpHSFA1V on sex chromosome. We propose that the diversification of HSFs is linked to the expansion of HS responses, which enabled coordinated multicellular reactions in land plants.

Effect of overexpression of SNF1 on the transcriptional and metabolic landscape of baker's yeast under freezing stress

Background

Freezing stress is the key factor that affecting the cell activity and fermentation performance of baker’s yeast in frozen dough production. Generally, cells protect themselves from injury and maintain metabolism by regulating gene expression and modulating metabolic patterns in stresses. The Snf1 protein kinase is an important regulator of yeast in response to stresses. In this study, we aim to study the role of the catalytic subunit of Snf1 protein kinase in the cell tolerance and dough leavening ability of baker’s yeast during freezing. Furthermore, the effects of SNF1 overexpression on the global gene expression and metabolite profile of baker’s yeast before and after freezing were analysed using RNA-sequencing and untargeted UPLC − QTOF-MS/MS, respectively.

Results

The results suggest that overexpression of SNF1 was effective in enhancing the cell tolerance and fermentation capacity of baker’s yeast in freezing, which may be related to the upregulated proteasome, altered metabolism of carbon sources and protectant molecules, and changed cell membrane components. SNF1 overexpression altered the level of leucin, proline, serine, isoleucine, arginine, homocitrulline, glycerol, palmitic acid, lysophosphatidylcholine (LysoPC), and lysophosphatidylethanolamine (LysoPE) before freezing, conferring cells resistance in freezing. After freezing, relative high level of proline, lysine, and glycerol maintained by SNF1 overexpression with increased content of LysoPC and LysoPE.

Conclusions

This study will increase the knowledge of the cellular response of baker’s yeast cells to freezing and provide new opportunities for the breeding of low-temperature resistant strains.