Going from where to why--interpretable prediction of protein subcellular localization

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

Rhizobial-induced phosphatase GmPP2C61A positively regulates soybean nodulation

Symbiotic nitrogen fixation (SNF) is crucial for legumes, providing them with the nitrogen necessary for plant growth and development. Nodulation is the first step in the establishment of SNF. However, the determinant genes in soybean nodulation and the understanding of the underlying molecular mechanisms governing nodulation are still limited. Herein, we identified a phosphatase, GmPP2C61A, which was specifically induced by rhizobia inoculation. Using transgenic hairy roots harboring GmPP2C61A::GUS, we showed that GmPP2C61A was mainly induced in epidermal cells following rhizobia inoculation. Functional analysis revealed that knockdown or knock-out of GmPP2C61A significantly reduced the number of nodules, while overexpression of GmPP2C61A promoted nodule formation. Additionally, GmPP2C61A protein was mainly localized in the cytoplasm and exhibited conserved phosphatase activity in vitro. Our findings suggest that phosphatase GmPP2C61A serves as a critical regulator in soybean nodulation, highlighting its potential significance in enhancing symbiotic nitrogen fixation.

A mutation in CsGME encoding GDP-mannose 3,5-epimerase results in little and wrinkled leaf in cucumber

KEY MESSAGE

Map-based cloning revealed that a mutation in a highly conserved amino acid of the CsGME gene encoding GDP-mannose 3,5-epimerase, causes the phenotype of little and wrinkled leaves in cucumbers. Leaf size is a critical determinant of plant architecture in cucumbers, yet only a few genes associated with this trait have been mapped or cloned. Here, we identified and characterized a mutant with little and wrinkled leaves, named lwl-1. Genetic analysis revealed that the phenotype of the lwl-1 was controlled by a single recessive gene. Through map-based cloning, the lwl-1 locus was narrowed down to a 12.22-kb region exclusively containing one fully annotated gene CsGME (CsaV3_2G004170). CsGME encodes GDP-mannose 3,5-epimerase, which is involved in the synthesis of ascorbic acid (ASA) and one of the components of pectin, RG-II. Whole-length sequencing of the 12.22 kb DNA fragment revealed the presence of only a non-synonymous mutation located in the sixth exon of CsGME in lwl-1, resulting in an amino acid alteration from Pro363 to Leu363. This mutation was unique among 118 inbred lines from cucumber natural populations. CsGME expression significantly reduced in various organs of lwl-1, accompanied by a significant decrease in ASA and pectin content in leaves. Both CsGME and Csgme proteins were localized to the cytoplasm. The mutant phenotype exhibited partial recovery after the application of exogenous boric acid. Silencing CsGME in cucumber through VIGS confirmed its role as the causal gene for lwl-1. Transcriptome profiling revealed that CsGME greatly affected the expression of genes related to the cell division process and cell plate formation. This study represents the first report to characterize and clone the CsGME in cucumber, indicating its crucial role in regulating leaf size and development.

Regulation of developmental gatekeeping and cell fate transition by the calpain protease DEK1 in Physcomitrium patens

Calpains are cysteine proteases that control cell fate transitions whose loss of function causes severe, pleiotropic phenotypes in eukaryotes. Although mainly considered as modulatory proteases, human calpain targets are directed to the N-end rule degradation pathway. Several such targets are transcription factors, hinting at a gene-regulatory role. Here, we analyze the gene-regulatory networks of the moss Physcomitrium patens and characterize the regulons that are misregulated in mutants of the calpain DEFECTIVE KERNEL1 (DEK1). Predicted cleavage patterns of the regulatory hierarchies in five DEK1-controlled subnetworks are consistent with a pleiotropic and regulatory role during cell fate transitions targeting multiple functions. Network structure suggests DEK1-gated sequential transitions between cell fates in 2D-to-3D development. Our method combines comprehensive phenotyping, transcriptomics and data science to dissect phenotypic traits, and our model explains the protease function as a switch gatekeeping cell fate transitions potentially also beyond plant development.

Gossypium hirsutum gene of unknown function Gohir.A02G161000 encodes a potential transmembrane Root UVB Sensitive 4 Protein with a putative protein-protein interaction interface

A gene of unknown function, Gohir.A02G161000.1, identified in Gossypium hirsutum was studied using computational sequence and structure bioinformatics tools. The associated protein GhRUS4-A0A1U8JPV7 (UniProt A0A1U8JPV7) is predicted to be a plastid-localized, transmembrane root UVB-sensitive 4 (RUS4) protein with a newly identified potential dimerization surface. Evidence from homology and sequence conservation suggest involvement in auxin transport and pollen maturation.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

PPAR-gamma agonist pioglitazone recovers mitochondrial quality control in fibroblasts from PITRM1-deficient patients

Introduction: Biallelic variants in PITRM1 are associated with a slowly progressive syndrome characterized by intellectual disability, spinocerebellar ataxia, cognitive decline and psychosis. The pitrilysin metallopeptidase 1 (PITRM1) is a mitochondrial matrix enzyme, which digests diverse oligopeptides, including the mitochondrial targeting sequences (MTS) that are cleaved from proteins imported across the inner mitochondrial membrane by the mitochondrial processing peptidase (MPP). Mitochondrial peptidases also play a role in the maturation of Frataxin, the protein affected in Friedreich's ataxia. Recent studies in yeast indicated that the mitochondrial matrix protease Ste23, which is a homologue of the human insulin-degrading enzyme (IDE), cooperates with Cym1 (homologue of PITRM1) to ensure the proper functioning of the preprotein processing machinery. In humans, IDE could be upregulated by Peroxisome Proliferator-Activated Receptor Gamma (PPARG) agonists. Methods: We investigated preprotein processing, mitochondrial membrane potential and MTS degradation in control and patients' fibroblasts, and we evaluated the pharmacological effect of the PPARG agonist Pioglitazone on mitochondrial proteostasis. Results: We discovered that PITRM1 dysfunction results in the accumulation of MTS, leading to the disruption and dissipation of the mitochondrial membrane potential. This triggers a feedback inhibition of MPP activity, consequently impairing the processing and maturation of Frataxin. Furthermore, we found that the pharmacological stimulation of PPARG by Pioglitazone upregulates IDE and also PITRM1 protein levels restoring the presequence processing machinery and improving Frataxin maturation and mitochondrial function. Discussion: Our findings provide mechanistic insights and suggest a potential pharmacological strategy for this rare neurodegenerative mitochondrial disease.

Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field

Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit-explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring "the state of the art" in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI-PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI-PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI-PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the "state of the art" on research in the AI-PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.

DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

The prediction of protein subcellular localization is of great relevance for proteomics research. Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability. For training and validation, we curate eukaryotic and human multi-location protein datasets with stringent homology partitioning and enriched with sorting signal information compiled from the literature. We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model. It has the further advantage that it uses sequence input rather than relying on slower protein profiles. We provide two means of better interpretability: an attention output along the sequence and highly accurate prediction of nine different types of protein sorting signals. We find that the attention output correlates well with the position of sorting signals. The webserver is available at services.healthtech.dtu.dk/service.php?DeepLoc-2.0.

The grapevine aspartic protease gene family: characterization and expression modulation in response to Plasmopara viticola

Grapevine aspartic proteases gene family is characterized and five VviAPs appear to be involved in grapevine defense against downy mildew. Grapevine (Vitis vinifera L.) is one of the most important crops worldwide. However, it is highly susceptible to the downy mildew disease caused by Plasmopara viticola (Berk. & Curt.) Berl. & De Toni. To minimize the use of fungicides used to control P. viticola, it is essential to gain a deeper comprehension on this pathosystem and proteases have gained particular interest in the past decade. Proteases were shown to actively participate in plant-pathogen interactions, not only in the processes that lead to plant cell death, stress responses and protein processing/degradation but also as components of the recognition and signalling pathways. The aim of this study was to identify and characterize the aspartic proteases (APs) involvement in grapevine defense against P. viticola. A genome-wide search and bioinformatics characterization of the V. vinifera AP gene family was conducted and a total of 81 APs proteins, coded by 65 genes, were found. VviAPs proteins can be divided into three categories, similar to those previously described for other plants. Twelve APs coding genes were selected, and expression analysis was conducted at several time-points after inoculation in both compatible and incompatible interactions. Five grapevine APs may be involved in grapevine tolerance against P. viticola. Our findings provide an overall understanding of the VviAPs gene family and establish better groundwork to further describe the roles of VviAPs in defense against P. viticola.

The DUF288 domain containing proteins GhSTLs participate in cotton fiber cellulose synthesis and impact on fiber elongation

Cotton is one of the most important economic crops in the world, with over 90 % cellulose in the mature fiber. However, the cellulose synthesis mechanism in cotton fibers is poorly understood. Here, we identified four DUF288 domain containing proteins, which we designated GhSTL1-4. These four GhSTL genes are highly expressed in 6 days post anthesis (dpa) and 20 dpa cotton fibers. They are localized to the Golgi apparatus, and can rescue the growth defects in primary cell wall (PCW) and secondary cell wall (SCW) of cellulose synthesis of the Arabidopsis stl1stl2 double mutant at varying degrees. Silencing of GhSTLs resulted in reduced cellulose content and shorter fibers. In addition, split-ubiquitin membrane yeast two-hybrid analysis showed that GhSTL1 and GhSTL4 can interact with PCW-related GhCesA6-1/6-3 and SCW-associated GhCesA7-1/7-2. GhSTL3 can interact with SCW-related GhCesA4-3. These interactions are further confirmed by firefly luciferase complementation imaging assay. Together, we demonstrate that GhSTLs can selectively interact with both the PCW and SCW-associated GhCesAs and impact on cellulose synthesis and fiber development. Our findings provide insights into the mechanism underlying cellulose biosynthesis in cotton fibers, and offer potential candidate genes to coordinate PCW and SCW cellulose synthesis of cotton fibers for developing elite cotton varieties with enhanced fiber quality.

Genome-Wide Analyses of Aspartic Proteases on Potato Genome (Solanum tuberosum): Generating New Tools to Improve the Resistance of Plants to Abiotic Stress

Aspartic proteases are proteolytic enzymes widely distributed in living organisms and viruses. Although they have been extensively studied in many plant species, they are poorly described in potatoes. The present study aimed to identify and characterize S. tuberosum aspartic proteases. Gene structure, chromosome and protein domain organization, phylogeny, and subcellular predicted localization were analyzed and integrated with RNAseq data from different tissues, organs, and conditions focused on abiotic stress. Sixty-two aspartic protease genes were retrieved from the potato genome, distributed in 12 chromosomes. A high number of intronless genes and segmental and tandem duplications were detected. Phylogenetic analysis revealed eight StAP groups, named from StAPI to StAPVIII, that were differentiated into typical (StAPI), nucellin-like (StAPIIIa), and atypical aspartic proteases (StAPII, StAPIIIb to StAPVIII). RNAseq data analyses showed that gene expression was consistent with the presence of cis-acting regulatory elements on StAP promoter regions related to water deficit. The study presents the first identification and characterization of 62 aspartic protease genes and proteins on the potato genome and provides the baseline material for functional gene determinations and potato breeding programs, including gene editing mediated by CRISPR.

Genome-wide in silico characterization and stress induced expression analysis of BcL-2 associated athanogene (BAG) family in Musa spp

Programmed cell death (PCD) is a genetically controlled process for the selective removal of damaged cells. Though understanding about plant PCD has improved over years, the mechanisms are yet to be fully deciphered. Among the several molecular players of PCD in plants, B cell lymphoma 2 (Bcl-2)-associated athanogene (BAG) family of co-chaperones are evolutionary conserved and regulate cell death, growth and development. In this study, we performed a genome-wide in silico analysis of the MusaBAG gene family in a globally important fruit crop banana. Thirteen MusaBAG genes were identified, out of which MusaBAG1, 7 and 8 genes were found to have multiple copies. MusaBAG genes were distributed on seven out of 11 chromosomes in banana. Except for one paralog of MusaBAG8 all the other 12 proteins have characteristic BAG domain. MusaBAG1, 2 and 4 have an additional ubiquitin-like domain whereas MusaBAG5-8 have a calmodulin binding motif. Most of the MusaBAG proteins were predicted to be localized in the nucleus and mitochondria or chloroplast. The in silico cis-regulatory element analysis suggested regulation associated with photoperiodic control, abiotic and biotic stress. The phylogenetic analysis revealed 2 major clusters. Digital gene expression analysis and quantitative real-time RT-PCR depicted the differential expression pattern of MusaBAG genes under abiotic and biotic stress conditions. Further studies are warranted to uncover the role of each of these proteins in growth, PCD and stress responses so as to explore them as candidate genes for engineering transgenic banana plants with improved agronomic traits.

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets

Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.

A mutation in CsHY2 encoding a phytochromobilin (PΦB) synthase leads to an elongated hypocotyl 1(elh1) phenotype in cucumber (Cucumis sativus L.)

The elongated hypocotyl1 (elh1) mutant in cucumber is due to a mutation in CsHY2, which is a homolog of the Arabidopsis HY2 encoding the phytochromobilin (PΦB) synthase for phytochrome biosynthesis Hypocotyl length is a critical determinant in establishing high quality seedlings for successful cucumber production, but knowledge on the molecular regulation of hypocotyl growth in cucumber is very limited. Here, we reported identification and characterization of a cucumber elongated hypocotyl 1 (elh1) mutant. We found that the longer hypocotyl in elh1 was due to longitudinal growth of hypocotyl cells. With fine mapping, the elh1 locus was delimited to a 20.9-kb region containing three annotated genes; only one polymorphism was identified in this region between two parental lines, which was a non-synonymous SNP (G28153633A) in the third exon of CsHY2 (CsGy1G030000) that encodes a phytochromobilin (PΦB) synthase. Uniqueness of the mutant allele at CsHY2 was verified in natural cucumber populations. Ectopic expression of CsHY2 in Arabidopsis hy2-1 long-hypocotyl mutant led to reduced hypocotyl length. The PΦB protein was targeted to the chloroplast. The expression levels of CsHY2 and five phytochrome genes CsPHYA1, CsPHYA2, CsPHYB, CsPHYC and CsPHYE were all significantly down-regulated while several cell elongation related genes were up-regulated in elh1 mutant compared to wild-type cucumber, which are correlated with dynamic hypocotyl elongation in the mutant. RNA-seq analysis in the WT and mutant revealed differentially expressed genes involved in porphyrin and chlorophyll metabolisms, cell elongation and plant hormone signal transduction pathways. This is the first report to characterize and clone the CsHY2 gene in cucumber. This work reveals the important of CsHY2 in regulating hypocotyl length and extends our understanding of the roles of CsHY2 in cucumber.

Multi-responses of O-methyltransferase genes to salt stress and fiber development of Gossypium species

BACKGROUND

O-methyltransferases (OMTs) are an important group of enzymes that catalyze the transfer of a methyl group from S-adenosyl-L-methionine to their acceptor substrates. OMTs are divided into several groups according to their structural features. In Gossypium species, they are involved in phenolics and flavonoid pathways. Phenolics defend the cellulose fiber from dreadful external conditions of biotic and abiotic stresses, promoting strength and growth of plant cell wall.

RESULTS

An OMT gene family, containing a total of 192 members, has been identified and characterized in three main Gossypium species, G. hirsutum, G. arboreum and G. raimondii. Cis-regulatory elements analysis suggested important roles of OMT genes in growth, development, and defense against stresses. Transcriptome data of different fiber developmental stages in Chromosome Substitution Segment Lines (CSSLs), Recombination Inbred Lines (RILs) with excellent fiber quality, and standard genetic cotton cultivar TM-1 demonstrate that up-regulation of OMT genes at different fiber developmental stages, and abiotic stress treatments have some significant correlations with fiber quality formation, and with salt stress response. Quantitative RT-PCR results revealed that GhOMT10_Dt and GhOMT70_At genes had a specific expression in response to salt stress while GhOMT49_At, GhOMT49_Dt, and GhOMT48_At in fiber elongation and secondary cell wall stages.

CONCLUSIONS

Our results indicate that O-methyltransferase genes have multi-responses to salt stress and fiber development in Gossypium species and that they may contribute to salt tolerance or fiber quality formation in Gossypium.

Protein Subcellular Localization Prediction

The elucidation of the subcellular localization of proteins is very important in order to deeply understand their functions. In fact, proteins activities are strictly correlated to the cellular compartment and microenvironment in which they are present.In recent years, several effective and reliable proteomics techniques and computational methods have been developed and implemented in order to identify the proteins subcellular localization. This process is often time-consuming and expensive, but the recent technological and bioinformatics progress allowed the development of more accurate and simple workflows to determine the localization, interactions, and functions of proteins.In the following chapter, a brief introduction on the importance of knowing subcellular localization of proteins will be presented. Then, sample preparation protocols, proteomic methods, data analysis strategies, and software for the prediction of proteins localization will be presented and discussed. Finally, the more recent and advanced spatial proteomics techniques will be shown.

An Overview of Genomics, Phylogenomics and Proteomics Approaches in Ascomycota

Fungi are among the most successful eukaryotes on Earth: they have evolved strategies to survive in the most diverse environments and stressful conditions and have been selected and exploited for multiple aims by humans. The characteristic features intrinsic of Fungi have required evolutionary changes and adaptations at deep molecular levels. Omics approaches, nowadays including genomics, metagenomics, phylogenomics, transcriptomics, metabolomics, and proteomics have enormously advanced the way to understand fungal diversity at diverse taxonomic levels, under changeable conditions and in still under-investigated environments. These approaches can be applied both on environmental communities and on individual organisms, either in nature or in axenic culture and have led the traditional morphology-based fungal systematic to increasingly implement molecular-based approaches. The advent of next-generation sequencing technologies was key to boost advances in fungal genomics and proteomics research. Much effort has also been directed towards the development of methodologies for optimal genomic DNA and protein extraction and separation. To date, the amount of proteomics investigations in Ascomycetes exceeds those carried out in any other fungal group. This is primarily due to the preponderance of their involvement in plant and animal diseases and multiple industrial applications, and therefore the need to understand the biological basis of the infectious process to develop mechanisms for biologic control, as well as to detect key proteins with roles in stress survival. Here we chose to present an overview as much comprehensive as possible of the major advances, mainly of the past decade, in the fields of genomics (including phylogenomics) and proteomics of Ascomycota, focusing particularly on those reporting on opportunistic pathogenic, extremophilic, polyextremotolerant and lichenized fungi. We also present a review of the mostly used genome sequencing technologies and methods for DNA sequence and protein analyses applied so far for fungi.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

Two phylogenetically divergent isocitrate dehydrogenases are encoded in Leishmania parasites. Molecular and functional characterization of Leishmania mexicana isoenzymes with specificity towards NAD+ and NADP

Leishmania parasites are of great relevance to public health because they are the causative agents of various long-term and health-threatening diseases in humans. Dependent on the manifestation, drugs either require difficult and lengthy administration, are toxic, expensive, not very effective or have lost efficacy due to the resistance developed by these pathogens against clinical treatments. The intermediary metabolism of Leishmania parasites is characterized by several unusual features, among which whether the Krebs cycle operates in a cyclic and/or in a non-cyclic mode is included. Our survey of the genomes of Leishmania species and monoxenous parasites such as those of the genera Crithidia and Leptomonas (http://www.tritrypdb.org) revealed that two genes encoding putative isocitrate dehydrogenases (IDHs) -with distantly related sequences- are strictly conserved among these parasites. Thus, in this study, we aimed to functionally characterize the two leishmanial IDH isoenzymes, for which we selected the genes LmxM10.0290 (Lmex_IDH-90) and LmxM32.2550 (Lmex_IDH-50) from L. mexicana. Phylogenetic analysis showed that Lmex_IDH-50 clustered with members of Subfamily I, which contains mainly archaeal and bacterial IDHs, and that Lmex_IDH-90 was a close relative of eukaryotic enzymes comprised within Subfamily II IDHs. 3-D homology modeling predicted that both IDHs exhibited the typical folding motifs recognized as canonical for prokaryotic and eukaryotic counterparts, respectively. Both IDH isoforms displayed dual subcellular localization, in the cytosol and the mitochondrion. Kinetic studies showed that Lmex_IDH-50 exclusively catalyzed the reduction of NAD⁺, while Lmex_IDH-90 solely used NADP⁺ as coenzyme. Besides, Lmex_IDH-50 differed from Lmex_IDH-90 by exhibiting a nearly 20-fold lower apparent K_m value towards isocitrate (2.0 μM vs 43 μM). Our findings showed, for the first time, that the genus Leishmania differentiates not only from other trypanosomatids such as Trypanosoma cruzi and Trypanosoma brucei, but also from most living organisms, by exhibiting two functional homo-dimeric IDHs, highly specific towards NAD⁺ and NADP⁺, respectively. It is tempting to argue that any or both types of IDHs might be directly or indirectly linked to the Krebs cycle and/or to the de novo synthesis of glutamate. Our results about the biochemical and structural features of leishmanial IDHs show the relevance of deepening our knowledge of the metabolic processes in these pathogenic parasites to potentially identify new therapeutic targets.

The Role of APOSTART in Switching between Sexuality and Apomixis in Poa pratensis

The production of seeds without sex is considered the holy grail of plant biology. The transfer of apomixis to various crop species has the potential to transform plant breeding, since it will allow new varieties to retain valuable traits thorough asexual reproduction. Therefore, a greater molecular understanding of apomixis is fundamental. In a previous work we identified a gene, namely APOSTART, that seemed to be involved in this asexual mode of reproduction, which is very common in Poa pratensis L., and here we present a detailed work aimed at clarifying its role in apomixis. In situ hybridization showed that PpAPOSTART is expressed in reproductive tissues from pre-meiosis to embryo development. Interestingly, it is expressed early in few nucellar cells of apomictic individuals possibly switching from a somatic to a reproductive cell as in aposporic apomixis. Moreover, out of 13 APOSTART members, we identified one, APOSTART_6, as specifically expressed in flower tissue. APOSTART_6 also exhibited delayed expression in apomictic genotypes when compared with sexual types. Most importantly, the SCAR (Sequence Characterized Amplified Region) derived from the APOSTART_6 sequence completely co-segregated with apomixis.

Integrative analysis of the nuclear proteome in Pinus radiata reveals thermopriming coupled to epigenetic regulation

Despite it being an important issue in the context of climate change, for most plant species it is not currently known how abiotic stresses affect nuclear proteomes and mediate memory effects. This study examines how Pinus radiata nuclei respond, adapt, 'remember', and 'learn' from heat stress. Seedlings were heat-stressed at 45 °C for 10 d and then allowed to recover. Nuclear proteins were isolated and quantified by nLC-MS/MS, the dynamics of tissue DNA methylation were examined, and the potential acquired memory was analysed in recovered plants. In an additional experiment, the expression of key gene genes was also quantified. Specific nuclear heat-responsive proteins were identified, and their biological roles were evaluated using a systems biology approach. In addition to heat-shock proteins, several clusters involved in regulation processes were discovered, such as epigenomic-driven gene regulation, some transcription factors, and a variety of RNA-associated functions. Nuclei exhibited differential proteome profiles across the phases of the experiment, with histone H2A and methyl cycle enzymes in particular being accumulated in the recovery step. A thermopriming effect was possibly linked to H2A abundance and over-accumulation of spliceosome elements in recovered P. radiata plants. The results suggest that epigenetic mechanisms play a key role in heat-stress tolerance and priming mechanisms.

Predicting Endoplasmic Reticulum Resident Proteins Using Auto-Cross Covariance Transformation With a U-Shaped Residue Weight-Transfer Function

Background: The endoplasmic reticulum (ER) is an important organelle in eukaryotic cells. It is involved in many important biological processes, such as cell metabolism, protein synthesis, and post-translational modification. The proteins that reside within the ER are called ER-resident proteins. These proteins are closely related to the biological functions of the ER. The difference between the ER-resident proteins and other non-resident proteins should be carefully studied.

Methods: We developed a support vector machine (SVM)-based method. We developed a U-shaped weight-transfer function and used it, along with the positional-specific physiochemical properties (PSPCP), to integrate together sequence order information, signaling peptides information, and evolutionary information.

Result: Our method achieved over 86% accuracy in a jackknife test. We also achieved roughly 86% sensitivity and 67% specificity in an independent dataset test. Our method is capable of identifying ER-resident proteins.

Novel redox-active enzymes for ligninolytic applications revealed from multiomics analyses of Peniophora sp. CBMAI 1063, a laccase hyper-producer strain

The repertoire of redox-active enzymes produced by the marine fungus Peniophora sp. CBMAI 1063, a laccase hyper-producer strain, was characterized by omics analyses. The genome revealed 309 Carbohydrate-Active Enzymes (CAZymes) genes, including 48 predicted genes related to the modification and degradation of lignin, whith 303 being transcribed under cultivation in optimized saline conditions for laccase production. The secretome confirmed that the fungus can produce a versatile ligninolytic enzyme cocktail. It secretes 56 CAZymes, including 11 oxidative enzymes classified as members of auxiliary activity families (AAs), comprising two laccases, Pnh_Lac1 and Pnh_Lac2, the first is the major secretory protein of the fungi. The Pnh_Lac1-mediator system was able to promote the depolymerization of lignin fragments and polymeric lignin removal from pretreated sugarcane bagasse, confirming viability of this fungus enzymatic system for lignocellulose-based bioproducts applications.

ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites

Background:

The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem.

Observation:

Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular localization are not efficiently taken into account. In this paper, we propose an integrated method for predicting protein subcellular localizations with both single site and multiple sites.

Methods:

Firstly, we extend the Multi-Label Radial Basis Function (ML-RBF) method to the regularized version, and augment the first layer of ML-RBF to take local correlations between subcellular localization into account. Secondly, we embed the modified ML-RBF into a multi-label Error-Correcting Output Codes (ECOC) method in order to further consider the subcellular localization dependency. We name our method ML-rRBF-ECOC. Finally, the performance of ML-rRBF-ECOC is evaluated on three benchmark datasets.

Results:

The results demonstrate that ML-rRBF-ECOC has highly competitive performance to the related multi-label learning method and some state-of-the-art methods for predicting protein subcellular localizations with multiple sites. Considering dependency between subcellular localizations can contribute to the improvement of prediction performance.

Conclusion:

This also indicates that correlations between different subcellular localizations really exist. Our method at least plays a complementary role to existing methods for predicting protein subcellular localizations with multiple sites.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Background:

Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods.

Methods:

In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers.

Result & Conclusion:

Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

A Brief History of Protein Sorting Prediction

Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

In SilicoDesigning a Novel Multi-epitope DNA Vaccine against Anti-apoptotic Proteins in Tumor Cells

Background:

Cancer therapy has been known as one of the most important challenges in the world. Various therapeutic methods such as cancer immunotherapy are used to eradicate tumor cells. Vaccines have an important role among different cancer immunotherapeutic approaches. In the field of vaccine production, bioinformatics approach is considered as a useful tool to design multi-epitope cancer vaccines, mainly for selecting immunodominant Cytotoxic T Lymphocytes (CTL) and Helper T Lymphocytes (HTL) epitopes.

Objective:

Generally, to design efficient multi-epitope cancer vaccines, Tumor-Specific Antigens (TSA) are targeted. In the context of DNA-based cancer vaccines, they contain genes that code tumor antigens and are delivered to host by different methods.

Methods:

In this study, the anti-apoptotic proteins (BCL2, BCL-X, survivin) that are over-expressed in different tumor cells were selected for CTL and HTL epitopes prediction through different servers such as RANKPEP, CTLpred, and BCPREDS.

Results:

Three regions from BCL2 and one region from BCL-X were selected as CTL epitopes and two segments from survivin were defined as HTL epitopes. In addition, β-defensin was used as a proper adjuvant to enhance vaccine efficacy. The aforesaid segments were joined together by appropriate linkers, and some important properties of designed vaccine such as antigenicity, allergenicity and physicochemical characteristics were determined by various bioinformatics servers.

Conclusion:

Based on the bioinformatics results, the physicochemical and immunological features showed that the designed vaccine construct can be used as an efficient cancer vaccine after its efficacy was confirmed by in vitro and in vivo immunological assays.

Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification

Background

Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it carries severe outcomes and can lead to death. It is thus important to detect the disease as early as possible, which can help devise effective intervention and treatment plan.

Here we investigate ways to utilize information available in electronic health records (EHRs) from regular office visits of more than 13,000 patients, in order to distinguish among several stages of the disease. While clinical data stored in EHRs provide valuable information for risk-stratification, one of the major challenges in using them arises from data imbalance. That is, records associated with a more severe condition are typically under-represented compared to those associated with a milder manifestation of the disease. To address imbalance, we propose and develop a sampling-based ensemble approach, hierarchical meta-classification, aiming to stratify CKD patients into severity stages, using simple quantitative non-text features gathered from standard office visit records.

Methods

The proposed hierarchical meta-classification method frames the multiclass classification task as a hierarchy of two subtasks. The first is binary classification, separating records associated with the majority class from those associated with all minority classes combined, using meta-classification. The second subtask separates the records assigned to the combined minority classes into the individual constituent classes.

Results

The proposed method identifies a significant proportion of patients suffering from the more advanced stages of the condition, while also correctly identifying most of the less severe cases, maintaining high sensitivity, specificity and F-measure (≥ 93%). Our results show that the high level of performance attained by our method is preserved even when the size of the training set is significantly reduced, demonstrating the stability and generalizability of our approach.

Conclusion

We present a new approach to perform classification while addressing data imbalance, which is inherent in the biomedical domain. Our model effectively identifies severity stages of CKD patients, using information readily available in office visit records within the realistic context of high data imbalance.

Genome-wide analysis of the invertase gene family from maize

The recent release of the maize genome (AGPv4) contains annotation errors of invertase genes and therefore the enzymes are bestly curated manually at the protein level in a comprehensible fashion The synthesis, transport and degradation of sucrose are determining factors for biomass allocation and yield of crop plants. Invertase (INV) is a key enzyme of carbon metabolism in both source and sink tissues. Current releases of the maize genome correctly annotates only two vacuolar invertases (ivr1 and ivr2) and four cell wall invertases (incw1, incw2 (mn1), incw3, and incw4). Our comprehensive survey identified 21 INV isogenes for which we propose a standard nomenclature grouped phylogenetically by amino acid similarity: three vacuolar (INVVR), eight cell wall (INVCW), and ten alkaline/neutral (INVAN) isogenes which form separate dendogram branches due to distinct molecular features. The acidic enzymes were curated for the presence of the DPN tripeptide which is coded by one of the smallest exons reported in plants. Particular attention was placed on the molecular role of INV in vascular tissues such as the nodes, internodes, leaf sheath, husk leaves and roots. We report the expression profile of most members of the maize INV family in nine tissues in two developmental stages, R1 and R3. INVCW7, INVVR2, INVAN8, INVAN9, INVAN10, and INVAN3 displayed the highest absolute expressions in most tissues. INVVR3, INVCW5, INVCW8, and INVAN1 showed low mRNA levels. Expressions of most INVs were repressed from stage R1 to R3, except for INVCW7 which increased significantly in all tissues after flowering. The mRNA levels of INVCW7 in the vegetative stem correlated with a higher transport rate of assimilates from leaves to the cob which led to starch accumulation and growth of the female reproductive organs.

Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

BACKGROUND

Apoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task.

RESULTS

In this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods.

CONCLUSION

The experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/ .

Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

BACKGROUND

Sugarcane bagasse has been proposed as a lignocellulosic residue for second-generation ethanol (2G) produced by breaking down biomass into fermentable sugars. The enzymatic cocktails for biomass degradation are mostly produced by fungi, but low cost and high efficiency can consolidate 2G technologies. A. fumigatus plays an important role in plant biomass degradation capabilities and recycling. To gain more insight into the divergence in gene expression during steam-exploded bagasse (SEB) breakdown, this study profiled the transcriptome of A. fumigatus by RNA sequencing to compare transcriptional profiles of A. fumigatus grown on media containing SEB or fructose as the sole carbon source. Secretome analysis was also performed using SDS-PAGE and LC-MS/MS.

RESULTS

The maximum activities of cellulases (0.032 U mL-1), endo-1,4-β--xylanase (10.82 U mL-1) and endo-1,3-β glucanases (0.77 U mL-1) showed that functional CAZymes (carbohydrate-active enzymes) were secreted in the SEB culture conditions. Correlations between transcriptome and secretome data identified several CAZymes in A. fumigatus. Particular attention was given to CAZymes related to lignocellulose degradation and sugar transporters. Genes encoding glycoside hydrolase classes commonly expressed during the breakdown of cellulose, such as GH-5, 6, 7, 43, 45, and hemicellulose, such as GH-2, 10, 11, 30, 43, were found to be highly expressed in SEB conditions. Lytic polysaccharide monooxygenases (LPMO) classified as auxiliary activity families AA9 (GH61), CE (1, 4, 8, 15, 16), PL (1, 3, 4, 20) and GT (1, 2, 4, 8, 20, 35, 48) were also differentially expressed in this condition. Similarly, the most important enzymes related to biomass degradation, including endoxylanases, xyloglucanases, β-xylosidases, LPMOs, α-arabinofuranosidases, cellobiohydrolases, endoglucanases and β-glucosidases, were also identified in the secretome.

CONCLUSIONS

This is the first report of a transcriptome and secretome experiment of Aspergillus fumigatus in the degradation of pretreated sugarcane bagasse. The results suggest that this strain employs important strategies for this complex degradation process. It was possible to identify a set of genes and proteins that might be applied in several biotechnology fields. This knowledge can be exploited for the improvement of 2G ethanol production by the rational design of enzymatic cocktails.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC.

Complex regulation of the TaMyc1 gene expression in wheat grain synthesizing anthocyanin pigments

The wheat TaMyc1 gene encodes for transcriptional factor (TF) with bHLH domain. The gene is expressed in purple wheat grains and activates transcription of the anthocyanin biosynthesis structural genes. To reveal the features of TaMyc1 regulation in wheat pericarp transcription start sites (TSS) were identified by 5' RACE mean and translation efficiency was predicted by in silico methods. Three alternative transcript variants of TaMyc1 differing by 5' leader sequence only were identified in purple pericarp. The three transcripts are generated from distinct TATA boxes and thereby are differed by TSS. Two transcripts (TaMyc1a, -b) have identical initiation AUG codons that lead to the TaMYC1 regulatory protein with bHLH domain. However because of different stability of secondary structures predicted in 5' leader the two transcripts might be translated with different efficiency. The third transcript is assumed to be not effectively translated. qRT-PCR and colonies counting were applied to assess contribution each of the transcripts to total TaMyc1 gene transcription level. TaMyc1c has the lowest contribution (ca. 16%), whereas the others two transcripts contribute equally (ca. 42%) to total TaMyc1 expression level. The role of the tree mRNA isoforms transcribed in one tissue is discussed.

Lysine Succinylation Contributes to Aflatoxin Production and Pathogenicity in Aspergillus flavus

Aspergillus flavus (A. flavus) is a ubiquitous saprophytic and pathogenic fungus that produces the aflatoxin carcinogen, and A. flavus can have tremendous economic and health impacts worldwide. Increasing evidence demonstrates that lysine succinylation plays an important regulatory role in metabolic processes in both bacterial and human cells. However, little is known about the extent and function of lysine succinylation in A. flavus. Here, we performed a global succinylome analysis of A. flavus using high accuracy nano-LC-MS/MS in combination with the enrichment of succinylated peptides from digested cell lysates and subsequent peptide identification. In total, 985 succinylation sites on 349 succinylated proteins were identified in this pathogen. Bioinformatics analysis revealed that the succinylated proteins were involved in various biological processes and were particularly enriched in the aflatoxin biosynthesis process. Site-specific mutagenesis and biochemical studies showed that lysine succinylation on the norsolorinic acid reductase NorA (AflE), a key enzyme in aflatoxins biosynthesis, can affect the production of sclerotia and aflatoxins biosynthesis in A. flavus. Together, our findings reveal widespread roles for lysine succinylation in regulating metabolism and aflatoxins biosynthesis in A. flavus. Our data provide a rich resource for functional analyses of lysine succinylation and facilitate the dissection of metabolic networks in this pathogen.

Prolonged Expression of a Putative Invertase Inhibitor in Micropylar Endosperm Suppressed Embryo Growth in Arabidopsis

Proper seed development requires coordinated growth among the three genetically distinct components, the embryo, the endosperm, and the seed coat. In Arabidopsis, embryo growth rate accelerates after endosperm cellularization, which requires a chromatin-remodeling complex, the FIS2-Polycomb Repressive Complex 2 (PRC2). After cellularization, the endosperm ceases to grow and is eventually absorbed by the embryo. This sequential growth pattern displayed by the endosperm and the embryo suggests a possibility that the supply of sugar might be shifted from the endosperm to the embryo upon endosperm cellularization. Since invertases and invertase inhibitors play an important role in sugar partition, we investigated their expression pattern during early stages of seed development in Arabidopsis. Two putative invertase inhibitors (InvINH1 and InvINH2) were identified as being preferentially expressed in the micropylar endosperm that surrounds the embryo. After endosperm cellularization, InvINH1 and InvINH2 were down-regulated in a FIS2-dependent manner. We hypothesized that FIS2-PRC2 complex either directly or indirectly represses InvINH1 and InvINH2 to increase invertase activity around the embryo, making more hexose available to support the accelerated embryo growth after endosperm cellularization. In support of our hypothesis, embryo growth was delayed in transgenic lines that ectopically expressed InvINH1 in the cellularized endosperm. Our data suggested a novel mechanism for the FIS2-PRC2 complex to control embryo growth rate via the regulation of invertase activity in the endosperm.

The truncated NLR protein TIR-NBS13 is a MOS6/IMPORTIN-α3 interaction partner required for plant immunity

Importin-α proteins mediate the translocation of nuclear localization signal (NLS)-containing proteins from the cytoplasm into the nucleus through nuclear pore complexes (NPCs). Genetically, Arabidopsis IMPORTIN-α3/MOS6 (MODIFIER OF SNC1, 6) is required for basal plant immunity and constitutive disease resistance activated in autoimmune mutant snc1 (suppressor of npr1-1, constitutive 1), suggesting that MOS6 plays a role in the nuclear import of proteins involved in plant defense signaling. Here, we sought to identify and characterize defense-regulatory cargo proteins and interaction partners of MOS6. We conducted both in silico database analyses and affinity purification of functional epitope-tagged MOS6 from pathogen-challenged stable transgenic plants coupled with mass spectrometry. We show that among the 13 candidate MOS6 interactors we selected for further functional characterization, the TIR-NBS-type protein TN13 is required for resistance against Pseudomonas syringae pv. tomato (Pst) DC3000 lacking the type-III effector proteins AvrPto and AvrPtoB. When expressed transiently in N. benthamiana leaves, TN13 co-immunoprecipitates with MOS6, but not with its closest homolog IMPORTIN-α6, and localizes to the endoplasmic reticulum (ER), consistent with a predicted N-terminal transmembrane domain in TN13. Our work uncovered the truncated NLR protein TN13 as a component of plant innate immunity that selectively binds to MOS6/IMPORTIN-α3 in planta. We speculate that the release of TN13 from the ER membrane in response to pathogen stimulus, and its subsequent nuclear translocation, is important for plant defense signal transduction.

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD)

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area.

Database URL:www.informatics.jax.org

A single Danio rerio hars gene encodes both cytoplasmic and mitochondrial histidyl-tRNA synthetases

Histidyl tRNA Synthetase (HARS) is a member of the aminoacyl tRNA synthetase (ARS) family of enzymes. This family of 20 enzymes is responsible for attaching specific amino acids to their cognate tRNA molecules, a critical step in protein synthesis. However, recent work highlighting a growing number of associations between ARS genes and diverse human diseases raises the possibility of new and unexpected functions in this ancient enzyme family. For example, mutations in HARS have been linked to two different neurological disorders, Usher Syndrome Type IIIB and Charcot Marie Tooth peripheral neuropathy. These connections raise the possibility of previously undiscovered roles for HARS in metazoan development, with alterations in these functions leading to complex diseases. In an attempt to establish Danio rerio as a model for studying HARS functions in human disease, we characterized the Danio rerio hars gene and compared it to that of human HARS. Using a combination of bioinformatics, molecular biology, and cellular approaches, we found that while the human genome encodes separate genes for cytoplasmic and mitochondrial HARS protein, the Danio rerio genome encodes a single hars gene which undergoes alternative splicing to produce the respective cytoplasmic and mitochondrial versions of Hars. Nevertheless, while the HARS genes of humans and Danio differ significantly at the genomic level, we found that they are still highly conserved at the amino acid level, underscoring the potential utility of Danio rerio as a model organism for investigating HARS function and its link to human diseases in vivo.

Pyruvate kinase M knockdown-induced signaling via AMP-activated protein kinase promotes mitochondrial biogenesis, autophagy, and cancer cell survival

Preferential expression of the low-activity (dimeric) M2 isoform of pyruvate kinase (PK) over its constitutively active splice variant M1 isoform is considered critical for aerobic glycolysis in cancer cells. However, our results reported here indicate co-expression of PKM1 and PKM2 and their possible physical interaction in cancer cells. We show that knockdown of either PKM1 or PKM2 differentially affects net PK activity, viability, and cellular ATP levels of the lung carcinoma cell lines H1299 and A549. The stable knockdown of PK isoforms in A549 cells significantly reduced the cellular ATP level, whereas in H1299 cells the level of ATP was unaltered. Interestingly, the PKM1/2 knockdown in H1299 cells activated AMP-activated protein kinase (AMPK) signaling and stimulated mitochondrial biogenesis and autophagy to maintain energy homeostasis. In contrast, knocking down either of the PKM isoforms in A549 cells lacking LKB1, a serine/threonine protein kinase upstream of AMPK, failed to activate AMPK and sustain energy homeostasis and resulted in apoptosis. Moreover, in a similar genetic background of silenced PKM1 or PKM2, the knocking down of AMPKα1/2 catalytic subunit in H1299 cells induced apoptosis. Our findings help explain why previous targeting of PKM2 in cancer cells to control tumor growth has not met with the expected success. We suggest that this lack of success is because of AMPK-mediated energy metabolism rewiring, protecting cancer cell viability. On the basis of our observations, we propose an alternative therapeutic strategy of silencing either of the PKM isoforms along with AMPK in tumors.

Paired Expression Analysis of Tumor Cell Surface Antigens

Adoptive immunotherapy with antibody-based therapy or with T cells transduced to express chimeric antigen receptors (CARs) is useful to the extent that the cell surface membrane protein being targeted is not expressed on normal tissues. The most successful CAR-based (anti-CD19) or antibody-based therapy (anti-CD20) in hematologic malignancies has the side effect of eliminating the normal B cell compartment. Targeting solid tumors may not provide a similar expendable marker. Beyond antibody to Her2/NEU and EGFR, very few antibody-based and no CAR-based therapies have seen broad clinical application for solid tumors. To expand the way in which the surfaceome of solid tumors can be analyzed, we created an algorithm that defines the pairwise relative overexpression of surface antigens. This enables the development of specific immunotherapies that require the expression of two discrete antigens on the surface of the tumor target. This dyad analysis was facilitated by employing the Hotelling’s T-squared test (Hotelling–Lawley multivariate analysis of variance) for two independent variables in comparison to a third constant entity (i.e., gene expression levels in normal tissues). We also present a unique consensus scoring mechanism for identifying transcripts that encode cell surface proteins. The unique application of our bioinformatics processing pipeline and statistical tools allowed us to compare the expression of two membrane protein targets as a pair, and to propose a new strategy based on implementing immunotherapies that require both antigens to be expressed on the tumor cell surface to trigger therapeutic effector mechanisms. Specifically, we found that, for MYCN amplified neuroblastoma, pairwise expression of ACVR2B or anaplastic lymphoma kinase (ALK) with GFRA3, GFRA2, Cadherin 24, or with one another provided the strongest hits. For MYCN, non-amplified stage 4 neuroblastoma, neurotrophic tyrosine kinase 1, or ALK paired with GFRA2, GFRA3, SSK1, GPR173, or with one another provided the most promising paired-hits. We propose that targeting these markers together would increase the specificity and thereby the safety of CAR-based therapy for neuroblastoma.

Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

Predicting human protein subcellular localization by heterogeneous and comprehensive approaches

Drug development and investigation of protein function both require an understanding of protein subcellular localization. We developed a system, REALoc, that can predict the subcellular localization of singleplex and multiplex proteins in humans. This system, based on comprehensive strategy, consists of two heterogeneous systematic frameworks that integrate one-to-one and many-to-many machine learning methods and use sequence-based features, including amino acid composition, surface accessibility, weighted sign aa index, and sequence similarity profile, as well as gene ontology function-based features. REALoc can be used to predict localization to six subcellular compartments (cell membrane, cytoplasm, endoplasmic reticulum/Golgi, mitochondrion, nucleus, and extracellular). REALoc yielded a 75.3% absolute true success rate during five-fold cross-validation and a 57.1% absolute true success rate in an independent database test, which was >10% higher than six other prediction systems. Lastly, we analyzed the effects of Vote and GANN models on singleplex and multiplex localization prediction efficacy. REALoc is freely available at http://predictor.nchu.edu.tw/REALoc.