Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

Expression patterns of Mal genes and association with differential maltose and maltotriose transport rate of two Saccharomyces pastorianus yeasts

Beer brewing is a well-known process that still faces great challenges, such as the total consumption of sugars present in the fermentation media. Lager-style beer, a major worldwide beer type, is elaborated by Saccharomyces pastorianus (Sp) yeast, which must ferment high maltotriose content worts, but its consumption represents a notable problem, especially among Sp strains belonging to group I. Factors, such as fermentation conditions, presence of maltotriose transporters, transporter copy number variation, and genetic regulation variations contribute to this issue. We assess the factors affecting fermentation in two Sp yeast strains: SpIB1, with limited maltotriose uptake, and SpIB2, known for efficient maltotriose transport. Here, SpIB2 transported significantly more maltose (28%) and maltotriose (32%) compared with SpIB1. Furthermore, SpIB2 expressed all MAL transporters (ScMALx1, SeMALx1, ScAGT1, SeAGT1, MTT1, and MPHx) on the first day of fermentation, whereas SpIB1 only exhibited ScMalx1, ScAGT1, and MPH2/3 genes. Some SpIB2 transporters had polymorphic transmembrane domains (TMD) resembling MTT1, accompanied by higher expression of these transporters and its positive regulator genes, such as MAL63. These findings suggest that, in addition to the factors mentioned above, positive regulators of Mal transporters contribute significantly to phenotypic diversity in maltose and maltotriose consumption among the studied lager yeast strains.IMPORTANCEBeer, the third most popular beverage globally with a 90% market share in the alcoholic beverage industry, relies on Saccharomyces pastorianus (Sp) strains for lager beer production. These strains exhibit phenotypic diversity in maltotriose consumption, a crucial process for the acceptable organoleptic profile in lager beer. This diversity ranges from Sp group II strains with a notable maltotriose-consuming ability to Sp group I strains with limited capacity. Our study highlights that differential gene expression of maltose and maltotriose transporters and its upstream trans-elements, such as MAL gene-positive regulators, adds complexity to this variation. This insight can contribute to a more comprehensive analysis needed to the development of controlled and efficient biotechnological processes in the beer brewing industry.

The mechanisms to dispose of misfolded proteins in the endoplasmic reticulum of adipocytes

Endoplasmic reticulum (ER)-associated degradation (ERAD) and ER-phagy are two principal degradative mechanisms for ER proteins and aggregates, respectively; however, the crosstalk between these two pathways under physiological settings remains unexplored. Using adipocytes as a model system, here we report that SEL1L-HRD1 protein complex of ERAD degrades misfolded ER proteins and limits ER-phagy and that, only when SEL1L-HRD1 ERAD is impaired, the ER becomes fragmented and cleared by ER-phagy. When both are compromised, ER fragments containing misfolded proteins spatially coalesce into a distinct architecture termed Coalescence of ER Fragments (CERFs), consisted of lipoprotein lipase (LPL, a key lipolytic enzyme and an endogenous SEL1L-HRD1 substrate) and certain ER chaperones. CERFs enlarge and become increasingly insoluble with age. Finally, we reconstitute the CERFs through LPL and BiP phase separation in vitro, a process influenced by both redox environment and C-terminal tryptophan loop of LPL. Hence, our findings demonstrate a sequence of events centered around SEL1L-HRD1 ERAD to dispose of misfolded proteins in the ER of adipocytes, highlighting the profound cellular adaptability to misfolded proteins in the ER in vivo.

Propagation, detection and correction of errors using the sequence database network

Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.

Tissue-specific transcriptome profiles identify functional differences key to understanding whole plant response to life in variable salinity

Plants endure environmental stressors via adaptation and phenotypic plasticity. Studying these mechanisms in seagrasses is extremely relevant as they are important primary producers and functionally significant carbon sinks. These mechanisms are not well understood at the tissue level in seagrasses. Using RNA-seq, we generated transcriptome sequences from tissue of leaf, basal leaf meristem and root organs of Posidonia australis, establishing baseline in situ transcriptomic profiles for tissues across a salinity gradient. Samples were collected from four P. australis meadows growing in Shark Bay, Western Australia. Analysis of gene expression showed significant differences between tissue types, with more variation among leaves than meristem or roots. Gene ontology enrichment analysis showed the differences were largely due to the role of photosynthesis, plant growth and nutrient absorption in leaf, meristem and root organs, respectively. Differential gene expression of leaf and meristem showed upregulation of salinity regulation processes in higher salinity meadows. Our study highlights the importance of considering leaf meristem tissue when evaluating whole-plant responses to environmental change.

This article has an associated First Person interview with the first author of the paper.

Summary: Differences in seagrass leaf, meristem and root transcriptomes across variable salinities are due to tissue-specific processes. Leaf meristem contained the broadest process range, indicating preferential use for inferring plant-wide activity.

The emerging potential of microbiome transplantation on human health interventions

The human microbiome has been the subject of intense research over the past few decades, in particular as a promising area for new clinical interventions. The microbiota colonizing the different body surfaces are of benefit for multiple physiological and metabolic processes of the human host and increasing evidence suggests an association between disturbances in the composition and functionality of the microbiota and several pathological conditions. This has provided a rationale for beneficial modulation of the microbiome. One approach being explored for modulating the microbiota in diseased individuals is transferring microbiota or microbiota constituents from healthy donors via microbiome transplantation. The great success of fecal microbiome transplantation for the treatment of Clostridioides difficile infections has encouraged the application of this procedure for the treatment of other diseases such as vaginal disorders via transplantation of vaginal microbiota, or of skin pathologies via the transplantation of skin microbiota. Microbiome modulation could even become a novel strategy for improving the efficacy of cancer therapies. This review discusses the principle, advantages and limitations of microbiome transplantation as well as different clinical contexts where microbiome transplantation has been applied.

The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction

Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from many unlabeled sequences. Such embeddings incorporate contextual information of amino acids, thereby modeling the underlying principles of protein sequences insensitive to the context of species. We used an existing pre-trained protein embedding method and subjected its molecular function prediction performance to detailed characterization, first to advance the understanding of protein language models, and second to determine areas of improvement. Then, we applied the model in a transfer learning task by training a function predictor based on the embeddings of annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalizes knowledge about protein function from one eukaryotic species to various other species, outperforming both an alignment-based and a supervised-learning-based baseline. This implies that such a method could be effective for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms.

Systematic analysis of the lysine malonylome in Sanghuangporus sanghuang

Background

Sanghuangporus sanghuang is a well-known traditional medicinal mushroom associated with mulberry. Despite the properties of this mushroom being known for many years, the regulatory mechanisms of bioactive compound biosynthesis in this medicinal mushroom are still unclear. Lysine malonylation is a posttranslational modification that has many critical functions in various aspects of cell metabolism. However, at present we do not know its role in S. sanghuang. In this study, a global investigation of the lysine malonylome in S. sanghuang was therefore carried out.

Results

In total, 714 malonyl modification sites were matched to 255 different proteins. The analysis indicated that malonyl modifications were involved in a wide range of cellular functions and displayed a distinct subcellular localization. Bioinformatics analysis indicated that malonylated proteins were engaged in different metabolic pathways, including glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid (TCA) cycle. Notably, a total of 26 enzymes related to triterpene and polysaccharide biosynthesis were found to be malonylated, indicating an indispensable role of lysine malonylation in bioactive compound biosynthesis in S. sanghuang.

Conclusions

These findings suggest that malonylation is associated with many metabolic pathways, particularly the metabolism of the bioactive compounds triterpene and polysaccharide. This paper represents the first comprehensive survey of malonylation in S. sanghuang and provides important data for further study on the physiological function of lysine malonylation in S. sanghuang and other medicinal mushrooms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08120-0.

Accurate annotation of protein coding sequences with IDTAXA

The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA's utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review

Deciphering the relationship between human proteins (genes) and phenotypes is one of the fundamental tasks in phenomics research. The Human Phenotype Ontology (HPO) builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic causes. To date, many computational methods have been proposed to predict the HPO annotations of proteins. In this paper, we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins, identifying missing HPO annotations, and prioritizing candidate proteins with respect to a certain HPO term. For each topic, we first give the formalized description of the problem, and then systematically revisit the published literatures highlighting their advantages and disadvantages, followed by the discussion on the challenges and promising future directions. In addition, we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO misannotations. We believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.

Functions of Essential Genes and a Scale-Free Protein Interaction Network Revealed by Structure-Based Function and Interaction Prediction for a Minimal Genome

When the JCVI-syn3.0 genome was designed and implemented in 2016 as the minimal genome of a free-living organism, approximately one-third of the 438 protein-coding genes had no known function. Subsequent refinement into JCVI-syn3A led to inclusion of 16 additional protein-coding genes, including several unknown functions, resulting in an improved growth phenotype. Here, we seek to unveil the biological roles and protein-protein interaction (PPI) networks for these poorly characterized proteins using state-of-the-art deep learning contact-assisted structure prediction, followed by structure-based annotation of functions and PPI predictions. Our pipeline is able to confidently assign functions for many previously unannotated proteins such as putative vitamin transporters, which suggest the importance of nutrient uptake even in a minimized genome. Remarkably, despite the artificial selection of genes in the minimal syn3 genome, our reconstructed PPI network still shows a power law distribution of node degrees typical of naturally evolved bacterial PPI networks. Making use of our framework for combined structure/function/interaction modeling, we are able to identify both fundamental aspects of network biology that are retained in a minimal proteome and additional essential functions not yet recognized among the poorly annotated components of the syn3.0 and syn3A proteomes.