InParanoid 7: new algorithms and tools for eukaryotic orthology analysis

Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks

MAIN CONCLUSION

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

Construction of protein-protein interaction network in sulfate-reducing bacteria: Unveiling of global response to Hg

Sulfate-reducing bacteria (SRB) play pivotal roles in the biotransformation of mercury (Hg). However, unrevealed global responses of SRB to Hg have restricted our understanding of details of Hg biotransformation processes. The absence of protein-protein interaction (PPI) network under Hg stimuli has been a bottleneck of proteomic analysis for molecular mechanisms of Hg transformation. This study constructed the first comprehensive PPI network of SRB in response to Hg, encompassing 67 connected nodes, 26 independent nodes, and 121 edges, covering 93% of differentially expressed proteins from both previous studies and this study. The network suggested that proteomic changes of SRB in response to Hg occurred globally, including microbial metabolism in diverse environments, carbon metabolism, nucleic acid metabolism and translation, nucleic acid repair, transport systems, nitrogen metabolism, and methyltransferase activity, partial of which could cover the known knowledge. Antibiotic resistance was the original response revealed by this network, providing insights into of Hg biotransformation mechanisms. This study firstly provided the foundational network for a comprehensive understanding of SRB's responses to Hg, convenient for exploration of potential targets for Hg biotransformation. Furthermore, the network indicated that Hg enhances the metabolic activities and modification pathways of SRB to maintain cellular activities, shedding light on the influences of Hg on the carbon, nitrogen, and sulfur cycles at the cellular level.

A protein network refinement method based on module discovery and biological information

BACKGROUND

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.

METHODS

Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.

RESULTS

To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Reference genome of the nutrition-rich orphan crop chia (Salvia hispanica) and its implications for future breeding

Chia (Salvia hispanica L.) is one of the most popular nutrition-rich foods and pseudocereal crops of the family Lamiaceae. Chia seeds are a rich source of proteins, polyunsaturated fatty acids (PUFAs), dietary fibers, and antioxidants. In this study, we present the assembly of the chia reference genome, which spans 303.6 Mb and encodes 48,090 annotated protein-coding genes. Our analysis revealed that ~42% of the chia genome harbors repetitive content, and identified ~3 million single nucleotide polymorphisms (SNPs) and 15,380 simple sequence repeat (SSR) marker sites. By investigating the chia transcriptome, we discovered that ~44% of the genes undergo alternative splicing with a higher frequency of intron retention events. Additionally, we identified chia genes associated with important nutrient content and quality traits, such as the biosynthesis of PUFAs and seed mucilage fiber (dietary fiber) polysaccharides. Notably, this is the first report of in-silico annotation of a plant genome for protein-derived small bioactive peptides (biopeptides) associated with improving human health. To facilitate further research and translational applications of this valuable orphan crop, we have developed the Salvia genomics database (SalviaGDB), accessible at https://salviagdb.org.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

A disease-related essential protein prediction model based on the transfer neural network

Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.

The genome of the king protea, Protea cynaroides

The king protea (Protea cynaroides), an early-diverging eudicot, is the most iconic species from the Megadiverse Cape Floristic Region, and the national flower of South Africa. Perhaps best known for its iconic flower head, Protea is a key genus for the South African horticulture industry and cut-flower market. Ecologically, the genus and the family Proteaceae are important models for radiation and adaptation, particularly to soils with limited phosphorus bio-availability. Here, we present a high-quality chromosome-scale assembly of the P. cynaroides genome as the first representative of the fynbos biome. We reveal an ancestral whole-genome duplication event that occurred in the Proteaceae around the late Cretaceous that preceded the divergence of all crown groups within the family and its extant diversity in all Southern continents. The relatively stable genome structure of P. cynaroides is invaluable for comparative studies and for unveiling paleopolyploidy in other groups, such as the distantly related sister group Ranunculales. Comparative genomics in sequenced genomes of the Proteales shows loss of key arbuscular mycorrhizal symbiosis genes likely ancestral to the family, and possibly the order. The P. cynaroides genome empowers new research in plant diversification, horticulture and adaptation, particularly to nutrient-poor soils.

Identification of Essential Proteins Based on Local Random Walk and Adaptive Multi-View Multi-Label Learning

Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins have been developing rapidly, there are as well various limitations such as unsatisfactory data suitability, low accuracy of predictive results and so on. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on the Random Walk and the Adaptive Multi-View multi-label Learning. In RWAMVL, considering that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks were obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method was designed to detect essential proteins based on these different features. Finally, in order to verify the predictive performance of RWAMVL, intensive experiments were done to compare it with multiple state-of-the-art predictive methods under different expeditionary frameworks. And as a result, RWAMVL was proven that it can achieve better prediction accuracy than all those competitive methods, which demonstrated as well that RWAMVL may be a potential tool for prediction of key proteins in the future.

Identifying essential proteins from protein-protein interaction networks based on influence maximization

BACKGROUND

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein-protein interaction (PPI) data, computationally identifying essential proteins from protein-protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed.

RESULTS

In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism.

CONCLUSIONS

We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

Evolutionary analyses of genes in Echinodermata offer insights towards the origin of metazoan phyla

Despite recent studies discussing the evolutionary impacts of gene duplications and losses among metazoans, the genomic basis for the evolution of phyla remains enigmatic. Here, we employ phylogenomic approaches to search for orthologous genes without known functions among echinoderms, and subsequently use them to guide the identification of their homologs across other metazoans. Our final set of 14 genes was obtained via a suite of homology prediction tools, gene expression data, gene ontology, and generating the Strongylocentrotus purpuratus phylome. The gene set was subjected to selection pressure analyses, which indicated that they are highly conserved and under negative selection. Their presence across broad taxonomic depths suggests that genes required to form a phylum are ancestral to that phylum. Therefore, rather than de novo gene genesis, we posit that evolutionary forces such as selection on existing genomic elements over large timescales may drive divergence and contribute to the emergence of phyla.

Cross-Species Analysis Reveals Co-Expressed Genes Regulating Antler Development in Cervidae

Antlers constitute an interesting model for basic research in regenerative biology. Despite decades of being studied, much is still unknown about the genes related to antler development. Here, we utilized both the genome and antlerogenic periosteum (AP) transcriptome data of four deer species to reveal antler-related genes through cross-species comparative analysis. The results showed that the global gene expression pattern matches the status of antler phenotypes, supporting the fact that the genes expressed in the AP may be related to antler phenotypes. The upregulated genes of the AP in three-antlered deer showed evidence of co-expression, and their protein sequences were highly conserved. These genes were growth related and likely participated in antler development. In contrast, the upregulated genes in antler-less deer (Chinese water deer) were involved mainly in organismal death and growth failure, possibly related to the loss of antlers during evolution. Overall, this study demonstrates that the co-expressed genes in antlered deer may regulate antler development.

InParanoid-DIAMOND: faster orthology analysis with the InParanoid algorithm

SUMMARY

Predicting orthologs, genes in different species having shared ancestry, is an important task in bioinformatics. Orthology prediction tools are required to make accurate and fast predictions, in order to analyze large amounts of data within a feasible time frame. InParanoid is a well-known algorithm for orthology analysis, shown to perform well in benchmarks, but having the major limitation of long runtimes on large datasets. Here, we present an update to the InParanoid algorithm that can use the faster tool DIAMOND instead of BLAST for the homolog search step. We show that it reduces the runtime by 94%, while still obtaining similar performance in the Quest for Orthologs benchmark.

AVAILABILITY AND IMPLEMENTATION

The source code is available at (https://bitbucket.org/sonnhammergroup/inparanoid).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information

High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.

A Novel Method for Predicting Essential Proteins by Integrating Multidimensional Biological Attribute Information and Topological Properties

Background:

Essential proteins are indispensable to the maintenance of life activities and play essential roles in the areas of synthetic biology. Identification of essential proteins by computational methods has become a hot topic in recent years because of its efficiency.

Objective:

Identification of essential proteins is of important significance and practical use in the areas of synthetic biology, drug targets, and human disease genes.

Method:

In this paper, a method called EOP(Edge clustering coefficient -Orthologous-Protein) is proposed to infer potential essential proteins by combining Multidimensional Biological Attribute Information of proteins with Topological Properties of the protein-protein interaction network.

Results:

The simulation results on the yeast protein interaction network show that the number of essential proteins identified by this method is more than the number identified by the other 12 methods(DC, IC, EC, SC, BC, CC, NC, LAC, PEC, CoEWC, POEM, DWE). Especially compared with DC（Degree Centrality）, the SN(sensitivity) is 9% higher, when the candidate protein is 1%, the recognition rate is 34% higher, when the candidate protein is 5%, 10%, 15%, 20%, 25% the recognition rate is 36%, 22%, 15%, 11%, 8% higher respectively.

Conclusion:

Experimental results show that our method can achieve satisfactory prediction results, which may provide references for future research.

Data-driven identification of inherent features of eukaryotic stress-responsive genes

Living organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms

Echinobase (www.echinobase.org) is a third generation web resource supporting genomic research on echinoderms. The new version was built by cloning the mature Xenopus model organism knowledgebase, Xenbase, refactoring data ingestion pipelines and modifying the user interface to adapt to multispecies echinoderm content. This approach leveraged over 15 years of previous database and web application development to generate a new fully featured informatics resource in a single year. In addition to the software stack, Echinobase uses the private cloud and physical hosts that support Xenbase. Echinobase currently supports six echinoderm species, focused on those used for genomics, developmental biology and gene regulatory network analyses. Over 38 000 gene pages, 18 000 publications, new improved genome assemblies, JBrowse genome browser and BLAST + services are available and supported by the development of a new echinoderm anatomical ontology, uniformly applied formal gene nomenclature, and consistent orthology predictions. A novel feature of Echinobase is integrating support for multiple, disparate species. New genomes from the diverse echinoderm phylum will be added and supported as data becomes available. The common code development design of the integrated knowledgebases ensures parallel improvements as each resource evolves. This approach is widely applicable for developing new model organism informatics resources.

How to Use the Candida Genome Database

The Candida Genome Database provides access to biological information about genes and proteins of several medically important Candida species. The website is organized into easily navigable pages that enable data retrieval and analysis. This chapter shows how to explore the CGD Home page and Locus Summary pages, which are the main access points to the database. It also provides a description of how to use the GO analysis tools, GO Term Finder, and GO Slim Mapper and how to browse large-scale datasets using the JBrowse genome browser. Finally, it shows how to search and retrieve data for user-defined sets of genes using the Advanced Search and Batch Download tools.

A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins

Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein–domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein–domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.

Tracing Eukaryotic Ribosome Biogenesis Factors Into the Archaeal Domain Sheds Light on the Evolution of Functional Complexity

Ribosome assembly is an essential and carefully choreographed cellular process. In eukaryotes, several 100 proteins, distributed across the nucleolus, nucleus, and cytoplasm, co-ordinate the step-wise assembly of four ribosomal RNAs (rRNAs) and approximately 80 ribosomal proteins (RPs) into the mature ribosomal subunits. Due to the inherent complexity of the assembly process, functional studies identifying ribosome biogenesis factors and, more importantly, their precise functions and interplay are confined to a few and very well-established model organisms. Although best characterized in yeast (Saccharomyces cerevisiae), emerging links to disease and the discovery of additional layers of regulation have recently encouraged deeper analysis of the pathway in human cells. In archaea, ribosome biogenesis is less well-understood. However, their simpler sub-cellular structure should allow a less elaborated assembly procedure, potentially providing insights into the functional essentials of ribosome biogenesis that evolved long before the diversification of archaea and eukaryotes. Here, we use a comprehensive phylogenetic profiling setup, integrating targeted ortholog searches with automated scoring of protein domain architecture similarities and an assessment of when search sensitivity becomes limiting, to trace 301 curated eukaryotic ribosome biogenesis factors across 982 taxa spanning the tree of life and including 727 archaea. We show that both factor loss and lineage-specific modifications of factor function modulate ribosome biogenesis, and we highlight that limited sensitivity of the ortholog search can confound evolutionary conclusions. Projecting into the archaeal domain, we find that only few factors are consistently present across the analyzed taxa, and lineage-specific loss is common. While members of the Asgard group are not special with respect to their inventory of ribosome biogenesis factors (RBFs), they unite the highest number of orthologs to eukaryotic RBFs in one taxon. Using large ribosomal subunit maturation as an example, we demonstrate that archaea pursue a simplified version of the corresponding steps in eukaryotes. Much of the complexity of this process evolved on the eukaryotic lineage by the duplication of ribosomal proteins and their subsequent functional diversification into ribosome biogenesis factors. This highlights that studying ribosome biogenesis in archaea provides fundamental information also for understanding the process in eukaryotes.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background

Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.

Results

In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.

Conclusions

We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

BACKGROUND

Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand.

RESULTS

In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein-protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles.

CONCLUSIONS

Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application.

Identification and validation of 12 immune-related genes as a prognostic signature for colon adenocarcinoma

Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive tract that threatens human health seriously. Thus, it is urgent to explore biomarkers that can be used to evaluate a patient's survival prognosis overall as a supplementary treatment. RNA-seq expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, and Lasso and multivariate Cox regression analyses were used for developing the prognostic model. Finally, a nomogram comprising the prognostic model was established to evaluate survival overall. A risk model comprised of a total of 12 immune-related gene pairs was constructed. Further analysis revealed the model's independent prognostic ability in relation to other clinical characteristics. This model's nomogram could help clinicians choose personalized treatment for COAD patients. This model has significant potential to complement COAD's clinical identifying characteristics, and also provide new insights into the identification of colon cancer patients with a high risk of death.

A nomenclature for echinoderm genes

Echinoderm embryos and larvae are prominent experimental model systems for studying developmental mechanisms. High-quality, assembled, annotated genome sequences are now available for several echinoderm species, including representatives from most classes. The increased availability of these data necessitates the development of a nomenclature that assigns universally interpretable gene symbols to echinoderm genes to facilitate cross-species comparisons of gene functions, both within echinoderms and across other phyla. This paper describes the implementation of an improved set of echinoderm gene nomenclature guidelines that both communicates meaningful orthology information in protein-coding gene symbols and names and establishes continuity with nomenclatures developed for major vertebrate model organisms, including humans. Differences between the echinoderm gene nomenclature guidelines and vertebrate guidelines are examined and explained. This nomenclature incorporates novel solutions to allow for several types of orthologous relationships, including the single echinoderm genes with multiple vertebrate co-orthologs that result from whole-genome-duplication events. The current version of the Echinoderm Gene Nomenclature Guidelines can be found at https://www.echinobase.org/gene/static/geneNomenclature.jsp

Database URL https://www.echinobase.org/

Distinct mutations in importin-β family nucleocytoplasmic transport receptors transportin-SR and importin-13 affect specific cargo binding

Importin-(Imp)β family nucleocytoplasmic transport receptors (NTRs) are supposed to bind to their cargoes through interaction between a confined interface on an NTR and a nuclear localization or export signal (NLS/NES) on a cargo. Although consensus NLS/NES sequence motifs have been defined for cargoes of some NTRs, many experimentally identified cargoes of those NTRs lack those motifs, and consensus NLSs/NESs have been reported for only a few NTRs. Crystal structures of NTR-cargo complexes have exemplified 3D structure-dependent binding of cargoes lacking a consensus NLS/NES to different sites on an NTR. Since only a limited number of NTR-cargo interactions have been studied, whether most cargoes lacking a consensus NLS/NES bind to the same confined interface or to various sites on an NTR is still unclear. Addressing this issue, we generated four mutants of transportin-(Trn)SR, of which many cargoes lack a consensus NLS, and eight mutants of Imp13, where no consensus NLS has been defined, and we analyzed their binding to as many as 40 cargo candidates that we previously identified by a nuclear import reaction-based method. The cargoes bind differently to the NTR mutants, suggesting that positions on an NTR contribute differently to the binding of respective cargoes.

A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

The Welwitschia genome reveals a unique biology underpinning extreme longevity in deserts

The gymnosperm Welwitschia mirabilis belongs to the ancient, enigmatic gnetophyte lineage. It is a unique desert plant with extreme longevity and two ever-elongating leaves. We present a chromosome-level assembly of its genome (6.8 Gb/1 C) together with methylome and transcriptome data to explore its astonishing biology. We also present a refined, high-quality assembly of Gnetum montanum to enhance our understanding of gnetophyte genome evolution. The Welwitschia genome has been shaped by a lineage-specific ancient, whole genome duplication (~86 million years ago) and more recently (1-2 million years) by bursts of retrotransposon activity. High levels of cytosine methylation (particularly at CHH motifs) are associated with retrotransposons, whilst long-term deamination has resulted in an exceptionally GC-poor genome. Changes in copy number and/or expression of gene families and transcription factors (e.g. R2R3MYB, SAUR) controlling cell growth, differentiation and metabolism underpin the plant's longevity and tolerance to temperature, nutrient and water stress.

Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.

Essential Protein Prediction Based on node2vec and XGBoost

Essential proteins are a vital part of the survival of organisms and cells. Identification of essential proteins lays a solid foundation for understanding protein functions and discovering drug targets. The traditional biological experiments are expensive and time-consuming. Recently, many computational methods have been proposed. However, some noises in the protein-protein interaction (PPI) networks affect the efficiency of essential protein prediction. It is necessary to construct a credible PPI network by using other useful biological information to reduce the effects of these noises. In this article, we proposed a model, Ess-NEXG, to identify essential proteins, which integrates biological information, including orthologous information, subcellular localization information, RNA-Seq information, and PPI network. In our model, first, we constructed a credible weighted PPI network by using different types of biological information. Second, we extracted the topological features of proteins in the constructed weighted PPI network by using the node2vec technique. Last, we used eXtreme Gradient Boosting (XGBoost) to predict essential proteins by using the topological features of proteins. The extensive results show that our model has better performance than other computational methods.

The Cowpea Kinome: Genomic and Transcriptomic Analysis Under Biotic and Abiotic Stresses

The present work represents a pioneering effort, being the first to analyze genomic and transcriptomic data from Vigna unguiculata (cowpea) kinases. We evaluated the cowpea kinome considering its genome-wide distribution and structural characteristics (at the gene and protein levels), sequence evolution, conservation among Viridiplantae species, and gene expression in three cowpea genotypes under different stress situations, including biotic (injury followed by virus inoculation-CABMV or CPSMV) and abiotic (root dehydration). The structural features of cowpea kinases (VuPKs) indicated that 1,293 bona fide VuPKs covered 20 groups and 118 different families. The RLK-Pelle was the largest group, with 908 members. Insights on the mechanisms of VuPK genomic expansion and conservation among Viridiplantae species indicated dispersed and tandem duplications as major forces for VuPKs' distribution pattern and high orthology indexes and synteny with other legume species, respectively. K _a /K _s ratios showed that almost all (91%) of the tandem duplication events were under purifying selection. Candidate cis-regulatory elements were associated with different transcription factors (TFs) in the promoter regions of the RLK-Pelle group. C2H2 TFs were closely associated with the promoter regions of almost all scrutinized families for the mentioned group. At the transcriptional level, it was suggested that VuPK up-regulation was stress, genotype, or tissue dependent (or a combination of them). The most prominent families in responding (up-regulation) to all the analyzed stresses were RLK-Pelle_DLSV and CAMK_CAMKL-CHK1. Concerning root dehydration, it was suggested that the up-regulated VuPKs are associated with ABA hormone signaling, auxin hormone transport, and potassium ion metabolism. Additionally, up-regulated VuPKs under root dehydration potentially assist in a critical physiological strategy of the studied cowpea genotype in this assay, with activation of defense mechanisms against biotic stress while responding to root dehydration. This study provides the foundation for further studies on the evolution and molecular function of VuPKs.

FAWMine: An integrated database and analysis platform for fall armyworm genomics

Fall armyworm (Spodoptera frugiperda), a native insect species in the Americas, is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn, rice, soybeans, and other crops. To control this pest, scientists have accumulated a great deal of high-throughput data of fall armyworm, and nine versions of its genomes and transcriptomes have been published. However, easily accessing and performing integrated analysis of these omics data sets is challenging. Here, we developed the Fall Armyworm Genome Database (FAWMine, http://159.226.67.243:8080/fawmine/) to maintain genome sequences, structural and functional annotations, transcriptomes, co-expression, protein interactions, homologs, pathways, and single-nucleotide variations. FAWMine provides a powerful framework that helps users to perform flexible and customized searching, present integrated data sets using diverse visualization methods, output results tables in a range of file formats, analyze candidate gene lists using multiple widgets, and query data available in other InterMine systems. Additionally, stand-alone JBrowse and BLAST services are also established, allowing the users to visualize RNA-Seq data and search genome and annotated gene sequences. Altogether, FAWMine is a useful tool for querying, visualizing, and analyzing compiled data sets rapidly and efficiently. FAWMine will be continually updated to function as a community resource for fall armyworm genomics and pest control research.

Integration of 1:1 orthology maps and updated datasets into Echinobase

Echinobase (https://echinobase.org) is a central online platform that generates, manages and hosts genomic data relevant to echinoderm research. While the resource primarily serves the echinoderm research community, the recent release of an excellent quality genome for the frequently studied purple sea urchin (Strongylocentrotus purpuratus genome, v5.0) has provided an opportunity to adapt to the needs of a broader research community across other model systems. To this end, establishing pipelines to identify orthologous genes between echinoderms and other species has become a priority in many contexts including nomenclature, linking to data in other model organisms, and in internal functionality where data gathered in one hosted species can be associated with genes in other hosted echinoderms. This paper describes the orthology pipelines currently employed by Echinobase and how orthology data are processed to yield 1:1 ortholog mappings between a variety of echinoderms and other model taxa. We also describe functions of interest that have recently been included on the resource, including an updated developmental time course for S.purpuratus, and additional tracks for genome browsing. These data enhancements will increase the accessibility of the resource to non-echinoderm researchers and simultaneously expand the data quality and quantity available to core Echinobase users. Database URL: https://echinobase.org.

Genome Architecture Facilitates Phenotypic Plasticity in the Honeybee (Apis mellifera)

Phenotypic plasticity, the ability of an organism to alter its phenotype in response to an environmental cue, facilitates rapid adaptation to changing environments. Plastic changes in morphology and behavior are underpinned by widespread gene expression changes. However, it is unknown if, or how, genomes are structured to ensure these robust responses. Here, we use repression of honeybee worker ovaries as a model of plasticity. We show that the honeybee genome is structured with respect to plasticity; genes that respond to an environmental trigger are colocated in the honeybee genome in a series of gene clusters, many of which have been assembled in the last 80 My during the evolution of the Apidae. These clusters are marked by histone modifications that prefigure the gene expression changes that occur as the ovary activates, suggesting that these genomic regions are poised to respond plastically. That the linear sequence of the honeybee genome is organized to coordinate widespread gene expression changes in response to environmental influences and that the chromatin organization in these regions is prefigured to respond to these influences is perhaps unexpected and has implications for other examples of plasticity in physiology, evolution, and human disease.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

Prevention and Control of Pathogens Based on Big-Data Mining and Visualization Analysis

Morbidity and mortality caused by infectious diseases rank first among all human illnesses. Many pathogenic mechanisms remain unclear, while misuse of antibiotics has led to the emergence of drug-resistant strains. Infectious diseases spread rapidly and pathogens mutate quickly, posing new threats to human health. However, with the increasing use of high-throughput screening of pathogen genomes, research based on big data mining and visualization analysis has gradually become a hot topic for studies of infectious disease prevention and control. In this paper, the framework was performed on four infectious pathogens (Fusobacterium, Streptococcus, Neisseria, and Streptococcus salivarius) through five functions: 1) genome annotation, 2) phylogeny analysis based on core genome, 3) analysis of structure differences between genomes, 4) prediction of virulence genes/factors with their pathogenic mechanisms, and 5) prediction of resistance genes/factors with their signaling pathways. The experiments were carried out from three angles: phylogeny (macro perspective), structure differences of genomes (micro perspective), and virulence and drug-resistance characteristics (prediction perspective). Therefore, the framework can not only provide evidence to support the rapid identification of new or unknown pathogens and thus plays a role in the prevention and control of infectious diseases, but also help to recommend the most appropriate strains for clinical and scientific research. This paper presented a new genome information visualization analysis process framework based on big data mining technology with the accommodation of the depth and breadth of pathogens in molecular level research.

Ab Initio Construction and Evolutionary Analysis of Protein-Coding Gene Families with Partially Homologous Relationships: Closely Related Drosophila Genomes as a Case Study

How have genes evolved within a well-known genome phylogeny? Many protein-coding genes should have evolved as a whole at the gene level, and some should have evolved partly through fragments at the subgene level. To comprehensively explore such complex homologous relationships and better understand gene family evolution, here, with de novo-identified modules, the subgene units which could consecutively cover proteins within a set of closely related species, we applied a new phylogeny-based approach that considers evolutionary models with partial homology to classify all protein-coding genes in nine Drosophila genomes. Compared with two other popular methods for gene family construction, our approach improved practical gene family classifications with a more reasonable view of homology and provided a much more complete landscape of gene family evolution at the gene and subgene levels. In the case study, we found that most expanded gene families might have evolved mainly through module rearrangements rather than gene duplications and mainly generated single-module genes through partial gene duplication, suggesting that there might be pervasive subgene rearrangement in the evolution of protein-coding gene families. The use of a phylogeny-based approach with partial homology to classify and analyze protein-coding gene families may provide us with a more comprehensive landscape depicting how genes evolve within a well-known genome phylogeny.

A Novel Multi-Ensemble Method for Identifying Essential Proteins

Essential proteins possess critical functions for cell survival. Identifying essential proteins improves our understanding of how a cell works and also plays a vital role in the research fields of disease treatment and drug development. Recently, some machine-learning methods and ensemble learning methods have been proposed to identify essential proteins by introducing effective protein features. However, the ensemble learning method only used to focus on the choice of base classifiers. In this article, we propose a novel ensemble learning framework called multi-ensemble to integrate different base classifiers. The multi-ensemble method adopts the idea of multi-view learning and selects multiple base classifiers and trains those classifiers by continually adding the samples that are predicted correctly by the other base classifiers. We applied multi-ensemble to Yeast data and Escherichia coli data. The results show that our approach achieved better performance than both individual classifiers and the other ensemble learning methods.

Genome-wide Identification, Evolution and Expression Analysis of Basic Helix-loop-helix (bHLH) Gene Family in Barley (Hordeum vulgare L.)

Background

The basic helix-loop-helix (bHLH) transcription factor is one of the most important gene families in plants, playing a key role in diverse metabolic, physiological, and developmental processes. Although it has been well characterized in many plants, the significance of the bHLH family in barley is not well understood at present.

Methods

Through a genome-wide search against the updated barley reference genome, the genomic organization, evolution and expression of the bHLH family in barley were systematically analyzed.

Results

We identified 141 bHLHs in the barley genome (HvbHLHs) and further classified them into 24 subfamilies based on phylogenetic analysis. It was found that HvbHLHs in the same subfamily shared a similar conserved motif composition and exon-intron structures. Chromosome distribution and gene duplication analysis revealed that segmental duplication mainly contributed to the expansion of HvbHLHs and the duplicated genes were subjected to strong purifying selection. Furthermore, expression analysis revealed that HvbHLHs were widely expressed in different tissues and also involved in response to diverse abiotic stresses. The co-expression network was further analyzed to underpin the regulatory function of HvbHLHs. Finally, 25 genes were selected for qRT-PCR validation, the expression profiles of HvbHLHs showed diverse patterns, demonstrating their potential roles in relation to stress tolerance regulation.

Conclusion

This study reported the genome organization, evolutionary characteristics and expression profile of the bHLH family in barley, which not only provide the targets for further functional analysis, but also facilitate better understanding of the regulatory network bHLH genes involved in stress tolerance in barley.

Identification and Regulation of Tomato Serine/Arginine-Rich Proteins Under High Temperatures

Alternative splicing is an important mechanism for the regulation of gene expression in eukaryotes during development, cell differentiation or stress response. Alterations in the splicing profiles of genes under high temperatures that cause heat stress (HS) can impact the maintenance of cellular homeostasis and thermotolerance. Consequently, information on factors involved in HS-sensitive alternative splicing is required to formulate the principles of HS response. Serine/arginine-rich (SR) proteins have a central role in alternative splicing. We aimed for the identification and characterization of SR-coding genes in tomato (Solanum lycopersicum), a plant extensively used in HS studies. We identified 17 canonical SR and two SR-like genes. Several SR-coding genes show differential expression and altered splicing profiles in different organs as well as in response to HS. The transcriptional induction of five SR and one SR-like genes is partially dependent on the master regulator of HS response, HS transcription factor HsfA1a. Cis-elements in the promoters of these SR genes were predicted, which can be putatively recognized by HS-induced transcription factors. Further, transiently expressed SRs show reduced or steady-state protein levels in response to HS. Thus, the levels of SRs under HS are regulated by changes in transcription, alternative splicing and protein stability. We propose that the accumulation or reduction of SRs under HS can impact temperature-sensitive alternative splicing.

Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling

Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted "neutral" (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington's epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.

A large accessory protein interactome is rewired across environments

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

GATA-type transcription factor MrNsdD regulates dimorphic transition, conidiation, virulence and microsclerotium formation in the entomopathogenic fungus Metarhizium rileyi

The GATA-type sexual development transcription factor NsdD has been implicated in virulence, secondary metabolism and asexual development in filamentous fungi. However, little is known about its function in the yeast-to-hypha transition and in microsclerotium formation. In the current study, the orthologous NsdD gene MrNsdD in the entomopathogenic fungus Metarhizium rileyi was characterized. Transcriptional analysis indicated that MrNsdD was involved in yeast-to-hypha transition, conidiation and microsclerotium formation. After targeted deletion of MrNsdD, dimorphic transition, conidiation, fungal virulence and microsclerotium formation were all impaired. Compared with the wild-type strain, the ΔMrNsdD mutants were hypersensitive to thermal stress. Furthermore, transcriptome sequencing analysis revealed that MrNsdD regulated a distinct signalling pathway in M. rileyi during the yeast-to-hypha transition or microsclerotium formation, but exhibited overlapping regulation of genes during the two distinct developmental stages. Taken together, characterization of the MrNsdD targets in this study will aid in the dissection of the molecular mechanisms of dimorphic transition and microsclerotium development.

The differential expression patterns and co-expression networks of paralogs as an indicator of the TNM stages of lung adenocarcinoma and squamous cell carcinoma

Cancers constitute a severe threat to human health. Elucidating the association between the expression patterns of the paralogous genes and transcription factors (TF) and the progression of cancers by comprehensively investigating the expression patterns and co-expression networks will contribute to the in-depth understanding of the pathogenesis of cancers. Here, we identified the paralogous gene pairs and systematically analyzed the expression patterns of these paralogs and the known TFs to elucidate the associations with Tumor, Node, Metastasis (TNM) staging information across ten cancers. We found that the expression of ~60% paralogs was cancer-dependent, and more than 50% of the differentially expressed TFs pairs showed positive expression correlations. The down-regulation patterns of paralogs and TFs were closely associated with the M and N developmental stages of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Our results will help to understand the roles of paralogs and TFs in cancer progression and to screen prognostic biomarkers for early cancer diagnosis.

United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

Identifying essential proteins plays an important role in disease study, drug design, and understanding the minimal requirement for cellular life. Computational methods for essential proteins discovery overcome the disadvantages of biological experimental methods that are often time-consuming, expensive, and inefficient. The topological features of protein-protein interaction (PPI) networks are often used to design computational prediction methods, such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), and Neighborhood Centrality (NC). However, the prediction accuracies of these individual methods still have space to be improved. Studies show that additional information, such as orthologous relations, helps discover essential proteins. Many researchers have proposed different methods by combining multiple information sources to gain improvement of prediction accuracy. In this study, we find that essential proteins appear in triangular structure in PPI network significantly more often than nonessential ones. Based on this phenomenon, we propose a novel pure centrality measure, so-called Neighborhood Closeness Centrality (NCC). Accordingly, we develop a new combination model, Extended Pareto Optimality Consensus model, named EPOC, to fuse NCC and Orthology information and a novel essential proteins identification method, NCCO, is fully proposed. Compared with seven existing classic centrality methods (DC, BC, IC, CC, SC, EC, and NC) and three consensus methods (PeC, ION, and CSC), our results on S.cerevisiae and E.coli datasets show that NCCO has clear advantages. As a consensus method, EPOC also yields better performance than the random walk model.

The interplay between microRNA and alternative splicing of linear and circular RNAs in eleven plant species

MOTIVATION

MicroRNA (miRNA) and alternative splicing (AS)-mediated post-transcriptional regulation has been extensively studied in most eukaryotes. However, the interplay between AS and miRNAs has not been explored in plants. To our knowledge, the overall profile of miRNA target sites in circular RNAs (circRNA) generated by alternative back splicing has never been reported previously. To address the challenge, we identified miRNA target sites located in alternatively spliced regions of the linear and circular splice isoforms using the up-to-date single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) and Illumina sequencing data in eleven plant species.

RESULTS

In total, we identified 399 401 and 114 574 AS events from linear and circular RNAs, respectively. Among them, there were 64 781 and 41 146 miRNA target sites located in linear and circular AS region, respectively. In addition, we found 38 913 circRNAs to be overlapping with 45 648 AS events of its own parent isoforms, suggesting circRNA regulation of AS of linear RNAs by forming R-loop with the genomic locus. Here, we present a comprehensive database of miRNA targets in alternatively spliced linear and circRNAs (ASmiR) and a web server for deposition and identification of miRNA target sites located in the alternatively spliced region of linear and circular RNAs. This database is accompanied by an easy-to-use web query interface for meaningful downstream analysis. Plant research community can submit user-defined datasets to the web service to search AS regions harboring small RNA target sites. In conclusion, this study provides an unprecedented resource to understand regulatory relationships between miRNAs and AS in both gymnosperms and angiosperms.

AVAILABILITY AND IMPLEMENTATION

The readily accessible database and web-based tools are available at http://forestry.fafu.edu.cn/bioinfor/db/ASmiR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences

BACKGROUND

Theoretically, paralogous genes generated through whole genome duplications should share identical expression levels due to their identical sequences and chromatin environments. However, functional divergences and expression differences have arisen due to selective pressures throughout evolution. A comprehensive investigation of the expression patterns of paralogous gene pairs in response to various stresses and a study of correlations between the expression levels and sequence divergences of the paralogs are needed.

RESULTS

In this study, we analyzed the expression patterns of paralogous genes under different types of stress and investigated the correlations between the expression levels and sequence divergences of the paralogs. We analyzed the differential expression patterns of the paralogs under four different types of stress (drought, cold, infection, and herbivory) and classified them into three main types according to their expression patterns. We then further analyzed the differential expression patterns under various degrees of stress and constructed corresponding co-expression networks of differentially expressed paralogs and transcription factors. Finally, we investigated the correlations between the expression levels and sequence divergences of the paralogs and identified positive correlations between expression level and sequence divergence. With regard to sequence divergence, we identified correlations between selective pressures and phylogenetic relationships.

CONCLUSIONS

These results shed light on differential expression patterns of paralogs in response to environmental stresses and are helpful for understanding the relationships between expression levels and sequences divergences.

Cell-based and multi-omics profiling reveals dynamic metabolic repurposing of mitochondria to drive developmental progression of Trypanosoma brucei

Mitochondrial metabolic remodeling is a hallmark of the Trypanosoma brucei digenetic life cycle because the insect stage utilizes a cost-effective oxidative phosphorylation (OxPhos) to generate ATP, while bloodstream cells switch to aerobic glycolysis. Due to difficulties in acquiring enough parasites from the tsetse fly vector, the dynamics of the parasite's metabolic rewiring in the vector have remained obscure. Here, we took advantage of in vitro-induced differentiation to follow changes at the RNA, protein, and metabolite levels. This multi-omics and cell-based profiling showed an immediate redirection of electron flow from the cytochrome-mediated pathway to an alternative oxidase (AOX), an increase in proline consumption, elevated activity of complex II, and certain tricarboxylic acid (TCA) cycle enzymes, which led to mitochondrial membrane hyperpolarization and increased reactive oxygen species (ROS) levels. Interestingly, these ROS molecules appear to act as signaling molecules driving developmental progression because ectopic expression of catalase, a ROS scavenger, halted the in vitro-induced differentiation. Our results provide insights into the mechanisms of the parasite's mitochondrial rewiring and reinforce the emerging concept that mitochondria act as signaling organelles through release of ROS to drive cellular differentiation.

OpenProt: a more comprehensive guide to explore eukaryotic coding potential and proteomes

Advances in proteomics and sequencing have highlighted many non-annotated open reading frames (ORFs) in eukaryotic genomes. Genome annotations, cornerstones of today's research, mostly rely on protein prior knowledge and on ab initio prediction algorithms. Such algorithms notably enforce an arbitrary criterion of one coding sequence (CDS) per transcript, leading to a substantial underestimation of the coding potential of eukaryotes. Here, we present OpenProt, the first database fully endorsing a polycistronic model of eukaryotic genomes to date. OpenProt contains all possible ORFs longer than 30 codons across 10 species, and cumulates supporting evidence such as protein conservation, translation and expression. OpenProt annotates all known proteins (RefProts), novel predicted isoforms (Isoforms) and novel predicted proteins from alternative ORFs (AltProts). It incorporates cutting-edge algorithms to evaluate protein orthology and re-interrogate publicly available ribosome profiling and mass spectrometry datasets, supporting the annotation of thousands of predicted ORFs. The constantly growing database currently cumulates evidence from 87 ribosome profiling and 114 mass spectrometry studies from several species, tissues and cell lines. All data is freely available and downloadable from a web platform (www.openprot.org) supporting a genome browser and advanced queries for each species. Thus, OpenProt enables a more comprehensive landscape of eukaryotic genomes’ coding potential.