Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis

Development of a novel multi-epitope mRNA vaccine candidate to combat SFTSV pandemic

Severe Fever with Thrombocytopenia Syndrome virus (SFTSV) is a novel identified pathogen, despite two decades of research on SFTSV, the potential widespread threats pose a significant challenge for researchers in developing new treatment and prevention methods. In this present, we have developed a multi-epitope mRNA vaccine for SFTSV and valid it with in silico methods. We screened 9 immunodominant epitopes for cytotoxic T cells (CTL), 7 for helper T cells (HTL), and 8 for Linear B-cell (LBL) based on promising candidate protein Gn, Gc, Np, and NSs. All predicted epitopes demonstrated strong antigenicity without any potential harm to humans. Additionally, the high conservancy is required to cover different strains. All epitopes as well as adjuvants were constructed into a final vaccine, which was further assesd by calculating of physicochemical properties. Then, we docked the vaccine protein with immune receptors and analyzed the complexes with dynamic simulations to evaluate its affinity to receptors. Finally, the vaccine sequence was constructed into a mRNA sequence. The constructed vaccine is a potential candidate for combating SFTSV by stimulating protective humoral and cellular immune responses.

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.

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.

Sifting through the core-genome to identify putative cross-protective antigens against Riemerella anatipestifer

Infectious serositis of ducks, caused by Riemerella anatipestifer, is one of the main infectious diseases that harm commercial ducks. Whole-strain-based vaccines with no or few cross-protection were observed between different serotypes of R. anatipestifer, and so far, control of infection is hampered by a lack of effective vaccines, especially subunit vaccines with cross-protection. Since the concept of reverse vaccinology was introduced, it has been widely used to screen for protective antigens in important pathogens. In this study, pan-genome binding reverse vaccinology, an emerging approach to vaccine candidate screening, was used to screen for cross-protective antigens against R. anatipestifer. Thirty proteins were identified from the core-genome as potential cross-protective antigens. Three of these proteins were recombinantly expressed, and their immunoreactivity with five antisera (anti-serotypes 1, 2, 6, 10, and 11) was demonstrated by Western blotting. Our study established a method for high-throughput screening of cross-protective antigens against R. anatipestifer in silico, which will lay the foundation for the development of a cross-protective subunit vaccine controlling R. anatipestifer infection. KEY POINTS: • Pan-genome binding reverse vaccine approach was first established in R. anatipestifer to screen for subunit vaccine candidates. • Thirty potential cross-protective antigens against R. anatipestifer were identified by this method. • The reliability of the method was verified preliminarily by the results of Western blotting of three of these potential antigens.

In silico analysis of Leishmania proteomes and protein-protein interaction network: Prioritizing therapeutic targets and drugs for repurposing to treat leishmaniasis

Leishmaniasis is a serious world health problem and its current therapies have several limitations demanding to develop novel therapeutics for this disease. The present study aims to prioritize novel broad-spectrum targets using proteomics and protein-protein interaction network (PPIN) data for 11 Leishmania species. Proteome comparison and host non-homology analysis resulted in 3605 pathogen-specific conserved core proteins. Gene ontology analysis indicated their involvement in major molecular functions like DNA binding, transportation, dioxygenase, and catalytic activity. PPIN analysis of these core proteins identified eight hub proteins (viz., vesicle-trafficking protein (LBRM2903_190011800), ribosomal proteins S17 (LBRM2903_34004790) and L2 (LBRM2903_080008100), eukaryotic translation initiation factor 3 (LBRM2903_350086700), replication factor A (LBRM2903_150008000), U3 small nucleolar RNA-associated protein (LBRM2903_340025600), exonuclease (LBRM2903_200021800), and mitochondrial RNA ligase (LBRM2903_200074100)). Among the hub proteins, six were classified as drug targets and two as vaccine candidates. Further, druggability analysis indicated three hub proteins, namely eukaryotic translation initiation factor 3, ribosomal proteins S17 and L2 as druggable. Their three-dimensional structures were modelled and docked with the identified ligands (2-methylthio-N6-isopentenyl-adenosine-5'-monophosphate, artenimol and omacetaxine mepesuccinate). These ligands could be experimentally validated (in vitro and in vivo) and repurposed for the development of novel antileishmanial agents.

A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets

Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.

A Systematic Immuno-Informatic Approach to Design a Multiepitope-Based Vaccine Against Emerging Multiple Drug Resistant Serratia marcescens

Serratia marcescens is now an important opportunistic pathogen that can cause serious infections in hospitalized or immunocompromised patients. Here, we used extensive bioinformatic analyses based on reverse vaccinology and subtractive proteomics-based approach to predict potential vaccine candidates against S. marcescens. We analyzed the complete proteome sequence of 49 isolate of Serratia marcescens and identified 5 that were conserved proteins, non-homologous from human and gut flora, extracellular or exported to the outer membrane, and antigenic. The identified proteins were used to select 5 CTL, 12 HTL, and 12 BCL epitopes antigenic, non-allergenic, conserved, hydrophilic, and non-toxic. In addition, HTL epitopes were able to induce interferon-gamma immune response. The selected peptides were used to design 4 multi-epitope vaccines constructs (SMV1, SMV2, SMV3 and SMV4) with immune-modulating adjuvants, PADRE sequence, and linkers. Peptide cleavage analysis showed that antigen vaccines are processed and presented via of MHC class molecule. Several physiochemical and immunological analyses revealed that all multiepitope vaccines were non-allergenic, stable, hydrophilic, and soluble and induced the immunity with high antigenicity. The secondary structure analysis revealed the designed vaccines contain mainly coil structure and alpha helix structures. 3D analyses showed high-quality structure. Molecular docking analyses revealed SMV4 as the best vaccine construct among the four constructed vaccines, demonstrating high affinity with the immune receptor. Molecular dynamics simulation confirmed the low deformability and stability of the vaccine candidate. Discontinuous epitope residues analyses of SMV4 revealed that they are flexible and can interact with antibodies. In silico immune simulation indicated that the designed SMV4 vaccine triggers an effective immune response. In silico codon optimization and cloning in expression vector indicate that SMV4 vaccine can be efficiently expressed in E. coli system. Overall, we showed that SMV4 multi-epitope vaccine successfully elicited antigen-specific humoral and cellular immune responses and may be a potential vaccine candidate against S. marcescens. Further experimental validations could confirm its exact efficacy, the safety and immunogenicity profile. Our findings bring a valuable addition to the development of new strategies to prevent and control the spread of multidrug-resistant Gram-negative bacteria with high clinical relevance.

A Comparative Genomics Approach for Shortlisting Broad-Spectrum Drug Targets in Nontuberculous Mycobacteria

Many members of nontuberculous mycobacteria (NTM) are opportunistic pathogens causing several infections in animals. The incidence of NTM infections and emergence of drug-resistant NTM strains are rising worldwide, emphasizing the need to develop novel anti-NTM drugs. The present study is aimed to identify broad-spectrum drug targets in NTM using a comparative genomics approach. The study identified 537 core proteins in NTM of which 45 were pathogen specific and essential for the survival of pathogens. Furthermore, druggability analysis indicated that 15 were druggable among those 45 proteins. These 15 proteins, which were core proteins, pathogen-specific, essential, and druggable, were considered as potential broad-spectrum candidates. Based on their locations in cytoplasm and membrane, targets were classified as drug and vaccine targets. The identified 15 targets were different enzymes, carrier proteins, transcriptional regulator, two-component system protein, ribosomal, and binding proteins. The identified targets could further be utilized by researchers to design inhibitors for the discovery of antimicrobial agents.

Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing

The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization's high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5-2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.

Novel Target Exploration from Hypothetical Proteins of Klebsiella pneumoniae MGH 78578 Reveals a Protein Involved in Host-Pathogen Interaction

The opportunistic pathogen Klebsiella pneumoniae is a causative agent of several hospital-acquired infections. It has become resistant to a wide range of currently available antibiotics, leading to high mortality rates among patients; this has further led to a demand for novel therapeutic intervention to treat such infections. Using a series of in silico analyses, the present study aims to explore novel drug/vaccine candidates from the hypothetical proteins of K. pneumoniae. A total of 540 proteins were found to be hypothetical in this organism. Analysis of these 540 hypothetical proteins revealed 30 pathogen-specific proteins essential for pathogen survival. A motifs/domain family analysis, similarity search against known proteins, gene ontology, and protein–protein interaction analysis of the shortlisted 30 proteins led to functional assignment for 17 proteins. They were mainly cataloged as enzymes, lipoproteins, stress-induced proteins, transporters, and other proteins (viz., two-component proteins, skeletal proteins and toxins). Among the annotated proteins, 16 proteins, located in the cytoplasm, periplasm, and inner membrane, were considered as potential drug targets, and one extracellular protein was considered as a vaccine candidate. A druggability analysis indicated that the identified 17 drug/vaccine candidates were “novel”. Furthermore, a host–pathogen interaction analysis of these identified target candidates revealed a betaine/carnitine/choline transporters (BCCT) family protein showing interactions with five host proteins. Structure prediction and validation were carried out for this protein, which could aid in structure-based inhibitor design.

Phyllosphere of staple crops under pig manure fertilization, a reservoir of antibiotic resistance genes

In China, the common use of antibiotics in agriculture is recognized as a potential public health risk through the increasing use of livestock derived manure as a means of fertilization. By doing so this may increase the transfer of antibiotic resistance genes (ARGs) from animals, to soils and plants. In this study two staple crops (rice and wheat) were investigated for ARG enrichment under differing fertilization regimes. Here, we applied 4 treatments, no fertilizer, mineral fertilizer, clean (reduced antibiotic practice) and dirty (current antibiotic practice) pig manure, to soil microcosms planted with either rice or wheat, to investigate fertilization effects on the abundance of ARGs in the respective phyllospheres. For both rice and wheat, samples were collected after two separate fertilization periods. In total, 162 unique ARGs and 5 mobile genetic elements (MGEs) were detected from all rice and wheat samples. The addition of both clean and dirty manure, enhanced ARG abundance significantly when compared to no fertilizer treatments (P < 0.001), though clean manure enriched ARGs to a lesser extent than dirty manure, in all rice and wheat samples (P < 0.001). The classes of ARGs recorded were different between crops, with wheat samples having a higher ARG diversity than rice. These results revealed that staple crops in China such as rice and wheat may be a reservoir for ARGs when clean and dirty pig manure is used for fertilization.

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/ .

Comparative metagenomic analyses of a high-altitude Himalayan geothermal spring revealed temperature-constrained habitat-specific microbial community and metabolic dynamics

Metagenomic surveys across microbial mat (~ 55 °C) samples of high-altitude (1760 m above sea level) Himalayan geothermal springs have revealed specialized community enriched with niche-specific functions. In this study, we have performed metagenomic sequence-based analyses to get insights into taxonomic composition and functional potential of hyperthermophiles in water (~ 95 °C) and sediment samples (78-98 °C). Community analyses revealed predominance of thermophilic bacterial and archeal genera dwelling in water in contrast to microbial mats (55 °C), namely Methylophilus, Methyloversatilis, Emticicia, Caulobacter, Thermus, Enhydrobacter and Pyrobaculum. Sediment samples having surface temperature (~ 78 °C) were colonized by Pyrobaculum and Chloroflexus while genus Massilia was found to be inhabited in high-temperature sediments (~ 98 °C). Functional analyses of metagenomic sequences revealed genetic enrichment of genes such as type IV secretion system, flagellar assembly and two-component system in contrast to mats. Furthermore, inter-sample comparison of enriched microbial diversity among water, sediment and microbial mats revealed habitat-specific clustering of the samples within same environment highlighting the role of temperature dynamics in modulating community structure across different habitats in same niche. However, function-based analysis demonstrated site-specific clustering among sediment, microbial mat and water samples. Furthermore, a novel thermophilic genotype of the genus Emticicia (designated as strain MM) was reconstructed from metagenome data. This is a correlative study between three major habitats present in geothermal spring environment, i.e., water, sediment and microbial mats revealing greater phylogenetic and functional dispersion emphasizing changing habitat-specific dynamics with temperature.

Long-Term Effect of Different Fertilization and Cropping Systems on the Soil Antibiotic Resistome

Different fertilization and cropping systems may influence short- and long-term residues of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in soil. Soils from dryland (peanut) and paddy (rice) fields, which originated from the same nonagricultural land (forested), were treated with either chemical fertilizer, composted manure, or no fertilizer for 26 years before sampling, which occurred one year after the last applications. ARGs and MGEs were investigated using highly parallel qPCR and high-throughput sequencing. Six of the 11 antibiotics measured by LC-MS/MS were detected in the manure applied soil, but not in the nonmanured soils, indicating their source was from previous manure applications. Compared to the unfertilized control, manure application did not show a large accumulation of ARGs in either cropping system but there were some minor effects of soil management on indigenous ARGs. Paddy soil showed higher accumulation of these ARGs, which corresponded to higher microbial biomass than the dryland soil. Chemical fertilizer increased relative abundance of these ARGs in dryland soil but decreased their relative abundance in paddy soil. These results show how long-term common soil management practices affect the abundance and type of ARGs and MGEs in two very different soil environments, one aerobic and the other primarily anaerobic.

Network biology discovers pathogen contact points in host protein-protein interactomes

In all organisms, major biological processes are controlled by complex protein-protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1MAIN). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSILRR) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

Functional assignment for essential hypothetical proteins of Staphylococcus aureus N315

Staphylococcus aureus, the causative agent of nosocomial infections worldwide, has acquired resistance to almost all antibiotics stressing the need to develop novel drugs against this pathogen. In S. aureus N315, 302 genes have been identified as essential genes, indispensable for growth and survival of the pathogen. The functions of 40 proteins encoded by S. aureus essential genes were found to be hypothetical and thus referred as essential hypothetical proteins (EHPs). The present study aims to carry out functional characterization of EHPs using bioinformatics tools/databases, whose performance was assessed by Receiver operating characteristic curve analysis. Evaluation of physicochemical parameters, homology search against known proteins, domain analysis, subcellular localization analysis and virulence prediction assisted us to characterize EHPs. Functional assignment for 35 EHPs was made with high confidence. They belong to different functional classes like enzymes, binding proteins, miscellaneous proteins, helicases, transporters and virulence factors. Around 35% of EHPs were from hydrolases family. A group of EHPs (32.5%) were predicted as virulence factors. Of 35, 19 essential pathogen-specific proteins were considered as probable drug targets. Two targets were found to be druggable and others were novel targets. Outcome of the study could aid to identify novel drugs for better treatment of S. aureus infections.

Influence of Soil Characteristics and Proximity to Antarctic Research Stations on Abundance of Antibiotic Resistance Genes in Soils

Soil is an important environmental reservoir of antibiotic resistance genes (ARGs), which are increasingly recognized as environmental contaminants. Methods to assess the risks associated with the acquisition or transfer of resistance mechanisms are still underdeveloped. Quantification of background levels of antibiotic resistance genes and what alters those is a first step in understanding our environmental resistome. Toward this goal, 62 samples were collected over 3 years from soils near the 30-year old Gondwana Research Station and for 4 years before and during development of the new Jang Bogo Research Station, both at Terra Nova Bay in Antarctica. These sites reflect limited and more extensive human impact, respectively. A qPCR array with 384 primer sets targeting antibiotic resistance genes and mobile genetic elements (MGEs) was used to detect and quantify these genes. A total of 73 ARGs and MGEs encompassing eight major antibiotic resistance gene categories were detected, but most at very low levels. Antarctic soil appeared to be a common reservoir for seven ARGs since they were present in most samples (42%-88%). If the seven widespread genes were removed, there was a correlation between the relative abundance of MGEs and ARGs, more typical of contaminated sites. There was a relationship between ARG content and distance from both research stations, with a significant effect at the Jang Bogo Station especially when excluding the seven widespread genes; however, the relative abundance of ARGs did not increase over the 4 year period. Silt, clay, total organic carbon, and SiO₂ were the top edaphic factors that correlated with ARG abundance. Overall, this study identifies that human activity and certain soil characteristics correlate with antibiotic resistance genes in these oligotrophic Antarctic soils and provides a baseline of ARGs and MGEs for future comparisons.

An antibiotic target ranking and prioritization pipeline combining sequence, structure and network-based approaches exemplified for Serratia marcescens

We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.