A Systematic Bioinformatics Approach for Mapping the Minimal Set of a Viral Peptidome.

Sequence changes in viral genomes generate protein sequence diversity that enables viruses to evade the host immune system, hindering the development of effective preventive and therapeutic interventions. The massive proliferation of sequence data provides unprecedented opportunities to study viral adaptation and evolution. An alignment-free approach removes various restrictions posed by an alignment-dependent approach for studying sequence diversity. The publicly available tool, UNIQmin, offers an alignment-free approach for studying viral sequence diversity at any given rank of taxonomy lineage and is big data ready. The tool performs an exhaustive search to determine the minimal set of sequences required to capture the peptidome diversity within a given dataset. This compression is possible through the removal of identical sequences and unique sequences that do not contribute effectively to the peptidome diversity pool. Herein, we describe a detailed four-part protocol utilizing UNIQmin to generate the minimal set for the purpose of viral diversity analyses, alignment-free at any rank of the taxonomy lineage, using the recent global public health threat Monkeypox virus (MPX) sequence data as a case study. The protocol enables a systematic bioinformatics approach to study sequence diversity across taxonomic lineages, which is crucial for our future preparedness against viral epidemics. This is particularly important when data are abundant, freely available, and alignment is not an option. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Tool installation and input file preparation Basic Protocol 2: Generation of a minimal set of sequences for a given dataset Basic Protocol 3: Comparative minimal set analysis across taxonomic lineage ranks Basic Protocol 4: Factors affecting the minimal set of sequences.

A global initiative on addressing bioinformatics' grand challenges.

The Bioinformatics Grand Challenges Consortium (BGCC) is a collaborative effort to address the most pressing challenges in bioinformatics. Initially focusing on education and training, the consortium successfully defined seven key grand challenges and is actively developing actionable solutions for these challenges. Building on this foundation, the BGCC plans to broaden its focus to include additional grand challenges in emerging areas.

The Role of the Gut Microbiome in Hematological Cancers.

Humans are in a complex symbiotic relationship with a wide range of microbial organisms, including bacteria, viruses, and fungi. The evolution and composition of the human microbiome can be an indicator of how it may affect human health and susceptibility to diseases. Microbiome alteration, termed as dysbiosis, has been linked to the pathogenesis and progression of hematological cancers. A variety of mechanisms, including epithelial barrier disruption, local chronic inflammation response trigger, antigen dis-sequestration, and molecular mimicry, have been proposed to be associated with gut microbiota. Dysbiosis may be induced or worsened by cancer therapies (such as chemotherapy and/or hematopoietic stem cell transplantation) or infection. The use of antibiotics during treatment may also promote dysbiosis, with possible long-term consequences. The aim of this review is to provide a succinct summary of the current knowledge describing the role of the microbiome in hematological cancers, as well as its influence on their therapies. Modulation of the gut microbiome, involving modifying the composition of the beneficial microorganisms in the management and treatment of hematological cancers is also discussed. Additionally discussed are the latest developments in modeling approaches and tools used for computational analyses, interpretation and better understanding of the gut microbiome data.

Human ACE2 orthologous peptide sequences show better binding affinity to SARS-CoV-2 RBD domain: Implications for drug design.

Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of human ACE2 helical peptide were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 peptide docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy; -14 kcal/mol) predicted from the docking results. The MD simulation confirmed the stability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.

Identification of Genetic Alterations in Rapid Progressive Glioblastoma by Use of Whole Exome Sequencing.

BACKGROUND: Glioblastoma poses an inevitable threat to patients despite aggressive therapy regimes. It displays a great level of molecular heterogeneity and numerous substitutions in several genes have been documented. Next-generation sequencing techniques have identified various molecular signatures that have led to a better understanding of the molecular pathogenesis of glioblastoma. In this limited study, we sought to identify genetic variants in a small number of rare patients with aggressive glioblastoma. METHODS: Five tumor tissue samples were isolated from four patients with rapidly growing glioblastoma. Genomic DNA was isolated and whole exome sequencing was used to study protein-coding regions. Generated FASTQ files were analyzed and variants were called for each sample. Variants were prioritized with different approaches and functional annotation was applied for the detrimental variants. RESULTS: A total of 49,780 somatic variants were identified in the five glioblastoma samples studied, with the majority as missense substitutions. The top ten genes with the highest number of substitutions were MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1. Notably, variant prioritization after annotation indicated that the MTCH2 (Chr11: 47647265 A>G) gene sequence change was putative deleterious in all of the aggressive tumor samples. CONCLUSION: The MTCH2 (Chr11: 47647265 A>G) gene substitution was identified as putative deleterious in highly aggressive glioblastomas, which merits further investigation. Moreover, a high tumor mutation burden was observed, with a signature of the highest substitutions in MUC3A, MUC4, MUC6, OR4C5, PDE4DIP, AHNAK2, OR4C3, ZNF806, TTN, and RP1L1 genes. The findings provide critical, initial data for the further rational design of genetic screening and diagnostic approaches against aggressive glioblastoma.

Genomic analysis and biological characterization of a novel Schitoviridae phage infecting Vibrio alginolyticus.

Vibrio alginolyticus is a Gram-negative bacterium commonly associated with mackerel poisoning. A bacteriophage that specifically targets and lyses this bacterium could be employed as a biocontrol agent for treating the bacterial infection or improving the shelf-life of mackerel products. However, only a few well-characterized V. alginolyticus phages have been reported in the literature. In this study, a novel lytic phage, named ΦImVa-1, specifically infecting V. alginolyticus strain ATCC 17749, was isolated from Indian mackerel. The phage has a short latent period of 15 min and a burst size of approximately 66 particles per infected bacterium. ΦImVa-1 remained stable for 2 h at a wide temperature (27-75 °C) and within a pH range of 5 to 10. Transmission electron microscopy revealed that ΦImVa-1 has an icosahedral head of approximately 60 nm in diameter with a short tail, resembling those in the Schitoviridae family. High throughput sequencing and bioinformatics analysis elucidated that ΦImVa-1 has a linear dsDNA genome of 77,479 base pairs (bp), with a G + C content of ~ 38.72% and 110 predicted gene coding regions (106 open reading frames and four tRNAs). The genome contains an extremely large virion-associated RNA polymerase gene and two smaller non-virion-associated RNA polymerase genes, which are hallmarks of schitoviruses. No antibiotic genes were found in the ΦImVa-1 genome. This is the first paper describing the biological properties, morphology, and the complete genome of a V. alginolyticus-infecting schitovirus. When raw mackerel fish flesh slices were treated with ΦImVa-1, the pathogen loads reduced significantly, demonstrating the potential of the phage as a biocontrol agent for V. alginolyticus strain ATCC 17749 in the food. KEY POINTS: • A novel schitovirus infecting Vibrio alginolyticus ATCC 17749 was isolated from Indian mackerel. • The complete genome of the phage was determined, analyzed, and compared with other phages. • The phage is heat stable making it a potential biocontrol agent in extreme environments.

Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers.

The human leukocyte antigen (HLA) system is a major factor controlling cancer immunosurveillance and response to immunotherapy, yet its status in pediatric cancers remains fragmentary. We determined high-confidence HLA genotypes in 576 children, adolescents and young adults with recurrent/refractory solid tumors from the MOSCATO-01 and MAPPYACTS trials, using normal and tumor whole exome and RNA sequencing data and benchmarked algorithms. There was no evidence for narrowed HLA allelic diversity but discordant homozygosity and allele frequencies across tumor types and subtypes, such as in embryonal and alveolar rhabdomyosarcoma, neuroblastoma MYCN and 11q subtypes, and high-grade glioma, and several alleles may represent protective or susceptibility factors to specific pediatric solid cancers. There was a paucity of somatic mutations in HLA and antigen processing and presentation (APP) genes in most tumors, except in cases with mismatch repair deficiency or genetic instability. The prevalence of loss-of-heterozygosity (LOH) ranged from 5.9 to 7.7% in HLA class I and 8.0 to 16.7% in HLA class II genes, but was widely increased in osteosarcoma and glioblastoma (~15-25%), and for DRB1-DQA1-DQB1 in Ewing sarcoma (~23-28%) and low-grade glioma (~33-50%). HLA class I and HLA-DR antigen expression was assessed in 194 tumors and 44 patient-derived xenografts (PDXs) by immunochemistry, and class I and APP transcript levels quantified in PDXs by RT-qPCR. We confirmed that HLA class I antigen expression is heterogeneous in advanced pediatric solid tumors, with class I loss commonly associated with the transcriptional downregulation of HLA-B and transporter associated with antigen processing (TAP) genes, whereas class II antigen expression is scarce on tumor cells and occurs on immune infiltrating cells. Patients with tumors expressing sufficient HLA class I and TAP levels such as some glioma, osteosarcoma, Ewing sarcoma and non-rhabdomyosarcoma soft-tissue sarcoma cases may more likely benefit from T cell-based approaches, whereas strategies to upregulate HLA expression, to expand the immunopeptidome, and to target TAP-independent epitopes or possibly LOH might provide novel therapeutic opportunities in others. The consequences of HLA class II expression by immune cells remain to be established. Immunogenetic profiling should be implemented in routine to inform immunotherapy trials for precision medicine of pediatric cancers.

Computational design and characterization of a multiepitope vaccine against carbapenemase-producing Klebsiella pneumoniae strains, derived from antigens identified through reverse vaccinology.

Klebsiella pneumoniae is a Gram-negative pathogen of clinical relevance, which can provoke serious urinary and blood infections and pneumonia. This bacterium is a major public health threat due to its resistance to several antibiotic classes. Using a reverse vaccinology approach, 7 potential antigens were identified, of which 4 were present in most of the sequences of Italian carbapenem-resistant K. pneumoniae clinical isolates. Bioinformatics tools demonstrated the antigenic potential of these bacterial proteins and allowed for the identification of T and B cell epitopes. This led to a rational design and in silico characterization of a multiepitope vaccine against carbapenem-resistant K. pneumoniae strains. As adjuvant, the mycobacterial heparin-binding hemagglutinin adhesin (HBHA), which is a Toll-like receptor 4 (TLR-4) agonist, was included, to increase the immunogenicity of the construct. The multiepitope vaccine candidate was analyzed by bioinformatics tools to assess its antigenicity, solubility, allergenicity, toxicity, physical and chemical parameters, and secondary and tertiary structures. Molecular docking binding energies to TLR-2 and TLR-4, two important innate immunity receptors involved in the immune response against K. pneumoniae infections, and molecular dynamics simulations of such complexes supported active interactions. A codon optimized multiepitope sequence cloning strategy is proposed, for production of recombinant vaccine in classical bacterial vectors. Finally, a 3 dose-immunization simulation with the multiepitope construct induced both cellular and humoral immune responses. These results suggest that this multiepitope construct has potential as a vaccination strategy against carbapenem-resistant K. pneumoniae and deserves further validation.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences.

BACKGROUND: Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications. RESULTS: This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions. CONCLUSION: Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage.

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

Avian Influenza H7N9 Virus Adaptation to Human Hosts.

Avian influenza virus A (H7N9), after circulating in avian hosts for decades, was identified as a human pathogen in 2013. Herein, amino acid substitutions possibly essential for human adaptation were identified by comparing the 4706 aligned overlapping nonamer position sequences (1-9, 2-10, etc.) of the reported 2014 and 2017 avian and human H7N9 datasets. The initial set of virus sequences (as of year 2014) exhibited a total of 109 avian-to-human (A2H) signature amino acid substitutions. Each represented the most prevalent substitution at a given avian virus nonamer position that was selectively adapted as the corresponding index (most prevalent sequence) of the human viruses. The majority of these avian substitutions were long-standing in the evolution of H7N9, and only 17 were first detected in 2013 as possibly essential for the initial human adaptation. Strikingly, continued evolution of the avian H7N9 virus has resulted in avian and human protein sequences that are almost identical. This rapid and continued adaptation of the avian H7N9 virus to the human host, with near identity of the avian and human viruses, is associated with increased human infection and a predicted greater risk of human-to-human transmission.

Dynamics of Influenza A (H5N1) virus protein sequence diversity.

BACKGROUND: Influenza A (H5N1) virus is a global concern with potential as a pandemic threat. High sequence variability of influenza A viruses is a major challenge for effective vaccine design. A continuing goal towards this is a greater understanding of influenza A (H5N1) proteome sequence diversity in the context of the immune system (antigenic diversity), the dynamics of mutation, and effective strategies to overcome the diversity for vaccine design. METHODS: Herein, we report a comprehensive study of the dynamics of H5N1 mutations by analysis of the aligned overlapping nonamer positions (1-9, 2-10, etc.) of more than 13,000 protein sequences of avian and human influenza A (H5N1) viruses, reported over at least 50 years. Entropy calculations were performed on 9,408 overlapping nonamer position of the proteome to study the diversity in the context of immune system. The nonamers represent the predominant length of the binding cores for peptides recognized by the cellular immune system. To further dissect the sequence diversity, each overlapping nonamer position was quantitatively analyzed for four patterns of sequence diversity motifs: index, major, minor and unique. RESULTS: Almost all of the aligned overlapping nonamer positions of each viral proteome exhibited variants (major, minor, and unique) to the predominant index sequence. Each variant motif displayed a characteristic pattern of incidence change in relation to increased total variants. The major variant exhibited a restrictive pyramidal incidence pattern, with peak incidence at 50% total variants. Post this peak incidence, the minor variants became the predominant motif for majority of the positions. Unique variants, each sequence observed only once, were present at nearly all of the nonamer positions. The diversity motifs (index and variants) demonstrated complex inter-relationships, with motif switching being a common phenomenon. Additionally, 25 highly conserved sequences were identified to be shared across viruses of both hosts, with half conserved to several other influenza A subtypes. DISCUSSION: The presence of distinct sequences (nonatypes) at nearly all nonamer positions represents a large repertoire of reported viral variants in the proteome, which influence the variability dynamics of the viral population. This work elucidated and provided important insights on the components that make up the viral diversity, delineating inherent patterns in the organization of sequence changes that function in the viral fitness-selection. Additionally, it provides a catalogue of all the mutational changes involved in the dynamics of H5N1 viral diversity for both avian and human host populations. This work provides data relevant for the design of prophylactics and therapeutics that overcome the diversity of the virus, and can aid in the surveillance of existing and future strains of influenza viruses.

Identification of highly conserved, serotype-specific dengue virus sequences: implications for vaccine design.

BACKGROUND: The sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome. METHODS: All reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon's entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database. RESULTS: A total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity. CONCLUSION: This work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens.

Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics.

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.

Mapping HLA-A2, -A3 and -B7 supertype-restricted T-cell epitopes in the ebolavirus proteome.

BACKGROUND: Ebolavirus (EBOV) is responsible for one of the most fatal diseases encountered by mankind. Cellular T-cell responses have been implicated to be important in providing protection against the virus. Antigenic variation can result in viral escape from immune recognition. Mapping targets of immune responses among the sequence of viral proteins is, thus, an important first step towards understanding the immune responses to viral variants and can aid in the identification of vaccine targets. Herein, we performed a large-scale, proteome-wide mapping and diversity analyses of putative HLA supertype-restricted T-cell epitopes of Zaire ebolavirus (ZEBOV), the most pathogenic species among the EBOV family. METHODS: All publicly available ZEBOV sequences (14,098) for each of the nine viral proteins were retrieved, removed of irrelevant and duplicate sequences, and aligned. The overall proteome diversity of the non-redundant sequences was studied by use of Shannon's entropy. The sequences were predicted, by use of the NetCTLpan server, for HLA-A2, -A3, and -B7 supertype-restricted epitopes, which are relevant to African and other ethnicities and provide for large (~86%) population coverage. The predicted epitopes were mapped to the alignment of each protein for analyses of antigenic sequence diversity and relevance to structure and function. The putative epitopes were validated by comparison with experimentally confirmed epitopes. RESULTS & DISCUSSION: ZEBOV proteome was generally conserved, with an average entropy of 0.16. The 185 HLA supertype-restricted T-cell epitopes predicted (82 (A2), 37 (A3) and 66 (B7)) mapped to 125 alignment positions and covered ~24% of the proteome length. Many of the epitopes showed a propensity to co-localize at select positions of the alignment. Thirty (30) of the mapped positions were completely conserved and may be attractive for vaccine design. The remaining (95) positions had one or more epitopes, with or without non-epitope variants. A significant number (24) of the putative epitopes matched reported experimentally validated HLA ligands/T-cell epitopes of A2, A3 and/or B7 supertype representative allele restrictions. The epitopes generally corresponded to functional motifs/domains and there was no correlation to localization on the protein 3D structure. These data and the epitope map provide important insights into the interaction between EBOV and the host immune system.