DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

A therapeutic epitopes-based vaccine engineering against Salmonella enterica XDR strains for typhoid fever: a Pan-vaccinomics approach

A prevalent food-borne pathogen, Salmonella enterica serotypes Typhi, is responsible for gastrointestinal and systemic infections globally. Salmonella vaccines are the most effective, however, producing a broad-spectrum vaccine remains challenging due to Salmonella's many serotypes. Efforts are urgently required to develop a novel vaccine candidate that can tackle all S. Typhi strains because of their high resistance to multiple kinds of antibiotics (particularly the XDR H58 strain). In this work, we used a computational pangenome-based vaccine design technique on all available (n = 119) S. Typhi reference genomes and identified one TonB-dependent siderophore receptor (WP_001034967.1) as highly conserved and prospective vaccine candidates from the predicted core genome (n = 3,351). The applied pan-proteomics and Immunoinformatic approaches help in the identification of four epitopes that may trigger adequate host body immune responses. Furthermore, the proposed vaccine ensemble demonstrates a stable binding conformation with the examined immunological receptor (HLAs and TRL2/4) and has large interaction energy determined via molecular docking and molecular dynamics simulation techniques. Eventually, an expression vector for the Escherichia. coli K12 strain was constructed from the vaccine sequence. Additional analysis revealed that the vaccine may help to elicit strong immune responses for typhoid infections, however, experimental analysis is required to verify the vaccine's effectiveness based on these results. Moreover, the applied computer-assisted vaccine design may considerably decrease vaccine development costs and speed up the process. The study's findings are intriguing, but they must be evaluated in the experimental labs to confirm the developed vaccine's biological efficiency against XDR S. Typhi.Communicated by Ramaswamy H. Sarma.

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.

New antibacterial candidates against Acinetobacter baumannii discovered by in silico-driven chemogenomics repurposing

Acinetobacter baumannii is a worldwide Gram-negative bacterium with a high resistance rate, responsible for a broad spectrum of hospital-acquired infections. A computational chemogenomics framework was applied to investigate the repurposing of approved drugs to target A. baumannii. This comprehensive approach involved compiling and preparing proteomic data, identifying homologous proteins in drug-target databases, evaluating the evolutionary conservation of targets, and conducting molecular docking studies and in vitro assays. Seven drugs were selected for experimental assays. Among them, tavaborole exhibited the most promising antimicrobial activity with a minimum inhibitory concentration (MIC) value of 2 μg/ml, potent activity against several clinically relevant strains, and robust efficacy against biofilms from multidrug-resistant strains at a concentration of 16 μg/ml. Molecular docking studies elucidated the binding modes of tavaborole in the editing and active domains of leucyl-tRNA synthetase, providing insights into its structural basis for antimicrobial activity. Tavaborole shows promise as an antimicrobial agent for combating A. baumannii infections and warrants further investigation in preclinical studies.

Unveiling a Comprehensive Multi-epitope Subunit Vaccine Strategy Against Salmonella subsp. enterica: Bridging Core, Subtractive Proteomics, and Immunoinformatics

Salmonella subsp. enterica (SE) presents a significant global health challenge in both developed and developing countries. Current SE vaccines have limitations, targeting specific strains and demonstrating moderate efficacy in adults, while also being unsuitable for young children and often unaffordable in regions with lower income levels where the disease is prevalent. To address these challenges, this study employed a computational approach integrating core proteomics, subtractive proteomics, and immunoinformatics to develop a universal SE vaccine and identify potential drug targets. Analysis of the core proteome of 185 SE strains revealed 1964 conserved proteins. Subtractive proteomics identified 9 proteins as potential vaccine candidates and 41 as novel drug targets. Using reverse vaccinology-based immunoinformatics, four multi-epitope-based subunit vaccine constructs (MESVCs) were designed, aiming to stimulate cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte responses. These constructs underwent comprehensive evaluations for antigenicity, immunogenicity, toxicity, hydropathicity, and physicochemical properties. Predictive modeling, refinement, and validation were conducted to determine the secondary and tertiary structures of the SE-MESVCs, followed by docking studies with MHC-I, MHC-II, and TLR4 receptors. Molecular docking assessments showed favorable binding with all three receptors, with SE-MESVC-4 exhibiting the most promising binding energy. Molecular dynamics simulations confirmed the binding affinity and stability of SE-MESVC-4 with the TLR4/MD2 complex. Additionally, codon optimization and in silico cloning verified the efficient translation and successful expression of SE-MESVC-4 in Escherichia coli (E. coli) str. K12. Subsequent in silico immune simulation evaluated the efficacy of SE-MESVC-4 in triggering an effective immune response. These results suggest that SE-MESVC-4 may induce both humoral and cellular immune responses, making it a potential candidate for an effective SE vaccine. However, further experimental investigations are necessary to validate the immunogenicity and efficacy of SE-MESVC-4, bringing us closer to effectively combating SE infections.

Drug to genome to drug: a computational large-scale chemogenomics screening for novel drug candidates against sporotrichosis

Sporotrichosis is recognized as the predominant subcutaneous mycosis in South America, attributed to pathogenic species within the Sporothrix genus. Notably, in Brazil, Sporothrix brasiliensis emerges as the principal species, exhibiting significant sapronotic, zoonotic and enzootic epidemic potential. Consequently, the discovery of novel therapeutic agents for the treatment of sporotrichosis is imperative. The present study is dedicated to the repositioning of pharmaceuticals for sporotrichosis therapy. To achieve this goal, we designed a pipeline with the following steps: (a) compilation and preparation of Sporothrix genome data; (b) identification of orthologous proteins among the species; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Sporothrix essential targets using validated genes from Saccharomyces cerevisiae; (e) molecular modeling studies; and (f) experimental validation of selected candidates. Based on this approach, we were able to prioritize eight drugs for in vitro experimental validation. Among the evaluated compounds, everolimus and bifonazole demonstrated minimum inhibitory concentration (MIC) values of 0.5 µg/mL and 4.0 µg/mL, respectively. Subsequently, molecular docking studies suggest that bifonazole and everolimus may target specific proteins within S. brasiliensis- namely, sterol 14-α-demethylase and serine/threonine-protein kinase TOR, respectively. These findings shed light on the potential binding affinities and binding modes of bifonazole and everolimus with their probable targets, providing a preliminary understanding of the antifungal mechanism of action of these compounds. In conclusion, our research advances the understanding of the therapeutic potential of bifonazole and everolimus, supporting their further investigation as antifungal agents for sporotrichosis in prospective hit-to-lead and preclinical investigations.

Comprehensive analysis of GINS subunit expression, prognostic value, and immune infiltration in clear cell renal cell carcinoma

Background

In recent decades, there has been increasing evidence that Go-Ichi-Nii-San (GINS) subunits play an important role in the development and progression of various tumors. However, little research has been conducted on the role of GINS subunits in clear cell renal cell carcinoma (ccRCC). This study sought to explore the differential expression, prognosis, and immunological significance of GINS subunits in ccRCC.

Methods

We used various analysis packages of R (version 3.6.3), the University of ALabama at Birmingham CANcer (UALCAN) data analysis portal, the Cancer Cell Line Encyclopedia (CCLE), the cBio Cancer Genomics Portal (cBioPortal), and the Tumor Immune Estimation Resource (TIMER) to study the gene expression, promoter methylation level, gene mutations, prognostic and diagnostic value, immune infiltration, pathway enrichment, and other aspects of the GINS subunits. Next, the genes related to the GINS subunits were analyzed using the STRING and GeneMANIA platforms, and the correlation between GINS subunits and the functions involved were investigated.

Results

The expression level of GINS1/2/3/4 was significantly higher in ccRCC tumor tissues than normal tissues, and was significantly related to tumor grade and stage. The expression of GINS1/2/4 may be related to the methylation degree of the promoter region. The prognostic and diagnostic analyses showed that the increased expression of GINS1 was associated with various poor prognoses and had diagnostic value. The GINS subunit mutation also significantly affected the clinical prognosis of ccRCC patients. Finally, the correlation analysis of the immune infiltration level, co-expression, and enrichment of related genes indicated that GINS subunit expression was associated with different levels of ccRCC immune infiltration.

Conclusions

The analysis results showed that the differential expression of GINS subunits in ccRCC, which had prognostic and diagnostic value, was correlated with clinicopathological stage, immune infiltration, and other related aspects. GINS1 may serve as a new potential prognostic biomarker for ccRCC patients and be used to guide treatment.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models

Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat to public health. The protein function of this bacterium is not well known, thus a systematic investigation of K. pneumoniae proteome is in urgent need. In this study, the protein functions of this bacteria were re-annotated, and their function groups were analyzed. Moreover, three machine learning models were built to identify novel virulence factors. Results showed that the functions of 16 uncharacterized proteins were first annotated by sequence alignment. In addition, K. pneumoniae proteins share a high proportion of homology with Haemophilus influenzae and a low homology proportion with Chlamydia pneumoniae. By sequence analysis, 10 proteins were identified as potential drug targets for this bacterium. Our model achieved a high accuracy of 0.901 in the benchmark dataset. By applying our models to K. pneumoniae, we identified 39 virulence factors in this pathogen. Our findings could provide novel clues for the treatment of K. pneumoniae infection.

Computational prediction of crucial genes involved in gonorrhea infection and neoplastic cell transformation: A multiomics approach

Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, protein‒protein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.

A deep learning method to predict bacterial ADP-ribosyltransferase toxins

MOTIVATION

ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.

RESULTS

We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew's correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes.

AVAILABILITY AND IMPLEMENTATION

ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.

An insight into MDR Acinetobacter baumannii infection and its pathogenesis: Potential therapeutic targets and challenges

Acinetobacter baumannii is observed as a common species of Gram-negative bacteria that exist in soil and water. Despite being accepted as a typical component of human skin flora, it has become an important opportunistic pathogen, especially in healthcare settings. The pathogenicity of A. baumannii is attributed to its virulence factors, which include adhesins, pili, lipopolysaccharides, outer membrane proteins, iron uptake systems, autotransporter, secretion systems, phospholipases etc. These elements provide the bacterium the ability to cling to and penetrate host cells, get past the host immune system, and destroy tissue. Its infection is a major contributor to human pathophysiological conditions including pneumonia, bloodstream infections, urinary tract infections, and surgical site infections. It is challenging to treat infections brought on by this pathogen since this bacterium has evolved to withstand numerous drugs and further emergence of drug-resistant A. baumannii results in higher rates of morbidity and mortality. The long-term survival of this bacterium on surfaces of medical supplies and hospital furniture facilitates its frequent spread in humans from one habitat to another. There is a need for urgent investigations to find effective drug targets for A. baumannii as well as designing novel drugs to reduce the survival and spread of infection. In the current review, we represent the specific features, pathogenesis, and molecular intricacies of crucial drug targets of A. baumannii. This would also assist in proposing strategies and alternative therapies for the prevention and treatment of A. baumannii infections and their spread.

Unveiling the mysteries: Functional insights into hypothetical proteins from Bacteroides fragilis 638R

Humans benefit from a vast community of microorganisms in their gastrointestinal tract, known as the gut microbiota, numbering in the tens of trillions. An imbalance in the gut microbiota known as dysbiosis, can lead to changes in the metabolite profile, elevating the levels of toxins like Bacteroides fragilis toxin (BFT), colibactin, and cytolethal distending toxin. These toxins are implicated in the process of oncogenesis. However, a significant portion of the Bacteroides fragilis genome consists of functionally uncharacterized and hypothetical proteins. This study delves into the functional characterization of hypothetical proteins (HPs) encoded by the Bacteroides fragilis genome, employing a systematic in silico approach. A total of 379 HPs were subjected to a BlastP homology search against the NCBI non-redundant protein sequence database, resulting in 162 HPs devoid of identity to known proteins. CDD-Blast identified 106 HPs with functional domains, which were then annotated using Pfam, InterPro, SUPERFAMILY, SCANPROSITE, SMART, and CATH. Physicochemical properties, such as molecular weight, isoelectric point, and stability indices, were assessed for 60 HPs whose functional domains were identified by at least three of the aforementioned bioinformatic tools. Subsequently, subcellular localization analysis was examined and the gene ontology analysis revealed diverse biological processes, cellular components, and molecular functions. Remarkably, E1WPR3 was identified as a virulent and essential gene among the HPs. This study presents a comprehensive exploration of B. fragilis HPs, shedding light on their potential roles and contributing to a deeper understanding of this organism's functional landscape.

Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function, while accessory genes are dispensable, particularly in nutrient-rich environments. However, this assumption is seldom tested genetically within the pangenome context. In this study, we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome. To evaluate the robustness of fitness category assignment, Tn-seq data for three independent mutant libraries per strain were analyzed by three methods, which indicates that the Hidden Markov Model (HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size, outperforming the Bayesian and Monte Carlo simulation-based methods. Consequently, the HMM method was used to classify the fitness category. Fitness genes, categorized as essential (ES), advantage (GA), and disadvantage (GD) genes for growth, are enriched in core genes, while nonessential genes (NE) are over-represented in accessory genes. Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes. Connectivity degrees in the cofitness network decrease in the order of ES, GD, and GA/NE. In addition to accessory genes, 1599 out of 3284 core genes display differential essentiality across test strains. Within the pangenome core, both shared quasi-essential (ES and GA) and strain-dependent fitness genes are enriched in similar functional categories. Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

Bacterial genome reduction for optimal chassis of synthetic biology: a review

Bacteria with streamlined genomes, that harbor full functional genes for essential metabolic networks, are able to synthesize the desired products more effectively and thus have advantages as production platforms in industrial applications. To obtain streamlined chassis genomes, a large amount of effort has been made to reduce existing bacterial genomes. This work falls into two categories: rational and random reduction. The identification of essential gene sets and the emergence of various genome-deletion techniques have greatly promoted genome reduction in many bacteria over the past few decades. Some of the constructed genomes possessed desirable properties for industrial applications, such as: increased genome stability, transformation capacity, cell growth, and biomaterial productivity. The decreased growth and perturbations in physiological phenotype of some genome-reduced strains may limit their applications as optimized cell factories. This review presents an assessment of the advancements made to date in bacterial genome reduction to construct optimal chassis for synthetic biology, including: the identification of essential gene sets, the genome-deletion techniques, the properties and industrial applications of artificially streamlined genomes, the obstacles encountered in constructing reduced genomes, and the future perspectives.

Design of a cryptococcus neoformans vaccine by subtractive proteomics combined with immunoinformatics

The emergence of Cryptococcus neoformans has posed an undeniable burden to many regions worldwide, with its strains mainly entering the lungs through the respiratory tract and spreading throughout the body. Limitations of drug regimens, such as high costs and limited options, have directed our attention toward the promising field of vaccine development. In this study, the subtractive proteomics approach was employed to select target proteins from databases that can accurately cover serotypes A and D of the Cryptococcus neoformans. Further, two multi-epitope vaccines consisting of T and B cell epitopes were demonstrated that they have good structural stability and could bind with immune receptor to induce desired immune responses in silico. After further evaluation, these vaccines show the potential for large-scale production and applicability to the majority of the population of the world. In summary, these two vaccines have been theoretically proven to combat Cryptococcus neoformans infections, awaiting further experimental validation of their actual protective effects.

Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

Exploring optimal drug targets through subtractive proteomics analysis and pangenomic insights for tailored drug design in tuberculosis

Tuberculosis (TB), caused by Mycobacterium tuberculosis, ranks among the top causes of global human mortality, as reported by the World Health Organization's 2022 TB report. The prevalence of M. tuberculosis strains that are multiple and extensive-drug resistant represents a significant barrier to TB eradication. Fortunately, having many completely sequenced M. tuberculosis genomes available has made it possible to investigate the species pangenome, conduct a pan-phylogenetic investigation, and find potential new drug targets. The 442 complete genome dataset was used to estimate the pangenome of M. tuberculosis. This study involved phylogenomic classification and in-depth analyses. Sequential filters were applied to the conserved core genome containing 2754 proteins. These filters assessed non-human homology, virulence, essentiality, physiochemical properties, and pathway analysis. Through these intensive filtering approaches, promising broad-spectrum therapeutic targets were identified. These targets were docked with FDA-approved compounds readily available on the ZINC database. Selected highly ranked ligands with inhibitory potential include dihydroergotamine and abiraterone acetate. The effectiveness of the ligands has been supported by molecular dynamics simulation of the ligand-protein complexes, instilling optimism that the identified lead compounds may serve as a robust basis for the development of safe and efficient drugs for TB treatment, subject to further lead optimization and subsequent experimental validation.

Frequent nonhomologous replacement of replicative helicase loaders by viruses in Vibrionaceae

Several microbial genomes lack textbook-defined essential genes. If an essential gene is absent from a genome, then an evolutionarily independent gene of unknown function complements its function. Here, we identified frequent nonhomologous replacement of an essential component of DNA replication initiation, a replicative helicase loader gene, in Vibrionaceae. Our analysis of Vibrionaceae genomes revealed two genes with unknown function, named vdhL1 and vdhL2, that were substantially enriched in genomes without the known helicase-loader genes. These genes showed no sequence similarities to genes with known function but encoded proteins structurally similar with a viral helicase loader. Analyses of genomic syntenies and coevolution with helicase genes suggested that vdhL1/2 encodes a helicase loader. The in vitro assay showed that Vibrio harveyi VdhL1 and Vibrio ezurae VdhL2 promote the helicase activity of DnaB. Furthermore, molecular phylogenetics suggested that vdhL1/2 were derived from phages and replaced an intrinsic helicase loader gene of Vibrionaceae over 20 times. This high replacement frequency implies the host's advantage in acquiring a viral helicase loader gene.

HOPS-Dependent Endosomal Escape Demands Protein Unfolding

The inefficient translocation of proteins across biological membranes limits their application as potential therapeutics and research tools. In many cases, the translocation of a protein involves two discrete steps: uptake into the endocytic pathway and endosomal escape. Certain charged or amphiphilic molecules can achieve high protein uptake, but few are capable of efficient endosomal escape. One exception to this rule is ZF5.3, a mini-protein that exploits elements of the natural endosomal maturation machinery to translocate across endosomal membranes. Although some ZF5.3-protein conjugates are delivered efficiently to the cytosol or nucleus, overall delivery efficiency varies widely for different cargoes with no obvious design rules. Here we show that delivery efficiency depends on the ability of the cargo to unfold. Using fluorescence correlation spectroscopy, a single-molecule technique that precisely measures intracytosolic protein concentration, we show that regardless of size and pI, low-Tm cargoes of ZF5.3 (including intrinsically disordered domains) bias endosomal escape toward a high-efficiency pathway that requires the homotypic fusion and protein sorting (HOPS) complex. Small protein domains are delivered with moderate efficiency through the same HOPS portal, even if the Tm is high. These findings imply a novel pathway out of endosomes that is exploited by ZF5.3 and provide clear guidance for the selection or design of optimally deliverable therapeutic cargo.

Genome mining of Mycobacterium tuberculosis: targeting SufD as a novel drug candidate through in silico characterization and inhibitor screening

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe-S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron-sulfur (Fe-S) cluster biogenesis pathway for the repair of damaged Fe-S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe-S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein's structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

L-Thyroxine and L-thyroxine-based antimicrobials against Streptococcus pneumoniae and other Gram-positive bacteria

Objectives

The rise of antibiotic-resistant Streptococcus pneumoniae (Sp) poses a significant global health threat, urging the quest for novel antimicrobial solutions. We have discovered that the human hormone l-thyroxine has antibacterial properties. In order to explore its drugability we perform here the characterization of a series of l-thyroxine analogues and describe the structural determinants influencing their antibacterial efficacy.

Method

We performed a high-throughput screening of a library of compounds approved for use in humans, complemented with ITC assays on purified Sp-flavodoxin, to pinpoint molecules binding to this protein. Antimicrobial in vitro susceptibility assays of the hit compound (l-thyroxine) as well as of 13 l-thyroxine analogues were done against a panel of Gram-positive and Gram-negative bacteria. Toxicity of compounds on HepG2 cells was also assessed. A combined structure-activity and computational docking analysis was carried out to uncover functional groups crucial for the antimicrobial potency of these compounds.

Results

Human l-thyroxine binds to Sp-flavodoxin, forming a 1:1 complex of low micromolar Kd. While l-thyroxine specifically inhibited Sp growth, some derivatives displayed activity against other Gram-positive bacteria like Staphylococcus aureus and Enterococcus faecalis, while remaining inactive against Gram-negative pathogens. Neither l-thyroxine nor some selected derivatives exhibited toxicity to HepG2 cells.

Conclusions

l-thyroxine derivatives targeting bacterial flavodoxins represent a new and promising class of antimicrobials.

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration

The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Pan-genome mediated therapeutic target mining in Kingella kingae and inhibition assessment using traditional Chinese medicinal compounds: an informatics approach

Kingella kingae causes bacteremia, endocarditis, osteomyelitis, septic arthritis, meningitis, spondylodiscitis, and lower respiratory tract infections in pediatric patients. Usually it demonstrates disease after inflammation of mouth, lips or infections of the upper respiratory tract. To date, therapeutic targets in this bacterium remain unexplored. We have utilized a battery of bioinformatics tools to mine these targets in this study. Core genes were initially inferred from 55 genomes of K. kingae and 39 therapeutic targets were mined using an in-house pipeline. We selected aroG product (KDPG aldolase) involved in chorismate pathway, for inhibition analysis of this bacterium using lead-like metabolites from traditional Chinese medicinal plants. Pharmacophore generation was done using control ZINC36444158 (1,16-bis[(dihydroxyphosphinyl)oxy]hexadecane), followed by molecular docking of top hits from a library of 36,000 compounds. Top prioritized compounds were ZINC95914016, ZINC33833283 and ZINC95914219. ADME profiling and simulation of compound dosing (100 mg tablet) was done to infer compartmental pharmacokinetics in a population of 300 individuals in fasting state. PkCSM based toxicity analysis revealed the compounds ZINC95914016 and ZINC95914219 as safe and with almost similar bioavailability. However, ZINC95914016 takes less time to reach maximum concentration in the plasma and shows several optimal parameters compared to other leads. In light of obtained data, we recommend this compound for further testing and induction in experimental drug design pipeline.Communicated by Ramaswamy H. Sarma.

Reconstruction of the genome-scale metabolic network model of Sinorhizobium fredii CCBAU45436 for free-living and symbiotic states

Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.

Essential gene knockdowns reveal genetic vulnerabilities and antibiotic sensitivities in Acinetobacter baumannii

The emergence of multidrug-resistant Gram-negative bacteria underscores the need to define genetic vulnerabilities that can be therapeutically exploited. The Gram-negative pathogen, Acinetobacter baumannii, is considered an urgent threat due to its propensity to evade antibiotic treatments. Essential cellular processes are the target of existing antibiotics and a likely source of new vulnerabilities. Although A. baumannii essential genes have been identified by transposon sequencing, they have not been prioritized by sensitivity to knockdown or antibiotics. Here, we take a systems biology approach to comprehensively characterize A. baumannii essential genes using CRISPR interference (CRISPRi). We show that certain essential genes and pathways are acutely sensitive to knockdown, providing a set of vulnerable targets for future therapeutic investigation. Screening our CRISPRi library against last-resort antibiotics uncovered genes and pathways that modulate beta-lactam sensitivity, an unexpected link between NADH dehydrogenase activity and growth inhibition by polymyxins, and anticorrelated phenotypes that may explain synergy between polymyxins and rifamycins. Our study demonstrates the power of systematic genetic approaches to identify vulnerabilities in Gram-negative pathogens and uncovers antibiotic-essential gene interactions that better inform combination therapies.IMPORTANCEAcinetobacter baumannii is a hospital-acquired pathogen that is resistant to many common antibiotic treatments. To combat resistant A. baumannii infections, we need to identify promising therapeutic targets and effective antibiotic combinations. In this study, we comprehensively characterize the genes and pathways that are critical for A. baumannii viability. We show that genes involved in aerobic metabolism are central to A. baumannii physiology and may represent appealing drug targets. We also find antibiotic-gene interactions that may impact the efficacy of carbapenems, rifamycins, and polymyxins, providing a new window into how these antibiotics function in mono- and combination therapies. Our studies offer a useful approach for characterizing interactions between drugs and essential genes in pathogens to inform future therapies.

Integrating core subtractive proteomics and reverse vaccinology for multi-epitope vaccine design against Rickettsia prowazekii endemic typhus

Rickettsia prowazekii is an intracellular, obligate, gram-negative coccobacillus responsible for epidemic typhus. Usually, the infected body louse or its excrement when rubbed into the skin abrasions transmits the disease. The infection with R. prowazekii causes the highest death rate (> 20% without antibiotic treatment and now 1-7%), followed by epidemic typhus, which often manifests in unsanitary conditions (up to 15-30%). Conventionally, vaccine design has required pathogen growth and both assays (in vivo and in vitro), which are costly and time-consuming. However, advancements in bioinformatics and computational biology have accelerated the development of effective vaccine designs, reducing the need for traditional, time-consuming laboratory experiments. Subtractive genomics and reverse vaccinology have become prominent computational methods for vaccine model construction. Therefore, the RefSeq sequence of Rickettsia prowazekii (strain Madrid E) (Proteome ID: UP000002480) was subjected to subtractive genomic analysis, including factors such as non-similarity to host proteome, essentiality, subcellular localization, antigenicity, non-allergenicity, and stability. Based on these parameters, the vaccine design process selected specific proteins such as outer membrane protein R (O05971_RICPR PETR; OmpR). Eventually, the OmpR was subjected to a reverse vaccinology approach that included molecular docking, immunological simulation, and the discovery of B-cell epitopes and MHC-I and MHC-II epitopes. Consequently, a chimeric or multi-epitope-based vaccine was proposed by selecting the V11 vaccine and its 3D structure modeling along with molecular docking against TLR and HLA protein, in silico simulation, and vector designing. The obtained results from this investigation resulted in a new perception of inhibitory ways against Rickettsia prowazekii by instigating novel immunogenic targets. To further assess the efficacy and protective ability of the newly designed V11 vaccine against Rickettsia prowazekii infections, additional evaluation such as in vitro or in vivo immunoassays is recommended.

PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BACKGROUND

In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

RESULTS

We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.

CONCLUSIONS

Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Text mining for contexts and relationships in cancer genomics literature

MOTIVATION

Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies.

RESULTS

We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts.

AVAILABILITY AND IMPLEMENTATION

Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.

Transposon sequencing reveals the essential gene set and genes enabling gut symbiosis in the insect symbiont Caballeronia insecticola

Caballeronia insecticola is a bacterium belonging to the Burkholderia genus sensu lato, which is able to colonize multiple environments like soils and the gut of the bean bug Riptortus pedestris. We constructed a saturated Himar1 mariner transposon library and revealed by transposon-sequencing that 498 protein-coding genes constitute the essential genome of Caballeronia insecticola for growth in free-living conditions. By comparing essential gene sets of Caballeronia insecticola and seven related Burkholderia s.l. strains, only 120 common genes were identified, indicating that a large part of the essential genome is strain-specific. In order to reproduce specific nutritional conditions that are present in the gut of Riptortus pedestris, we grew the mutant library in minimal media supplemented with candidate gut nutrients and identified several condition-dependent fitness-defect genes by transposon-sequencing. To validate the robustness of the approach, insertion mutants in six fitness genes were constructed and their growth deficiency in media supplemented with the corresponding nutrient was confirmed. The mutants were further tested for their efficiency in Riptortus pedestris gut colonization, confirming that gluconeogenic carbon sources, taurine and inositol, are nutrients consumed by the symbiont in the gut. Thus, our study provides insights about specific contributions provided by the insect host to the bacterial symbiont.

The specificity landscape of bacterial ribonuclease P

Developing quantitative models of substrate specificity for RNA processing enzymes is a key step toward understanding their biology and guiding applications in biotechnology and biomedicine. Optimally, models to predict relative rate constants for alternative substrates should integrate an understanding of structures of the enzyme bound to "fast" and "slow" substrates, large datasets of rate constants for alternative substrates, and transcriptomic data identifying in vivo processing sites. Such data are either available or emerging for bacterial ribonucleoprotein RNase P a widespread and essential tRNA 5' processing endonuclease, thus making it a valuable model system for investigating principles of biological specificity. Indeed, the well-established structure and kinetics of bacterial RNase P enabled the development of high throughput measurements of rate constants for tRNA variants and provided the necessary framework for quantitative specificity modeling. Several studies document the importance of conformational changes in the precursor tRNA substrate as well as the RNA and protein subunits of bacterial RNase P during binding, although the functional roles and dynamics are still being resolved. Recently, results from cryo-EM studies of E. coli RNase P with alternative precursor tRNAs are revealing prospective mechanistic relationships between conformational changes and substrate specificity. Yet, extensive uncharted territory remains, including leveraging these advances for drug discovery, achieving a complete accounting of RNase P substrates, and understanding how the cellular context contributes to RNA processing specificity in vivo.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Design of a multi-epitope vaccine (vme-VAC/MST-1) against cholera and vibriosis based on reverse vaccinology and immunoinformatics approaches

Vibriosis and cholera are serious diseases distributed worldwide and caused by six marine bacteria of the Vibrio genus. Thousands of deaths occur each year due to these illnesses, necessitating the development of new preventive measures. Presently, the existing cholera vaccine demonstrates an effectiveness of approximately 60%. Here we describe a new multi-epitope vaccine, 'vme-VAC/MST-1' based on vaccine targets identified by reverse vaccinology and epitopes predicted by immunoinformatics, two currently effective tools for predicting new vaccines for bacterial pathogens. The vaccine was designed to combat vibriosis and cholera by incorporating epitopes predicted for CTL, HTL, and B cells. These epitopes were identified from six vaccine targets revealed through subtractive genomics, combined with reverse vaccinology, and were further filtered using immunoinformatics approaches based on their predicted immunogenicity. To construct the vaccine, 28 epitopes (24 CTL/B and 4 HTL/B) were linked to the sequence of the cholera toxin B subunit adjuvant. In silico analyses indicate that the resulting immunogen is stable, soluble, non-toxic, and non-allergenic. Furthermore, it exhibits no homology to the host and demonstrates a strong capacity to elicit innate, B-cell, and T-cell immune responses. Our analysis suggests that it is likely to elicit immune reactions mediated through the TLR5 pathway, as evidenced by the molecular docking of the vaccine with the receptor, which revealed high affinity and a favorable reaction. Thus, vme-VAC/MST-1 is predicted to be a safe and effective solution against pathogenic Vibrio spp. However, further experimental analyses are required to measure the vaccine's effects In vivo.Communicated by Ramaswamy H. Sarma.

Potency of CRISPR-Cas Antifungals Is Enhanced by Cotargeting DNA Repair and Growth Regulatory Machinery at the Genetic Level

The emergence of virulent, resistant, and rapidly evolving fungal pathogens poses a significant threat to public health, agriculture, and the environment. Targeting cellular processes with standard small-molecule intervention may be effective but requires long development times and is prone to antibiotic resistance. To overcome the current limitations of antibiotic development and treatment, this study harnesses CRISPR-Cas systems as antifungals by capitalizing on their adaptability, specificity, and efficiency in target design. The conventional design of CRISPR-Cas antimicrobials, based on induction of DNA double-strand breaks (DSBs), is potentially less effective in fungi due to robust eukaryotic DNA repair machinery. Here, we report a novel design principle to formulate more effective CRISPR-Cas antifungals by cotargeting essential genes with DNA repair defensive genes that remove the fungi's ability to repair the DSB sites of essential genes. By evaluating this design on the model fungus Saccharomyces cerevisiae, we demonstrated that essential and defensive gene cotargeting is more effective than either essential or defensive gene targeting alone. The top-performing CRISPR-Cas antifungals performed as effectively as the antibiotic Geneticin. A gene cotargeting interaction analysis revealed that cotargeting essential genes with RAD52 involved in homologous recombination (HR) was the most synergistic combination. Fast growth kinetics of S. cerevisiae induced resistance to CRISPR-Cas antifungals, where genetic mutations mostly occurred in defensive genes and guide RNA sequences.

Improving Gibberellin GA3 Production with the Construction of a Genome-Scale Metabolic Model of Fusarium fujikuroi

Liquid fermentation is the primary method for GA3 production usingFusarium fujikuroi. However, production capacity is limited due to unknown metabolic pathways. To address this, we constructed a genome-scale metabolic model (iCY1235) with 1753 reactions, 1979 metabolites, and 1235 genes to understand the GA3 regulation mechanisms. The model was validated by analyzing growth rates under different glucose uptake rates and identifying essential genes. We used the model to optimize fermentation conditions, including carbon sources and dissolved oxygen. Through the OptForce algorithm, we identified 20 reactions as targets. Overexpressing FFUJ_02053 and FFUJ_14337 resulted in a 37.5 and 75% increase in GA3 titers, respectively. These targets enhance carbon flux toward GA3 production. Our model holds promise for guiding the metabolic engineering of F. fujikuroi to achieve targeted overproduction. In summary, our study utilizes the iCY1235 model to understand GA3 regulation, optimize fermentation conditions, and identify specific targets for enhancing GA3 production through metabolic engineering.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker

BACKGROUND

Salmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome.

RESULTS

Subtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein.

CONCLUSION

In summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Design rules for efficient endosomal escape

The inefficient translocation of proteins across biological membranes limits their application as therapeutic compounds and research tools. In most cases, translocation involves two steps: uptake into the endocytic pathway and endosomal escape. Certain charged or amphiphilic molecules promote protein uptake but few enable efficient endosomal escape. One exception is ZF5.3, a mini-protein that exploits natural endosomal maturation machinery to translocate across endosomal membranes. Although certain ZF5.3-protein conjugates are delivered efficiently into the cytosol or nucleus, overall delivery efficiency varies widely with no obvious design rules. Here we evaluate the role of protein size and thermal stability in the ability to efficiently escape endosomes when attached to ZF5.3. Using fluorescence correlation spectroscopy, a single-molecule technique that provides a precise measure of intra-cytosolic protein concentration, we demonstrate that delivery efficiency depends on both size and the ease with which a protein unfolds. Regardless of size and pI, low-Tm cargos of ZF5.3 (including intrinsically disordered domains) bias its endosomal escape route toward a high-efficiency pathway that requires the homotypic fusion and protein sorting (HOPS) complex. Small protein domains are delivered with moderate efficiency through the same HOPS portal even if the Tm is high. These findings imply a novel protein- and/or lipid-dependent pathway out of endosomes that is exploited by ZF5.3 and provide clear guidance for the selection or design of optimally deliverable therapeutic cargo.

Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB

Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.

Proteome level analysis of drug-resistant Prevotella melaninogenica for the identification of novel therapeutic candidates

The management of infectious diseases has become more critical due to the development of novel pathogenic strains with enhanced resistance. Prevotella melaninogenica, a gram-negative bacterium, was found to be involved in various infections of the respiratory tract, aerodigestive tract, and gastrointestinal tract. The need to explore novel drug and vaccine targets against this pathogen was triggered by the emergence of antimicrobial resistance against reported antibiotics to combat P. melaninogenica infections. The study involves core genes acquired from 14 complete P. melaninogenica strain genome sequences, where promiscuous drug and vaccine candidates were explored by state-of-the-art subtractive proteomics and reverse vaccinology approaches. A stringent bioinformatics analysis enlisted 18 targets as novel, essential, and non-homologous to humans and having druggability potential. Moreover, the extracellular and outer membrane proteins were subjected to antigenicity, allergenicity, and physicochemical analysis for the identification of the candidate proteins to design multi-epitope vaccines. Two candidate proteins (ADK95685.1 and ADK97014.1) were selected as the best target for the designing of a vaccine construct. Lead B- and T-cell overlapped epitopes were joined to generate potential chimeric vaccine constructs in combination with adjuvants and linkers. Finally, a prioritized vaccine construct was found to have stable interactions with the human immune cell receptors as confirmed by molecular docking and MD simulation studies. The vaccine construct was found to have cloning and expression ability in the bacterial cloning system. Immune simulation ensured the elicitation of significant immune responses against the designed vaccine. In conclusion, our study reported novel drug and vaccine targets and designed a multi-epitope vaccine against the P. melaninogenica infection. Further experimental validation will help open new avenues in the treatment of this multi-drug-resistant pathogen.

Mechanisms of Virulence Reprogramming in Bacterial Pathogens

Bacteria are single-celled organisms that carry a comparatively small set of genetic information, typically consisting of a few thousand genes that can be selectively activated or repressed in an energy-efficient manner and transcribed to encode various biological functions in accordance with environmental changes. Research over the last few decades has uncovered various ingenious molecular mechanisms that allow bacterial pathogens to sense and respond to different environmental cues or signals to activate or suppress the expression of specific genes in order to suppress host defenses and establish infections. In the setting of infection, pathogenic bacteria have evolved various intelligent mechanisms to reprogram their virulence to adapt to environmental changes and maintain a dominant advantage over host and microbial competitors in new niches. This review summarizes the bacterial virulence programming mechanisms that enable pathogens to switch from acute to chronic infection, from local to systemic infection, and from infection to colonization. It also discusses the implications of these findings for the development of new strategies to combat bacterial infections.

Essential genes: a cross-species perspective

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

Essential Gene Knockdowns Reveal Genetic Vulnerabilities and Antibiotic Sensitivities in Acinetobacter baumannii

The emergence of multidrug-resistant Gram-negative bacteria underscores the need to define genetic vulnerabilities that can be therapeutically exploited. The Gram-negative pathogen, Acinetobacter baumannii, is considered an urgent threat due to its propensity to evade antibiotic treatments. Essential cellular processes are the target of existing antibiotics and a likely source of new vulnerabilities. Although A. baumannii essential genes have been identified by transposon sequencing (Tn-seq), they have not been prioritized by sensitivity to knockdown or antibiotics. Here, we take a systems biology approach to comprehensively characterize A. baumannii essential genes using CRISPR interference (CRISPRi). We show that certain essential genes and pathways are acutely sensitive to knockdown, providing a set of vulnerable targets for future therapeutic investigation. Screening our CRISPRi library against last-resort antibiotics uncovered genes and pathways that modulate beta-lactam sensitivity, an unexpected link between NADH dehydrogenase activity and growth inhibition by polymyxins, and anticorrelated phenotypes that underpin synergy between polymyxins and rifamycins. Our study demonstrates the power of systematic genetic approaches to identify vulnerabilities in Gram-negative pathogens and uncovers antibiotic-essential gene interactions that better inform combination therapies.

Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.

Targeted insertion and reporter transgene activity at a gene safe harbor of the human blood fluke, Schistosoma mansoni

The identification and characterization of genomic safe harbor sites (GSHs) can facilitate consistent transgene activity with minimal disruption to the host cell genome. We combined computational genome annotation and chromatin structure analysis to predict the location of four GSHs in the human blood fluke, Schistosoma mansoni, a major infectious pathogen of the tropics. A transgene was introduced via CRISPR-Cas-assisted homology-directed repair into one of the GSHs in the egg of the parasite. Gene editing efficiencies of 24% and transgene-encoded fluorescence of 75% of gene-edited schistosome eggs were observed. The approach advances functional genomics for schistosomes by providing a tractable path for generating transgenics using homology-directed, repair-catalyzed transgene insertion. We also suggest that this work will serve as a roadmap for the development of similar approaches in helminths more broadly.

Census of exposed aggregation-prone regions in proteomes

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome

Protein products of essential genes, indispensable for organismal survival, are highly conserved and bring about fundamental functions. Interestingly, proteins that contain amino acid homorepeats that tend to evolve rapidly are enriched in eukaryotic essentialomes. Why are proteins with hypermutable homorepeats enriched in conserved and functionally vital essential proteins? We solve this functional versus evolutionary paradox by demonstrating that human essential proteins with homorepeats bring about crosstalk across biological processes through high interactability and have distinct regulatory functions affecting expansive global regulation. Importantly, essential proteins with homorepeats rapidly diverge with the amino acid substitutions frequently affecting functional sites, likely facilitating rapid adaptability. Strikingly, essential proteins with homorepeats influence human-specific embryonic and brain development, implying that the presence of homorepeats could contribute to the emergence of human-specific processes. Thus, we propose that homorepeat-containing essential proteins affecting species-specific traits can be potential intervention targets across pathologies, including cancers and neurological disorders.

Anti-Vibrio parahaemolyticus compounds from Streptomyces parvus based on Pan-genome and subtractive proteomics

Introduction

Vibrio parahaemolyticus is a foodborne pathogen commonly found in seafood, and drug resistance poses significant challenges to its control. This study aimed to identify novel drug targets for antibacterial drug discovery.

Methods

To identify drug targets, we performed a pan-genome analysis on 58 strains of V. parahaemolyticus genomes to obtain core genes. Subsequently, subtractive proteomics and physiochemical checks were conducted on the core proteins to identify potential therapeutic targets. Molecular docking was then employed to screen for anti-V. parahaemolyticus compounds using a in-house compound library of Streptomyces parvus, chosen based on binding energy. The anti-V. parahaemolyticus efficacy of the identified compounds was further validated through a series of experimental tests.

Results and Discussion

Pangenome analysis of 58 V. parahaemolyticus genomes revealed that there were 1,392 core genes. After Subtractive proteomics and physiochemical checks, Flagellar motor switch protein FliN was selected as a therapeutic target against V. parahaemolyticus. FliN was modeled and docked with Streptomyces parvus source compounds, and Actinomycin D was identified as a potential anti-V. parahaemolyticus agent with a strong binding energy. Experimental verification confirmed its effectiveness in killing V. parahaemolyticus and significantly inhibiting biofilm formation and motility. This study is the first to use pan-genome and subtractive proteomics to identify new antimicrobial targets for V. parahaemolyticus and to identify the anti-V. parahaemolyticus effect of Actinomycin D. These findings suggest potential avenues for the development of new antibacterial drugs to control V. parahaemolyticus infections.