The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes

The decision for or against mycoparasitic attack by Trichoderma spp. is taken already at a distance in a prey-specific manner and benefits plant-beneficial interactions

BACKGROUND

The application of plant-beneficial microorganisms as bio-fertilizer and biocontrol agents has gained traction in recent years, as both agriculture and forestry are facing the challenges of poor soils and climate change. Trichoderma spp. are gaining popularity in agriculture and forestry due to their multifaceted roles in promoting plant growth through e.g. nutrient translocation, hormone production, induction of plant systemic resistance, but also direct antagonism of other fungi. However, the mycotrophic nature of the genus bears the risk of possible interference with other native plant-beneficial fungi, such as ectomycorrhiza, in the rhizosphere. Such interference could yield unpredictable consequences for the host plants of these ecosystems. So far, it remains unclear, whether Trichoderma is able to differentiate between plant-beneficial and plant-pathogenic fungi during the process of plant colonization.

RESULTS

We investigated whether Trichoderma spp. can differentiate between beneficial ectomycorrhizal fungi (represented by Laccaria bicolor and Hebeloma cylindrosporum) and pathogenic fungi (represented by Fusarium graminearum and Alternaria alternata) in different confrontation scenarios, including a newly developed olfactometer "race tube"-like system. Using two independent species, T. harzianum and T. atrobrunneum, with plant-growth-promoting and immune-stimulating properties towards Populus x canescens, our study revealed robustly accelerated growth towards phytopathogens, while showing a contrary response to ectomycorrhizal fungi. Transcriptomic analyses identified distinct genetic programs during interaction corresponding to the lifestyles, emphasizing the expression of mycoparasitism-related genes only in the presence of phytopathogens.

CONCLUSION

The findings reveal a critical mode of fungal community interactions belowground and suggest that Trichoderma spp. can distinguish between fungal partners of different lifestyles already at a distance. This sheds light on the entangled interactions of fungi in the rhizosphere and emphasizes the potential benefits of using Trichoderma spp. as a biocontrol agent and bio-fertilizer in tree plantations.

Fungal biofilm formation and its regulatory mechanism

Fungal biofilm is a microbial community composed of fungal cells and extracellular polymeric substances (EPS). In recent years, fungal biofilms have played an increasingly important role in many fields. However, there are few studies on fungal biofilms and their related applications and development are still far from enough. Therefore, this review summarizes the composition and function of EPS in fungal biofilms, and improves and refines the formation process of fungal biofilms according to the latest viewpoints. Moreover, based on the study of Saccharomyces cerevisiae and Candida albicans, this review summarizes the gene regulation network of fungal biofilm synthesis, which is crucial for systematically understanding the molecular mechanism of fungal biofilm formation. It is of great significance to further develop effective methods at the molecular level to control harmful biofilms or enhance and regulate the formation of beneficial biofilms. Finally, the quorum sensing factors and mixed biofilms formed by fungi in the current research of fungal biofilms are summarized. These results will help to deepen the understanding of the formation process and internal regulation mechanism of fungal biofilm, provide reference for the study of EPS composition and structure, formation, regulation, group behavior and mixed biofilm formation of other fungal biofilms, and provide strategies and theoretical basis for the control, development and utilization of fungal biofilms.

An oxylipin signal confers protection against antifungal echinocandins in pathogenic aspergilli

Aspergillus fumigatus is the leading causative agent of life-threatening invasive aspergillosis in immunocompromised individuals. One antifungal class used to treat Aspergillus infections is the fungistatic echinocandins, semisynthetic drugs derived from naturally occurring fungal lipopeptides. By inhibiting beta-1,3-glucan synthesis, echinocandins cause both fungistatic stunting of hyphal growth and repeated fungicidal lysis of apical tip compartments. Here, we uncover an endogenous mechanism of echinocandin tolerance in A. fumigatus whereby the inducible oxylipin signal 5,8-diHODE confers protection against tip lysis via the transcription factor ZfpA. Treatment of A. fumigatus with echinocandins induces 5,8-diHODE synthesis by the fungal oxygenase PpoA in a ZfpA dependent manner resulting in a positive feedback loop. This protective 5,8-diHODE/ZfpA signaling relay is conserved among diverse isolates of A. fumigatus and in two other Aspergillus pathogens. Our findings reveal an oxylipin-directed growth program-possibly arisen through natural encounters with native echinocandin producing fungi-that enables echinocandin tolerance in pathogenic aspergilli.

Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome

In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.

Zinc Starvation Induces Cell Wall Remodeling and Activates the Antioxidant Defense System in Fonsecaea pedrosoi

The survival of pathogenic fungi in the host after invasion depends on their ability to obtain nutrients, which include the transition metal zinc. This essential micronutrient is required to maintain the structure and function of various proteins and, therefore, plays a critical role in various biological processes. The host's nutritional immunity limits the availability of zinc to pathogenic fungi mainly by the action of calprotectin, a component of neutrophil extracellular traps. Here we investigated the adaptive responses of Fonsecaea pedrosoi to zinc-limiting conditions. This black fungus is the main etiological agent of chromoblastomycosis, a chronic neglected tropical disease that affects subcutaneous tissues. Following exposure to a zinc-limited environment, F. pedrosoi induces a high-affinity zinc uptake machinery, composed of zinc transporters and the zincophore Pra1. A proteomic approach was used to define proteins regulated by zinc deprivation. Cell wall remodeling, changes in neutral lipids homeostasis, and activation of the antioxidant system were the main strategies for survival in the hostile environment. Furthermore, the downregulation of enzymes required for sulfate assimilation was evident. Together, the adaptive responses allow fungal growth and development and reveals molecules that may be related to fungal persistence in the host.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

Mycoparasitism related targets of Tmk1 indicate stimulating regulatory functions of this MAP kinase in Trichoderma atroviride

Mycoparasitism is a key feature of Trichoderma (Hypocreales, Ascomycota) biocontrol agents. Recent studies of intracellular signal transduction pathways of the potent mycoparasite Trichoderma atroviride revealed the involvement of Tmk1, a mitogen-activated protein kinase (MAPK), in triggering the mycoparasitic response. We previously showed that mutants missing Tmk1 exhibit reduced mycoparasitic activity against several plant pathogenic fungi. In this study, we identified the most robustly regulated targets that were governed by Tmk1 during mycoparasitism using transcriptome and proteome profiling. Tmk1 mainly exerts a stimulating function for T. atroviride during its mycoparasitic interaction with the fungal plant pathogen Rhizoctonia solani, as reflected by 89% of strongly differently responding genes in the ∆tmk1 mutant compared to the wild type. Specifically, 54% of these genes showed strong downregulation in the response with a deletion of the tmk1 gene, whereas in the wild type the same genes were strongly upregulated during the interaction with the fungal host. These included the gene encoding the mycoparasitism-related proteinase Prb1; genes involved in signal transduction pathways such as a candidate coding for a conserved 14-3-3 protein, and a gene coding for Tmk2, the T. atroviride cell-wall integrity MAP kinase; genes encoding a specific siderophore synthetase, and multiple FAD-dependent oxidoreductases and aminotransferases. Due to the phosphorylating activity of Tmk1, different (phospho-)proteomics approaches were applied and identified proteins associated with cellular metabolism, energy production, protein synthesis and fate, and cell organization. Members of FAD- and NAD/NADP-binding-domain proteins, vesicular trafficking of molecules between cellular organelles, fungal translational, as well as protein folding apparatus were among others found to be phosphorylated by Tmk1 during mycoparasitism. Outstanding downregulation in the response of the ∆tmk1 mutant to the fungal host compared to the wild type at both the transcriptome and the proteome levels was observed for nitrilase, indicating that its defense and detoxification functions might be greatly dependent on Tmk1 during T. atroviride mycoparasitism. An intersection network analysis between the identified transcripts and proteins revealed a strong involvement of Tmk1 in molecular functions with GTPase and oxidoreductase activity. These data suggest that during T. atroviride mycoparasitism this MAPK mainly governs processes regulating cell responses to extracellular signals and those involved in reactive oxygen stress.

Proteomic identification of metabolic changes in Paracoccidioides brasiliensis induced by a nitroheteroarylchalcone

Aim: To access the metabolic changes caused by a chalcone derivative (LabMol-75) through a proteomic approach. Methods: Proteomic analysis was performed after 9 h of Paracoccidioides brasiliensis yeast (Pb18) cell incubation with the LabMol-75 at MIC. The proteomic findings were validated through in vitro and in silico assays. Results: Exposure to the compound led to the downregulation of proteins associated with glycolysis and gluconeogenesis, β-oxidation, the citrate cycle and the electron transport chain. Conclusion: LabMol-75 caused an energetic imbalance in the fungus metabolism and deep oxidative stress. Additionally, the in silico molecular docking approach pointed to this molecule as a putative competitive inhibitor of DHPS.

Interactions between the transcription factors FfmA and AtrR are required to properly regulate gene expression in the fungus Aspergillus fumigatus

Transcriptional regulation of azole resistance in the filamentous fungus Aspergillus fumigatus is a key step in development of this problematic clinical phenotype. We and others have previously described a C2H2-containing transcription factor called FfmA that is required for normal levels of voriconazole susceptibility. Null alleles of ffmA exhibit a strongly compromised growth rate even in the absence of any external stress. Here, we employ an acutely repressible doxycycline-off form of ffmA to rapidly deplete FfmA protein from the cell. Using this approach, we carried out RNA-seq analyses to probe the transcriptome cells acutely deprived of FfmA. A total of 2,000 genes were differentially expressed upon acute depletion of FfmA, illustrating the broad transcriptomic effect of this factor. Interestingly, the transcriptome changes observed upon this acute depletion of FfmA expression only shared limited overlap with those found in an ffmAΔ null strain analyzed by others. Chromatin immunoprecipitation coupled with high throughput DNA sequencing analysis (ChIP-seq) identified 530 genes that were bound by FfmA. More than 300 of these genes were also bound by AtrR, a transcription factor important in azole drug resistance, demonstrating striking regulatory overlap with FfmA. However, while AtrR is an upstream activation protein with known specificity, our data suggest that FfmA is a chromatin-associated factor that binds DNA in a manner dependent on other factors. We provide evidence that AtrR and FfmA interact in the cell and show reciprocal expression modulation. Interaction of AtrR and FfmA is required for normal gene expression in A. fumigatus.

Experimental verification of strain-dependent relationship between mycovirus and its fungal host

Mycoviruses are viruses that infect fungi. Unlike mammalian infectious viruses, their life cycle does not generally have an extracellular stage, and a symbiosis-like relationship is maintained between virus and host fungi. Recently, mycoviruses have been reported to show effects on host fungi, altering biological properties such as growth rate, virulence, drug resistance, and metabolite production. In this study, we systematically elucidated the effects of viruses on host cells by comparing host phenotypes and transcriptomic responses in multiple sets of virus-infected and -eliminated Aspergillus flavus strains. The comparative study showed that mycoviruses affect several cellular activities at the molecular level in a virus- and host strain-dependent manner. The virus-swapping experiment revealed that difference with only three bases in the virus genome led to different host fungal response at the transcriptional level. Our results highlighted highly specific relationship between viruses and their host fungi.

Fus3/Kss1-MAP kinase and Ste12-like control distinct biocontrol-traits besides regulation of insect cuticle penetration via phosphorylation cascade in a filamentous fungal pathogen

BACKGROUND

Homolog of the yeast Fus3/Kss1 mitogen-activated protein kinase (MAPK) pathway and its target transcription factor, Ste12-like, are involved in penetration of host cuticle/pathogenicity in many ascomycete pathogens. However, details of their interaction during fungal infection, as well as their controlled other virulence-associated traits, are unclear.

RESULTS

Ste12-like (BbSte12) and Fus3/Kss1 MAPK homolog (Bbmpk1) interacted in nucleus, and phosphorylation of BbSte12 by Bbmpk1 was essential for penetration of insect cuticle in an insect fungal pathogen, Beauveria bassiana. However, some distinct biocontrol-traits were found to be mediated by Ste12 and Bbmpk1. In contrast to ΔBbmpk1 colony that grew more rapid than wild-type strain, inactivation of BbSte12 resulted in the opposite phenotype, which was consistent with their different proliferation rates in insect hemocoel after direct injection of conidia bypass the cuticle. Reduced conidial yield with decreased hydrophobicity was examined in both mutants, however they displayed distinct conidiogenesis, accompanying with differently altered cell cycle, distinct hyphal branching and septum formation. Moreover, ΔBbmpk1 showed increased tolerance to oxidative agent, whereas the opposite phenotype was seen for ΔBbSte12 strain. RNA sequencing analysis revealed that Bbmpk1 controlled 356 genes depending on BbSte12 during cuticle penetration, but 1077 and 584 genes were independently controlled by Bbmpk1 and BbSte12.

CONCLUSION

BbSte12 and Bbmpk1 separately participate in additional pathways for control of conidiation, growth and hyphal differentiation, as well as oxidative stress response besides regulating cuticle penetration via phosphorylation cascade. © 2023 Society of Chemical Industry.

Transcription factor FfmA interacts both physically and genetically with AtrR to properly regulate gene expression in the fungus Aspergillus fumigatus

Transcriptional regulation of azole resistance in the filamentous fungus Aspergillus fumigatus is a key step in development of this problematic clinical phenotype. We and others have previously described a C2H2-containing transcription factor called FfmA that is required for normal levels of voriconazole susceptibility and expression of an ATP-binding cassette transporter gene called abcG1 . Null alleles of ffmA exhibit a strongly compromised growth rate even in the absence of any external stress. Here we employ an acutely repressible doxycycline-off form of ffmA to rapidly deplete FfmA protein from the cell. Using this approach, we carried out RNA-seq analyses to probe the transcriptome of A. fumigatus cells that have been deprived of normal FfmA levels. We found that 2000 genes were differentially expressed upon depletion of FfmA, consistent with the wide-ranging effect of this factor on gene regulation. Chromatin immunoprecipitation coupled with high throughput DNA sequencing analysis (ChIP-seq) identified 530 genes that were bound by FfmA using two different antibodies for immunoprecipitation. More than 300 of these genes were also bound by AtrR demonstrating the striking regulatory overlap with FfmA. However, while AtrR is clearly an upstream activation protein with clear sequence specificity, our data suggest that FfmA is a chromatin-associated factor that may bind to DNA in a manner dependent on other factors. We provide evidence that AtrR and FfmA interact in the cell and can influence one another's expression. This interaction of AtrR and FfmA is required for normal azole resistance in A. fumigatus .

Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network

Interaction networks can be used to predict the functions of unknown proteins using known interactions and proteins with known functions. Many graph theory or diffusion-based methods have been proposed, using the assumption that the topological properties of a protein in a network are related to its biological function. Here we seek to improve function prediction by finding more similar neighbors with a new diffusion-based alignment technique to overcome the topological information loss of the node. In this study, we introduce the Diffusion Alignment Coefficient (DAC) algorithm, which combines diffusion, longest common subsequence, and longest common substring techniques to measure the similarity of two nodes in protein interaction networks. As a proof of concept, our experiments, conducted on a real PPI networks S.cerevisiae and Homo Sapiens, demonstrated that our method obtained better results than competitors for MIPS and MSigDB Collections hallmark gene set functional categories. This is the first study to develop a measure of node function similarity using alignment to consider the positions of nodes in protein-protein interaction networks. According to the experimental results, the use of spatial information belonging to the nodes in the network has a positive effect on the detection of more functionally similar neighboring nodes.

Analysis of gene expression related to polyamine concentration and dimorphism induced in ornithine decarboxylase (odc) and spermidine synthase (spd) Ustilago maydis mutants

Polyamines are ubiquitous small organic cations, and their roles as regulators of several cellular processes are widely recognized. They are implicated in the key stages of the fungal life cycle. Ustilago maydis is a phytopathogenic fungus, the causal agent of common smut of maize and a model system to understand dimorphism and virulence. U. maydis grows in yeast form at pH 7 and it can develop its mycelial form in vitro at pH 3. Δodc mutants that are unable to synthesize polyamines, grew as yeast at pH 3 with a low putrescine concentration, and to complete its dimorphic transition high putrescine concentration was required. Δspd mutants required spermidine to grow and cannot form mycelium at pH 3. In this work, the increased expression of the mating genes, mfa1 and mfa2, on Δodc mutants, was related to high putrescine concentration. Global gene expression analysis comparisons of Δodc and Δspd U. maydis mutants indicated that 2,959 genes were differentially expressed in the presence of exogenous putrescine at pH 7 and 475 genes at pH 3. While, in Δspd mutant, the expression of 1,426 genes was affected by exogenous spermine concentration at pH 7 and 11 genes at pH 3. Additionally, we identified 28 transcriptional modules with correlated expression during seven tested conditions: mutant genotype, morphology (yeast, and mycelium), pH, and putrescine or spermidine concentration. Furthermore, significant differences in transcript levels were noted for genes in modules relating to pH and genotype genes involved in ribosome biogenesis, mitochondrial oxidative phosphorylation, N-glycan synthesis, and Glycosylphosphatidylinositol (GPI)-anchor. In summary, our results offer a valuable tool for the identification of potential factors involved in phenomena related to polyamines and dimorphism.

An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus

Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.

Sfl1 is required for Candida albicans biofilm formation under acidic conditions

Candida albicans is a common Candida species, responsible for infections in various anatomical sites under different environmental conditions, aggravated in the presence of its biofilms. As such, this study aimed to reveal the regulation of C. albicans biofilms under acidic conditions by the transcription factor Sfl1, whose role on biofilm formation is unclear. For that, microbiologic and transcriptomic analyses were performed with the knock-out mutant C. albicans sfl1Δ/sfl1Δ and its parental strain SN76, grown in planktonic and biofilm lifestyles at pH 4 (vaginal pH). The results revealed that despite being a filamentation repressor Sf1 is required for maximal biofilm formation under acidic conditions. Additionally, Sfl1 was found to induce 275 and 126 genes in biofilm and planktonic cells, respectively, with an overlap of 19 genes. The functional distribution of Sfl1 targets was similar in planktonic and biofilm modes but an enrichment of carbohydrate metabolism function was found in biofilm cells, including some genes encoding proteins involved in the biofilm matrix production. Furthermore, this study shows that the regulatory network of Sfl1 in acidic biofilms is complex and includes positive and negative regulation of transcription factors involved in adhesion and biofilm formation, such as Ahr1, Brg1, Tye7, Tec1, Wor1, and some of their targets. Overall, this study shows that Sfl1 is a relevant regulator of C. albicans biofilm formation in acidic environments and contributes to a better understanding of C. albicans virulence under acidic conditions.

Genomic, transcriptomic, and metabolic characterization of 2-Phenylethanol-resistant Saccharomyces cerevisiae obtained by evolutionary engineering

2-Phenylethanol is an aromatic compound commonly used in the food, cosmetic, and pharmaceutical industries. Due to increasing demand for natural products by consumers, the production of this flavor by microbial fermentation is gaining interest, as a sustainable alternative to chemical synthesis or expensive plant extraction, both processes relying on the use of fossil resources. However, the drawback of the fermentation process is the high toxicity of 2-phenylethanol to the producing microorganism. The aim of this study was to obtain a 2-phenylethanol-resistant Saccharomyces cerevisiae strain by in vivo evolutionary engineering and characterize the adapted yeast at the genomic, transcriptomic and metabolic levels. For this purpose, the tolerance to 2-phenylethanol was developed by gradually increasing the concentration of this flavor compound through successive batch cultivations, leading to an adapted strain that could tolerate 3.4 g/L of 2-phenylethanol, which was about 3-times better than the reference strain. Genome sequencing of the adapted strain identified point mutations in several genes, notably in HOG1 that encodes the Mitogen-Activated Kinase of the high-osmolarity signaling pathway. As this mutation is localized in the phosphorylation lip of this protein, it likely resulted in a hyperactive protein kinase. Transcriptomic analysis of the adapted strain supported this suggestion by revealing a large set of upregulated stress-responsive genes that could be explained in great part by HOG1-dependent activation of the Msn2/Msn4 transcription factor. Another relevant mutation was found in PDE2 encoding the low affinity cAMP phosphodiesterase, the missense mutation of which may lead to hyperactivation of this enzyme and thereby enhance the stressful state of the 2-phenylethanol adapted strain. In addition, the mutation in CRH1 that encodes a chitin transglycosylase implicated in cell wall remodeling could account for the increased resistance of the adapted strain to the cell wall-degrading enzyme lyticase. Finally, the potent upregulation of ALD3 and ALD4 encoding NAD⁺ -dependent aldehyde dehydrogenase together with the observed phenylacetate resistance of the evolved strain suggest a resistance mechanism involving conversion of 2-phenylethanol into phenylacetaldehyde and phenylacetate implicating these dehydrogenases.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

A circadian clock translational control mechanism targets specific mRNAs to cytoplasmic messenger ribonucleoprotein granules

Phosphorylation of Neurospora crassa eukaryotic initiation factor 2 α (eIF2α), a conserved translation initiation factor, is clock controlled. To determine the impact of rhythmic eIF2α phosphorylation on translation, we performed temporal ribosome profiling and RNA sequencing (RNA-seq) in wild-type (WT), clock mutant Δfrq, eIF2α kinase mutant Δcpc-3, and constitutively active cpc-3c cells. About 14% of mRNAs are rhythmically translated in WT cells, and translation rhythms for ∼30% of these mRNAs, which we named circadian translation-initiation-controlled genes (cTICs), are dependent on the clock and CPC-3. Most cTICs are expressed from arrhythmic mRNAs and contain a P-body (PB) localization motif in their 5' leader sequence. Deletion of SNR-1, a component of cytoplasmic messenger ribonucleoprotein granules (cmRNPgs) that include PBs and stress granules (SGs), and the PB motif on one of the cTIC mRNAs, zip-1, significantly alters zip-1 rhythmic translation. These results reveal that the clock regulates rhythmic translation of specific mRNAs through rhythmic eIF2α activity and cmRNPg metabolism.

IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database

Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.

Genome-Wide Characterization Reveals Variation Potentially Involved in Pathogenicity and Mycotoxins Biosynthesis of Fusarium proliferatum Causing Spikelet Rot Disease in Rice

Fusarium proliferatum is the primary cause of spikelet rot disease in rice (Oryza sativa L.) in China. The pathogen not only infects a wide range of cereals, causing severe yield losses but also contaminates grains by producing various mycotoxins that are hazardous to humans and animals. Here, we firstly reported the whole-genome sequence of F. proliferatum strain Fp9 isolated from the rice spikelet. The genome was approximately 43.9 Mb with an average GC content of 48.28%, and it was assembled into 12 scaffolds with an N₅₀ length of 4,402,342 bp. There is a close phylogenetic relationship between F. proliferatum and Fusarium fujikuroi, the causal agent of the bakanae disease of rice. The expansion of genes encoding cell wall-degrading enzymes and major facilitator superfamily (MFS) transporters was observed in F. proliferatum relative to other fungi with different nutritional lifestyles. Species-specific genes responsible for mycotoxins biosynthesis were identified among F. proliferatum and other Fusarium species. The expanded and unique genes were supposed to promote F. proliferatum adaptation and the rapid response to the host's infection. The high-quality genome of F. proliferatum strain Fp9 provides a valuable resource for deciphering the mechanisms of pathogenicity and secondary metabolism, and therefore shed light on development of the disease management strategies and detoxification of mycotoxins contamination for spikelet rot disease in rice.

MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN

In recent years, a number of computational approaches have been proposed to effectively integrate multiple heterogeneous biological networks, and have shown impressive performance for inferring gene function. However, the previous methods do not fully represent the critical neighborhood relationship between genes during the feature learning process. Furthermore, it is difficult to accurately estimate the contributions of different views for multi-view integration. In this paper, we propose MGEGFP, a multi-view graph embedding method based on adaptive estimation with Graph Convolutional Network (GCN), to learn high-quality gene representations among multiple interaction networks for function prediction. First, we design a dual-channel GCN encoder to disentangle the view-specific information and the consensus pattern across diverse networks. By the aid of disentangled representations, we develop a multi-gate module to adaptively estimate the contributions of different views during each reconstruction process and make full use of the multiplexity advantages, where a diversity preservation constraint is designed to prevent the over-fitting problem. To validate the effectiveness of our model, we conduct experiments on networks from the STRING database for both yeast and human datasets, and compare the performance with seven state-of-the-art methods in five evaluation metrics. Moreover, the ablation study manifests the important contribution of the designed dual-channel encoder, multi-gate module and the diversity preservation constraint in MGEGFP. The experimental results confirm the superiority of our proposed method and suggest that MGEGFP can be a useful tool for gene function prediction.

Majority Vote Cascading: A Semi-Supervised Framework for Improving Protein Function Prediction

A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code and networks for the cross-validation experiments appear at http://bcb.cs.tufts.edu/cascade.

Transcriptomic Responses of Fusarium verticillioides to Lactam and Lactone Xenobiotics

The important cereal crops of maize, rye, and wheat constitutively produce precursors to 2-benzoxazolinone, a phytochemical having antifungal effects towards many Fusarium species. However, Fusarium verticillioides can tolerate 2-benzoxazolinone by converting it into non-toxic metabolites through the synergism of two previously identified gene clusters, FDB1 and FDB2. Inspired by the induction of these two clusters upon exposure to 2-benzoxazolinone, RNA sequencing experiments were carried out by challenging F. verticillioides individually with 2-benzoxazolinone and three related chemical compounds, 2-oxindole, 2-coumaranone, and chlorzoxazone. These compounds all contain lactam and/or lactone moieties, and transcriptional analysis provided inferences regarding the degradation of such lactams and lactones. Besides induction of FDB1 and FDB2 gene clusters, four additional clusters were identified as induced by 2-benzoxazolinone exposure, including a cluster thought to be responsible for biosynthesis of pyridoxine (vitamin B6), a known antioxidant providing tolerance to reactive oxygen species. Three putative gene clusters were identified as induced by challenging F. verticillioides with 2-oxindole, two with 2-coumaranone, and two with chlorzoxazone. Interestingly, 2-benzoxazolinone and 2-oxindole each induced two specific gene clusters with similar composition of enzymatic functions. Exposure to 2-coumranone elicited the expression of the fusaric acid biosynthetic gene cluster. Another gene cluster that may encode enzymes responsible for degrading intermediate catabolic metabolites with carboxylic ester bonds was induced by 2-benzoxazolinone, 2-oxindole, and chlorzoxazone. Also, the induction of a dehalogenase encoding gene during chlorzoxazone exposure suggested its role in the removal of the chlorine atom. Together, this work identifies genes and putative gene clusters responsive to the 2-benzoxazolinone-like compounds with metabolic inferences. Potential targets for future functional analyses are discussed.

Organic acids and glucose prime late-stage fungal biotrophy in maize

Many plant-associated fungi are obligate biotrophs that depend on living hosts to proliferate. However, little is known about the molecular basis of the biotrophic lifestyle, despite the impact of fungi on the environment and food security. In this work, we show that combinations of organic acids and glucose trigger phenotypes that are associated with the late stage of biotrophy for the maize pathogen Ustilago maydis. These phenotypes include the expression of a set of effectors normally observed only during biotrophic development, as well as the formation of melanin associated with sporulation in plant tumors. U. maydis and other hemibiotrophic fungi also respond to a combination of carbon sources with enhanced proliferation. Thus, the response to combinations of nutrients from the host may be a conserved feature of fungal biotrophy.

Transcriptome analysis of Epichloë strains in tall fescue in response to drought stress

Epichloë coenophiala, a systemic fungal symbiont (endophyte) of tall fescue (Lolium arundinaceum), has been documented to confer to this grass better persistence than plants lacking the endophyte, especially under stress conditions such as drought. The response, if any, of the endophyte to imposition of stress on the host plant has not been characterized previously. Therefore, we investigated effects on gene expression by E. coenophiala and a related endophyte when plant-endophyte symbiota were subjected to acute water-deficit stress. Plants harboring different endophyte strains were grown in sand in the greenhouse, then half were deprived of water for 48 h and the other half were watered controls. RNA was isolated from different plant tissues, and mRNA sequencing (RNA-seq) was conducted to identify genes that were differentially expressed comparing stress treatment with control. We compared two different plants harboring the common toxic E. coenophiala strain (CTE) and two non-ergot-alkaloid-producing Epichloë strains in tall fescue pseudostems, and in a second experiment we compared responses of E. coenophiala CTE in plant pseudostem and crown tissues. The endophytes responded to the stress with increased expression of genes involved in oxidative stress response, oxygen radical detoxification, C-compound carbohydrate metabolism, heat shock, and cellular transport pathways. The magnitude of fungal gene responses during stress varied among plant-endophyte symbiota. Responses in pseudostems and crowns involved some common pathways as well as some tissue-specific pathways. The fungal response to water-deficit stress involved gene expression changes in similar pathways that have been documented for plant stress responses, indicating that Epichloë spp. and their host plants either coordinate stress responses or separately activate similar stress response mechanisms that work together for mutual protection.

Identifying Functions of Proteins in Mice With Functional Embedding Features

In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods.

A methanolic extract of Zanthoxylum bungeanum modulates secondary metabolism regulator genes in Aspergillus flavus and shuts down aflatoxin production

Aflatoxin B1 (AFB1) is a food-borne toxin produced by Aspergillus flavus and a few similar fungi. Natural anti-aflatoxigenic compounds are used as alternatives to chemical fungicides to prevent AFB1 accumulation. We found that a methanolic extract of the food additive Zanthoxylum bungeanum shuts down AFB1 production in A. flavus. A methanol sub-fraction (M20) showed the highest total phenolic/flavonoid content and the most potent antioxidant activity. Mass spectrometry analyses identified four flavonoids in M20: quercetin, epicatechin, kaempferol-3-O-rhamnoside, and hyperoside. The anti-aflatoxigenic potency of M20 (IC₅₀: 2-4 µg/mL) was significantly higher than its anti-proliferation potency (IC₅₀: 1800-1900 µg/mL). RNA-seq data indicated that M20 triggers significant transcriptional changes in 18 of 56 secondary metabolite pathways in A. flavus, including repression of the AFB1 biosynthesis pathway. Expression of aflR, the specific activator of the AFB1 pathway, was not changed by M20 treatment, suggesting that repression of the pathway is mediated by global regulators. Consistent with this, the Velvet complex, a prominent regulator of secondary metabolism and fungal development, was downregulated. Decreased expression of the conidial development regulators brlA and Medusa, genes that orchestrate redox responses, and GPCR/oxylipin-based signal transduction further suggests a broad cellular response to M20. Z. bungeanum extracts may facilitate the development of safe AFB1 control strategies.

Proteomic Shifts Reflecting Oxidative Stress and Reduced Capacity for Protein Synthesis, and Alterations to Mitochondrial Membranes in Neurospora crassa Lacking VDAC

Voltage-dependent anion-selective channels (VDAC) maintain the bidirectional flow of small metabolites across the mitochondrial outer membrane and participate in the regulation of multiple cellular processes. To understand the roles of VDAC in cellular homeostasis, preliminary proteomic analyses of S100 cytosolic and mitochondria-enriched fractions from a VDAC-less Neurospora crassa strain (ΔPor-1) were performed. In the variant cells, less abundant proteins include subunits of translation initiation factor eIF-2, enzymes in the shikimate pathway leading to precursors of aromatic amino acids, and enzymes involved in sulfate assimilation and in the synthesis of methionine, cysteine, alanine, serine, and threonine. In contrast, some of the more abundant proteins are involved in electron flow, such as the α subunit of the electron transfer flavoprotein and lactate dehydrogenase, which is involved in one pathway leading to pyruvate synthesis. Increased levels of catalase and catalase activity support predicted increased levels of oxidative stress in ΔPor-1 cells, and higher levels of protein disulfide isomerase suggest activation of the unfolded protein response in the endoplasmic reticulum. ΔPor-1 cells are cold-sensitive, which led us to investigate the impact of the absence of VDAC on several mitochondrial membrane characteristics. Mitochondrial membranes in ΔPor-1 are more fluid than those of wild-type cells, the ratio of C18:1 to C18:3n3 acyl chains is reduced, and ergosterol levels are lower. In summary, these initial results indicate that VDAC-less N. crassa cells are characterized by a lower abundance of proteins involved in amino acid and protein synthesis and by increases in some associated with pyruvate metabolism and stress responses. Membrane lipids and hyphal morphology are also impacted by the absence of VDAC.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00434-y.

A roadmap for metagenomic enzyme discovery

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

Pleiotropic Effects of the P5-Type ATPase SpfA on Stress Response Networks Contribute to Virulence in the Pathogenic Mold Aspergillus fumigatus

Aspergillus fumigatus is a human-pathogenic mold that extracts nutrients from the environment or from host tissues by secreting hydrolytic enzymes. The ability of A. fumigatus to adjust secretion levels in proportion to demand relies on the assistance of the unfolded protein response (UPR), an adaptive stress response pathway that regulates the unique protein-folding environment of the endoplasmic reticulum (ER). The P5-type ATPase Spf1 has recently been implicated in a novel mechanism of ER homeostasis that involves correcting errors in ER-membrane protein targeting. However, the contribution of this protein to the biology of A. fumigatus is unknown. Here, we employed a gene knockout and RNA sequencing strategy to determine the functional role of the A. fumigatus gene coding for the orthologous P5 ATPase SpfA. The data reveal that the spfA gene is induced by ER stress in a UPR-dependent manner. In the absence of spfA, the A. fumigatus transcriptome shifts toward a profile of altered redox and lipid balance, in addition to a signature of ER stress that includes srcA, encoding a second P-type ATPase in the ER. A ΔspfA deletion mutant showed increased sensitivity to ER stress, oxidative stress, and antifungal drugs that target the cell wall or plasma membrane. The combined loss of spfA and srcA exacerbated these phenotypes and attenuated virulence in two animal infection models. These findings demonstrate that the ER-resident ATPases SpfA and SrcA act jointly to support diverse adaptive functions of the ER that are necessary for fitness in the host environment. IMPORTANCE The fungal UPR is an adaptive signaling pathway in the ER that buffers fluctuations in ER stress but also serves as a virulence regulatory hub in species of pathogenic fungi that rely on secretory pathway homeostasis for pathogenicity. This study demonstrates that the gene encoding the ER-localized P5-type ATPase SpfA is a downstream target of the UPR in the pathogenic mold A. fumigatus and that it works together with a second ER-localized P-type ATPase, SrcA, to support ER homeostasis, oxidative stress resistance, susceptibility to antifungal drugs, and virulence of A. fumigatus.

Trends in the Application of "Omics" to Ecotoxicology and Stress Ecology

Our ability to predict and assess how environmental changes such as pollution and climate change affect components of the Earth's biome is of paramount importance. This need positioned the fields of ecotoxicology and stress ecology at the center of environmental monitoring efforts. Advances in these interdisciplinary fields depend not only on conceptual leaps but also on technological advances and data integration. High-throughput "omics" technologies enabled the measurement of molecular changes at virtually all levels of an organism's biological organization and thus continue to influence how the impacts of stressors are understood. This bibliometric review describes literature trends (2000-2020) that indicate that more different stressors than species are studied each year but that only a few stressors have been studied in more than two phyla. At the same time, the molecular responses of a diverse set of non-model species have been investigated, but cross-species comparisons are still rare. While transcriptomics studies dominated until 2016, a shift towards proteomics and multiomics studies is apparent. There is now a wealth of data at functional omics levels from many phylogenetically diverse species. This review, therefore, addresses the question of how to integrate omics information across species.

Protein function prediction with gene ontology: from traditional to deep learning models

Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (J_max) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high J_max was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.

This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

The integration of data at different levels of cellular organization is an important goal in computational biology for understanding the way the genotypic variation translates into phenotypic variation. Novel profiling technologies and accurate high-throughput phenotyping now allows genomic, transcriptomic, metabolic and proteomic characterization of a large number of individuals under various environmental conditions. However, the metabolic fluxes remain difficult to measure. In this work, we take advantage of recent advances in genome-scale functional annotation and constraint-based metabolic modeling to provide a mathematical framework that allows to estimate internal cellular fluxes from protein abundances and elucidate the biological mechanisms underlying phenotypic variation. Applied to yeast as a model system, this approach highlights that the negative correlation between production traits such as maximum population size and growth and fermentation traits is explained by a differential usage of energy production pathways. The ability to identify molecular and metabolic bases of the variation of integrated traits through population studies has a broad applicability domain.

Databases for Protein-Protein Interactions

Protein-protein interaction networks have a crucial role in biological processes. Proteins perform multiple functions in forming physical and functional interactions in cellular systems. Information concerning an enormous number of protein interactions in a wide range of species has accumulated and has been integrated into various resources for molecular biology and systems biology. This chapter provides a review of the representative databases and the major computational methods used for protein-protein interactions.

HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction

MOTIVATION

Automated protein function prediction is a complex multi-class, multi-label, structured classification problem in which protein functions are organized in a controlled vocabulary, according to the Gene Ontology (GO). "Hierarchy-unaware" classifiers, also known as "flat" methods, predict GO terms without exploiting the inherent structure of the ontology, potentially violating the True-Path-Rule (TPR) that governs the GO, while "hierarchy-aware" approaches, even if they obey the TPR, do not always show clear improvements with respect to flat methods, or do not scale well when applied to the full GO.

RESULTS

To overcome these limitations, we propose Hierarchical Ensemble Methods for Directed Acyclic Graphs (HEMDAG), a family of highly modular hierarchical ensembles of classifiers, able to build upon any flat method and to provide "TPR-safe" predictions, by leveraging a combination of isotonic regression and TPR learning strategies. Extensive experiments on synthetic and real data across several organisms firstly show that HEMDAG can be used as a general tool to improve the predictions of flat classifiers, and secondly that HEMDAG is competitive versus state-of-the-art hierarchy-aware learning methods proposed in the last CAFA international challenges.

AVAILABILITY

Fully-tested R code freely available at https://anaconda.org/bioconda/r-hemdag. Tutorial and documentation at https://hemdag.readthedocs.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromatin accessibility profiling in Neurospora crassa reveals molecular features associated with accessible and inaccessible chromatin

BACKGROUND

Regulation of chromatin accessibility and transcription are tightly coordinated processes. Studies in yeast and higher eukaryotes have described accessible chromatin regions, but little work has been done in filamentous fungi.

RESULTS

Here we present a genome-scale characterization of accessible chromatin regions in Neurospora crassa, which revealed characteristic molecular features of accessible and inaccessible chromatin. We present experimental evidence of inaccessibility within heterochromatin regions in Neurospora, and we examine features of both accessible and inaccessible chromatin, including the presence of histone modifications, types of transcription, transcription factor binding, and relative nucleosome turnover rates. Chromatin accessibility is not strictly correlated with expression level. Accessible chromatin regions in the model filamentous fungus Neurospora are characterized the presence of H3K27 acetylation and commonly associated with pervasive non-coding transcription. Conversely, methylation of H3 lysine-36 catalyzed by ASH1 is correlated with inaccessible chromatin within promoter regions.

CONCLUSIONS

In N. crassa, H3K27 acetylation is the most predictive histone modification for open chromatin. Conversely, our data show that H3K36 methylation is a key marker of inaccessible chromatin in gene-rich regions of the genome. Our data are consistent with an expanded role for H3K36 methylation in intergenic regions of filamentous fungi compared to the model yeasts, S. cerevisiae and S. pombe, which lack homologs of the ASH1 methyltransferase.

Revealing Candida glabrata biofilm matrix proteome: global characterization and pH response

Candida glabrata is a clinically relevant human pathogen with the ability to form high recalcitrant biofilms that contribute to the establishment and persistence of infection. A defining trait of biofilms is the auto-produced matrix, which is suggested to have structural, virulent and protective roles. Thus, elucidation of matrix components, their function and modulation by the host environment is crucial to disclose their role in C. glabrata pathogenesis. As a major step toward this end, this study aimed to reveal, for the first time, the matrix proteome of C. glabrata biofilms, to characterize it with bioinformatic tools and to study its modulation by the environmental pH (acidic and neutral). The results showed the presence of several pH-specific matrix proteins (51 acidic- and 206 neutral-specific) and also proteins commonly found at both pH conditions (236). Of note, several proteins related to mannan and β-glucan metabolism, which have a potential role in the delivery/organization of carbohydrates in the matrix, were found in both pH conditions but in much higher quantity under the neutral environment. Additionally, several virulence-related proteins, including epithelial adhesins, yapsins and moonlighting enzymes, were found among matrix proteins. Importantly, several proteins seem to have a non-canonical secretion pathway and Pdr1 was found to be a potential regulator of matrix proteome. Overall, this study indicates a relevant impact of environmental cues in the matrix proteome and provides a unique resource for further functional investigation of matrix proteins, contributing to the identification of potential targets for the development of new therapies against C. glabrata biofilms.

Comparative Proteomic Analysis of Histoplasma capsulatum Yeast and Mycelium Reveals Differential Metabolic Shifts and Cell Wall Remodeling Processes in the Different Morphotypes

Histoplasma capsulatum is a thermally dimorphic fungus distributed worldwide, but with the highest incidence in the Americas within specific geographic areas, such as the Mississippi River Valley and regions in Latin America. This fungus is the etiologic agent of histoplasmosis, an important life-threatening systemic mycosis. Dimorphism is an important feature for fungal survival in different environments and is related to the virulence of H. capsulatum, and essential to the establishment of infection. Proteomic profiles have made important contributions to the knowledge of metabolism and pathogenicity in several biological models. However, H. capsulatum proteome studies have been underexplored. In the present study, we report the first proteomic comparison between the mycelium and the yeast cells of H. capsulatum. Liquid chromatography coupled to mass spectrometry was used to evaluate the proteomic profile of the two phases of H. capsulatum growth, mycelium, and yeast. In summary, 214 and 225 proteins were only detected/or preferentially abundant in mycelium or yeast cells, respectively. In mycelium, enzymes related to the glycolytic pathway and to the alcoholic fermentation occurred in greater abundance, suggesting a higher use of anaerobic pathways for energy production. In yeast cells, proteins related to the tricarboxylic acid cycle and response to temperature stress were in high abundance. Proteins related to oxidative stress response or involved with cell wall metabolism were identified with differential abundance in both conditions. Proteomic data validation was performed by enzymatic activity determination, Western blot assays, or immunofluorescence microscopy. These experiments corroborated, directly or indirectly, the abundance of isocitrate lyase, 2-methylcitrate synthase, catalase B, and mannosyl-oligosaccharide-1,2-alpha-mannosidase in the mycelium and heat shock protein (HSP) 30, HSP60, glucosamine-fructose-6-phosphate aminotransferase, glucosamine-6-phosphate deaminase, and N-acetylglucosamine-phosphate mutase in yeast cells. The proteomic profile-associated functional classification analyses of proteins provided new and interesting information regarding the differences in metabolism between the two distinct growth forms of H. capsulatum.

Tolerance to nascent protein misfolding stress requires fine-tuning of the cAMP/PKA pathway

Protein aggregation is the abnormal association of misfolded proteins into larger, often insoluble structures that can be toxic during aging and in protein aggregation-associated diseases. Previous research has established a role for the cytosolic Tsa1 peroxiredoxin in responding to protein misfolding stress. Tsa1 is also known to downregulate the cAMP/protein kinase A (PKA) pathway as part of the response to hydrogen peroxide stress. However, whether the cAMP/PKA pathway is involved in protein misfolding stress is not known. Using transcriptomics, we examined the response to protein misfolding stress and found upregulation of numerous stress gene functions and downregulation of many genes related to protein synthesis and other growth-related processes consistent with the well-characterized environmental stress response. The scope of the transcriptional response is largely similar in wild-type and tsa1 mutant strains, but the magnitude is dampened in the strain lacking Tsa1. We identified a direct protein interaction between Tsa1 and the Bcy1 regulatory subunit of PKA that is present under normal growth conditions and explains the observed differences in gene expression profiles. This interaction is increased in a redox-dependent manner in response to nascent protein misfolding, via Tsa1-mediated oxidation of Bcy1. Oxidation of Bcy1 causes a reduction in cAMP binding by Bcy1, which dampens PKA pathway activity, leading to a targeted reprogramming of gene expression. Redox regulation of the regulatory subunit of PKA provides a mechanism to mitigate the toxic consequences of protein misfolding stress that is distinct to stress caused by exogenous sources of reactive oxygen species.

Identifying Conserved Generic Aspergillus spp. Co-Expressed Gene Modules Associated with Germination Using Cross-Platform and Cross-Species Transcriptomics

Aspergillus spp. is an opportunistic human pathogen that may cause a spectrum of pulmonary diseases. In order to establish infection, inhaled conidia must germinate, whereby they break dormancy, start to swell, and initiate a highly polarized growth process. To identify critical biological processes during germination, we performed a cross-platform, cross-species comparative analysis of germinating A. fumigatus and A. niger conidia using transcriptional data from published RNA-Seq and Affymetrix studies. A consensus co-expression network analysis identified four gene modules associated with stages of germination. These modules showed numerous shared biological processes between A. niger and A. fumigatus during conidial germination. Specifically, the turquoise module was enriched with secondary metabolism, the black module was highly enriched with protein synthesis, the darkgreen module was enriched with protein fate, and the blue module was highly enriched with polarized growth. More specifically, enriched functional categories identified in the blue module were vesicle formation, vesicular transport, tubulin dependent transport, actin-dependent transport, exocytosis, and endocytosis. Genes important for these biological processes showed similar expression patterns in A. fumigatus and A. niger, therefore, they could be potential antifungal targets. Through cross-platform, cross-species comparative analysis, we were able to identify biologically meaningful modules shared by A. fumigatus and A. niger, which underscores the potential of this approach.

Acid pH Strategy Adaptation through NRG1 in Ustilago maydis

The role of the Ustilago maydis putative homolog of the transcriptional repressor ScNRG1, previously described in Saccharomyces cerevisiae, Candida albicans and Cryptococcus neoformans, was analyzed by means of its mutation. In S. cerevisiae this gene regulates a set of stress-responsive genes, and in C. neoformans it is involved in pathogenesis. It was observed that the U. maydisNRG1 gene regulates several aspects of the cell response to acid pH, such as the production of mannosyl-erythritol lipids, inhibition of the expression of the siderophore cluster genes, filamentous growth, virulence and oxidative stress. A comparison of the gene expression pattern of the wild type strain versus the nrg1 mutant strain of the fungus, through RNA Seq analyses, showed that this transcriptional factor alters the expression of 368 genes when growing at acid pH (205 up-regulated, 163 down-regulated). The most relevant genes affected by NRG1 were those previously reported as the key ones for particular cellular stress responses, such as HOG1 for osmotic stress and RIM101 for alkaline pH. Four of the seven genes included WCO1 codifying PAS domain ( These has been shown as the key structural motif involved in protein-protein interactions of the circadian clock, and it is also a common motif found in signaling proteins, where it functions as a signaling sensor) domains sensors of blue light, two of the three previously reported to encode opsins, one vacuolar and non-pH-responsive, and another one whose role in the acid pH response was already known. It appears that all these light-reactive cell components are possibly involved in membrane potential equilibrium and as virulence sensors. Among previously described specific functions of this transcriptional regulator, it was found to be involved in glucose repression, metabolic adaptation to adverse conditions, cellular transport, cell rescue, defense and interaction with an acidic pH environment.

Proteome adaptations under contrasting soil phosphate regimes of Rhizophagus irregularis engaged in a common mycorrhizal network

For many plants, their symbiosis with arbuscular mycorrhizal fungi plays a key role in the acquisition of mineral nutrients such as inorganic phosphate (Pi), in exchange for assimilated carbon. To study gene regulation and function in the symbiotic partners, we and others have used compartmented microcosms in which the extra-radical mycelium (ERM), responsible for mineral nutrient supply for the plants, was separated by fine nylon nets from the associated host roots and could be harvested and analysed in isolation. Here, we used such a model system to perform a quantitative comparative protein profiling of the ERM of Rhizophagus irregularis BEG75, forming a common mycorrhizal network (CMN) between poplar and sorghum roots under a long-term high- or low-Pi fertilization regime. Proteins were extracted from the ERM and analysed by liquid chromatography-tandem mass spectrometry. This workflow identified a total of 1301 proteins, among which 162 displayed a differential amount during Pi limitation, as monitored by spectral counting. Higher abundances were recorded for proteins involved in the mobilization of external Pi, such as secreted acid phosphatase, 3',5'-bisphosphate nucleotidase, and calcium-dependent phosphotriesterase. This was also the case for intracellular phospholipase and lysophospholipases that are involved in the initial degradation of phospholipids from membrane lipids to mobilize internal Pi. In Pi-deficient conditions. The CMN proteome was especially enriched in proteins assigned to beta-oxidation, glyoxylate shunt and gluconeogenesis, indicating that storage lipids rather than carbohydrates are fuelled in ERM as the carbon source to support hyphal growth and energy requirements. The contrasting pattern of expression of AM-specific fatty acid biosynthetic genes between the two plants suggests that in low Pi conditions, fatty acid provision to the fungal network is mediated by sorghum roots but not by poplar. Loss of enzymes involved in arginine synthesis coupled to the mobilization of proteins involved in the breakdown of nitrogen sources such as intercellular purines and amino acids, support the view that ammonium acquisition by host plants through the mycorrhizal pathway may be reduced under low-Pi conditions. This proteomic study highlights the functioning of a CMN in Pi limiting conditions, and provides new perspectives to study plant nutrient acquisition as mediated by arbuscular mycorrhizal fungi.

AligNet: alignment of protein-protein interaction networks

Background

All molecular functions and biological processes are carried out by groups of proteins that interact with each other. Metaproteomic data continuously generates new proteins whose molecular functions and relations must be discovered. A widely accepted structure to model functional relations between proteins are protein-protein interaction networks (PPIN), and their analysis and alignment has become a key ingredient in the study and prediction of protein-protein interactions, protein function, and evolutionary conserved assembly pathways of protein complexes. Several PPIN aligners have been proposed, but attaining the right balance between network topology and biological information is one of the most difficult and key points in the design of any PPIN alignment algorithm.

Results

Motivated by the challenge of well-balanced and efficient algorithms, we have designed and implemented AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at bridging the gap between topologically efficient and biologically meaningful matchings. A comparison of the results obtained with AligNet and with the best aligners shows that AligNet achieves indeed a good balance between topological and biological matching.

Conclusion

In this paper we present AligNet, a new pairwise global PPIN aligner that produces biologically meaningful alignments, by achieving a good balance between structural matching and protein function conservation, and more efficient computations than state-of-the-art tools.

Plant nitrogen supply affects the Botrytis cinerea infection process and modulates known and novel virulence factors

Plant nitrogen (N) fertilization is known to affect disease; however, the underlying mechanisms remain mostly unknown. We investigated the impact of N supply on the Arabidopsis thaliana-Botrytis cinerea interaction. A. thaliana plants grown in low nitrate were more tolerant to all wild-type B. cinerea strains tested. We determined leaf nitrate concentrations and showed that they had a limited impact on B. cinerea growth in vitro. For the first time, we performed a dual RNA-Seq of infected leaves of plants grown with different nitrate concentrations. Transcriptome analysis showed that plant and fungal transcriptomes were marginally affected by plant nitrate supply. Indeed, only a limited set of plant (182) and fungal (22) genes displayed expression profiles altered by nitrate supply. The expression of selected genes was confirmed by quantitative reverse transcription PCR at 6 hr postinfection (hpi) and analysed at a later time point (24 hpi). We selected three of the 22 B. cinerea genes identified for further analysis. B. cinerea mutants affected in these genes were less aggressive than the wild-type strain. We also showed that plants grown in ammonium were more tolerant to B. cinerea. Furthermore, expression of the selected B. cinerea genes in planta was altered when plants were grown with ammonium instead of nitrate, demonstrating an impact of the nature of N supplied to plants on the interaction. Identification of B. cinerea genes expressed differentially in planta according to plant N supply unveils two novel virulence functions required for full virulence in A. thaliana: a secondary metabolite (SM) and an acidic protease (AP).