Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Combined pangenomics and transcriptomics reveals core and redundant virulence processes in a rapidly evolving fungal plant pathogen

BACKGROUND

Studying genomic variation in rapidly evolving pathogens potentially enables identification of genes supporting their "core biology", being present, functional and expressed by all strains or "flexible biology", varying between strains. Genes supporting flexible biology may be considered to be "accessory", whilst the "core" gene set is likely to be important for common features of a pathogen species biology, including virulence on all host genotypes. The wheat-pathogenic fungus Zymoseptoria tritici represents one of the most rapidly evolving threats to global food security and was the focus of this study.

RESULTS

We constructed a pangenome of 18 European field isolates, with 12 also subjected to RNAseq transcription profiling during infection. Combining this data, we predicted a "core" gene set comprising 9807 sequences which were (1) present in all isolates, (2) lacking inactivating polymorphisms and (3) expressed by all isolates. A large accessory genome, consisting of 45% of the total genes, was also defined. We classified genetic and genomic polymorphism at both chromosomal and individual gene scales. Proteins required for essential functions including virulence had lower-than average sequence variability amongst core genes. Both core and accessory genomes encoded many small, secreted candidate effector proteins that likely interact with plant immunity. Viral vector-mediated transient in planta overexpression of 88 candidates failed to identify any which induced leaf necrosis characteristic of disease. However, functional complementation of a non-pathogenic deletion mutant lacking five core genes demonstrated that full virulence was restored by re-introduction of the single gene exhibiting least sequence polymorphism and highest expression.

CONCLUSIONS

These data support the combined use of pangenomics and transcriptomics for defining genes which represent core, and potentially exploitable, weaknesses in rapidly evolving pathogens.

Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Biological network topology features predict gene dependencies in cancer cell-lines

Motivation

Protein-protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those proteins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using 12 topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest classifiers (DependANT) to predict novel gene dependencies.

Results

We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent.

Availability and implementation

Software available at https://bitbucket.org/bioinformatics_lab_sussex/dependant2.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Aspergillus fumigatus versus Genus Aspergillus: Conservation, Adaptive Evolution and Specific Virulence Genes

Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-β pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information

Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are required to design a score function which is based on prior knowledge, yet is usually not sufficient to capture the complexity of biological information. In machine learning-based methods, some selected biological features cannot represent the complete properties of biological information as they lack a computational framework to automatically select features. To tackle these problems, we propose a deep learning framework to automatically learn biological features without prior knowledge. We use node2vec technique to automatically learn a richer representation of protein-protein interaction (PPI) network topologies than a score function. Bidirectional long short term memory cells are applied to capture non-local relationships in gene expression data. For subcellular localization information, we exploit a high dimensional indicator vector to characterize their feature. To evaluate the performance of our method, we tested it on PPI network of S. cerevisiae. Our experimental results demonstrate that the performance of our method is better than traditional centrality methods and is superior to existing machine learning-based methods. To explore which of the three types of biological information is the most vital element, we conduct an ablation study by removing each component in turn. Our results show that the PPI network embedding contributes most to the improvement. In addition, gene expression profiles and subcellular localization information are also helpful to improve the performance in identification of essential proteins.

DeeplyEssential: a deep neural network for predicting essential genes in microbes

BACKGROUND

Essential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies.

RESULTS

We propose a deep neural network for predicting essential genes in microbes. Our architecture called DEEPLYESSENTIAL makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DEEPLYESSENTIAL outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes.

CONCLUSION

Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.

Predicting essential genes of 41 prokaryotes by a semi-supervised method

Essential genes are vitally important to the survival and reproduction of organisms. Many machine learning methods have been widely employed to predict essential genes and have obtained satisfactory results. However, most of these methods are supervised methods and may not obtain the desired result when the labeled data are insufficient. In this paper, we proposed a learning with local and global consistency (LGC) method-based classifier, which was employed to predict the essential genes of 41 prokaryotes. LGC is a graph-based semi-supervised learning method that can construct a prediction model using finite label and constraint information. The performance of the proposed classifier was evaluated by employing intra-organism prediction and leave-one-species-out validation. The average AUC value of 41 organisms in intra-organisms prediction was 0.723 when the labeled sample ratio was 0.5. The results of this study indicate that the proposed method can achieve acceptable prediction performance with limited labeled data. Additionally, the results demonstrate that this method has good universality.

The Essential Thioredoxin Reductase of the Human Pathogenic Mold Aspergillus fumigatus Is a Promising Antifungal Target

The identification of cellular targets for antifungal compounds is a cornerstone for the development of novel antimycotics, for which a significant need exists due to increasing numbers of susceptible patients, emerging pathogens, and evolving resistance. For the human pathogenic mold Aspergillus fumigatus, the causative agent of the opportunistic disease aspergillosis, only a limited number of established targets and corresponding drugs are available. Among several targets that were postulated from a variety of experimental approaches, the conserved thioredoxin reductase (TrxR) activity encoded by the trxR gene was assessed in this study. Its essentiality could be confirmed following a conditional TetOFF promoter replacement strategy. Relevance of the trxR gene product for oxidative stress resistance was revealed and, most importantly, its requirement for full virulence of A. fumigatus in two different models of infection resembling invasive aspergillosis. Our findings complement the idea of targeting the reductase component of the fungal thioredoxin system for antifungal therapy.

DeepEP: a deep learning framework for identifying essential proteins

Background

Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics.

Results

We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins.

Conclusion

We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

Construction of Refined Protein Interaction Network for Predicting Essential Proteins

Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN. In this paper, we propose a new method for constructing refined PIN by using gene expression profiles and subcellular location information. The basic idea behind refining the PIN is that two proteins should have higher possibility to physically interact with each other if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is denoted by S-PIN while the final PIN refined by our method is denoted by TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to be used in the identification of essential proteins, 10 network-based essential protein discovery methods (DC, EC, SC, BC, CC, IC, LAC, NC, BN, and DMNC) are applied on it to identify essential proteins. A comparison of TS-PIN and two other networks: S-PIN and NF-APIN (a noise-filtered active PIN constructed by using gene expression data and S-PIN) is implemented on the prediction of essential proteins by using these ten network-based methods. The comparison results show that all of the 10 network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BACKGROUND

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

RESULTS

We have designed an integrative computational pipeline term as "PanRV" that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

CONCLUSION

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

A Survey on Computational Methods for Essential Proteins and Genes Prediction

Background:

Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research.

Objective:

The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Identification of Essential Proteins Based on Improved HITS Algorithm

Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein⁻protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques.

Identifying mouse developmental essential genes using machine learning

The genes that are required for organismal survival are annotated as ‘essential genes’. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies on mouse development, we developed a supervised machine learning classifier based on phenotype data from mouse knockout experiments. We used this classifier to predict the essentiality of mouse genes lacking experimental data. Validation of our predictions against a blind test set of recent mouse knockout experimental data indicated a high level of accuracy (>80%). We also validated our predictions for other mouse mutagenesis methodologies, demonstrating that the predictions are accurate for lethal phenotypes isolated in random chemical mutagenesis screens and embryonic stem cell screens. The biological functions that are enriched in essential and non-essential genes have been identified, showing that essential genes tend to encode intracellular proteins that interact with nucleic acids. The genome distribution of predicted essential and non-essential genes was analysed, demonstrating that the density of essential genes varies throughout the genome. A comparison with human essential and non-essential genes was performed, revealing conservation between human and mouse gene essentiality status. Our genome-wide predictions of mouse essential genes will be of value for the planning of mouse knockout experiments and phenotyping assays, for understanding the functional processes required during mouse development, and for the prioritisation of disease candidate genes identified in human genome and exome sequence datasets.

Summary: Here, we used computer-based machine learning methodology to predict which genes in the mouse genome are essential for development, and present a database of mouse essential and non-essential genes.

Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment

Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.

Antifungal drugs: New insights in research & development

The need for better antifungal therapy is commonly accepted in view of the high mortality rates associated with systemic infections, the low number of available antifungal classes, their associated toxicity and the increasing number of infections caused by strains with natural or acquired resistance. The urgency to expand the range of therapeutic options for the treatment of fungal infections has led researchers in recent decades to seek alternative antifungal targets when compared to the conventional ones currently used. Although new potential targets are reported, translating the discoveries from bench to bedside is a long process and most of these drugs fail to reach the patients. In this review, we discuss the development of antifungal drugs focusing on the approach of drug repurposing and the search for novel drugs for classical targets, the most recently described gene targets for drug development, the possibilities of immunotherapy using antibodies, cytokines, therapeutic vaccines and antimicrobial peptides.

A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria

Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd.edu.in/raghava/vactarbac/).

Feature Selection via Swarm Intelligence for Determining Protein Essentiality

Protein essentiality is fundamental to comprehend the function and evolution of genes. The prediction of protein essentiality is pivotal in identifying disease genes and potential drug targets. Since the experimental methods need many investments in time and funds, it is of great value to predict protein essentiality with high accuracy using computational methods. In this study, we present a novel feature selection named Elite Search mechanism-based Flower Pollination Algorithm (ESFPA) to determine protein essentiality. Unlike other protein essentiality prediction methods, ESFPA uses an improved swarm intelligence⁻based algorithm for feature selection and selects optimal features for protein essentiality prediction. The first step is to collect numerous features with the highly predictive characteristics of essentiality. The second step is to develop a feature selection strategy based on a swarm intelligence algorithm to obtain the optimal feature subset. Furthermore, an elite search mechanism is adopted to further improve the quality of feature subset. Subsequently a hybrid classifier is applied to evaluate the essentiality for each protein. Finally, the experimental results show that our method is competitive to some well-known feature selection methods. The proposed method aims to provide a new perspective for protein essentiality determination.

Vitamin Biosynthesis as an Antifungal Target

The large increase in the population of immunosuppressed patients, coupled with the limited efficacy of existing antifungals and rising resistance toward them, have dramatically highlighted the need to develop novel drugs for the treatment of invasive fungal infections. An attractive possibility is the identification of possible drug targets within essential fungal metabolic pathways not shared with humans. Here, we review the vitamin biosynthetic pathways (vitamins A⁻E, K) as candidates for the development of antifungals. We present a set of ranking criteria that identify the vitamin B2 (riboflavin), B5 (pantothenic acid), and B9 (folate) biosynthesis pathways as being particularly rich in new antifungal targets. We propose that recent scientific advances in the fields of drug design and fungal genomics have developed sufficiently to merit a renewed look at these pathways as promising sources for the development of novel classes of antifungals.

Improved flower pollination algorithm for identifying essential proteins

Background

Essential proteins are necessary for the survival and development of cells. The identification of essential proteins can help to understand the minimal requirements for cellular life and it also plays an important role in the disease genes study and drug design. With the development of high-throughput techniques, a large amount of protein-protein interactions data is available to predict essential proteins at the network level. Hitherto, even though a number of essential protein discovery methods have been proposed, the prediction precision still needs to be improved.

Methods

In this paper, we propose a new algorithm, improved Flower Pollination algorithm (FPA) for identifying Essential proteins, named FPE. Different from other existing essential protein discovery methods, we apply FPA which is a new intelligent algorithm imitating pollination behavior of flowering plants in nature to identify essential proteins. Analogous to flower pollination is to find optimal reproduction from the perspective of biological evolution, and the identification of essential proteins is to discover a candidate essential protein set by analyzing the corresponding relationships between FPA algorithm and the prediction of essential proteins, and redefining the positions of flowers and specific pollination process. Moreover, it has been proved that the integration of biological and topological properties can get improved precision for identifying essential proteins. Consequently, we develop a GSC measurement in order to judge the essentiality of proteins, which takes into account not only the Gene expression data, Subcellular localization and protein Complexes information, but also the network topology.

Results

The experimental results show that FPE performs better than the state-of-the-art methods (DC, SC, IC, EC, LAC, NC, PeC, WDC, UDoNC and SON) in terms of the prediction precision, precision-recall curve and jackknife curve for identifying essential proteins and also has high stability.

Conclusions

We confirm that FPE can be used to effectively identify essential proteins by the use of nature-inspired algorithm FPA and the combination of network topology with gene expression data, subcellular localization and protein complexes information. The experimental results have shown the superiority of FPE for the prediction of essential proteins.

Riboflavin and pantothenic acid biosynthesis are crucial for iron homeostasis and virulence in the pathogenic mold Aspergillus fumigatus

BACKGROUND

Aspergillus fumigatus is the most prevalent airborne fungal pathogen, causing invasive fungal infections mainly in immunosuppressed individuals. Death rates from invasive aspergillosis remain high because of limited treatment options and increasing antifungal resistance. The aim of this study was to identify key fungal-specific genes participating in vitamin B biosynthesis in A. fumigatus. Because these genes are absent in humans they can serve as possible novel targets for antifungal drug development.

METHODS

By sequence homology we identified, deleted and analysed four key A. fumigatus genes (riboB, panA, pyroA, thiB) involved respectively in the biosynthesis of riboflavin (vitamin B2), pantothenic acid (vitamin B5), pyridoxine (vitamin B6) and thiamine (vitamin B1).

RESULTS

Deletion of riboB, panA, pyroA or thiB resulted in respective vitamin auxotrophy. Lack of riboflavin and pantothenic acid biosynthesis perturbed many cellular processes including iron homeostasis. Virulence in murine pulmonary and systemic models of infection was severely attenuated following deletion of riboB and panA, strongly reduced after pyroA deletion and weakly attenuated after thiB deletion.

CONCLUSIONS

This study reveals the biosynthetic pathways of the vitamins riboflavin and pantothenic acid as attractive targets for novel antifungal therapy. Moreover, the virulence studies with auxotrophic mutants serve to identify the availability of nutrients to pathogens in host niches.

ABBREVIATIONS

BPS: bathophenanthrolinedisulfonate; BSA: bovine serum albumin; CFU: colony forming unit; -Fe: iron starvation; +Fe: iron sufficiency; hFe: high iron; NRPSs: nonribosomal peptide synthetases; PKSs: polyketide synthaseses; wt: wild type.

Sequence-based information-theoretic features for gene essentiality prediction

Background

Identification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.

Results

We developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).

Conclusions

The proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1884-5) contains supplementary material, which is available to authorized users.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

Mutant characterization and in vivo conditional repression identify aromatic amino acid biosynthesis to be essential for Aspergillus fumigatus virulence

Pathogenicity of the saprobe Aspergillus fumigatus strictly depends on nutrient acquisition during infection, as fungal growth determines colonisation and invasion of a susceptible host. Primary metabolism has to be considered as a valid target for antimycotic therapy, based on the fact that several fungal anabolic pathways are not conserved in higher eukaryotes. To test whether fungal proliferation during invasive aspergillosis relies on endogenous biosynthesis of aromatic amino acids, defined auxotrophic mutants of A. fumigatus were generated and assessed for their infectious capacities in neutropenic mice and found to be strongly attenuated in virulence. Moreover, essentiality of the complete biosynthetic pathway could be demonstrated, corroborated by conditional gene expression in infected animals and inhibitor studies. This brief report not only validates the aromatic amino acid biosynthesis pathway of A. fumigatus to be a promising antifungal target but furthermore demonstrates feasibility of conditional gene expression in a murine infection model of aspergillosis.