Complete Proteome Research Articles

Epitope profiling reveals binding signatures of SARS-CoV-2 immune response in natural infection and cross-reactivity with endemic human CoVs.

A major goal of current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine efforts is to elicit antibody responses that confer protection. Mapping the epitope targets of the SARS-CoV-2 antibody response is critical for vaccine design, diagnostics, and development of therapeutics. Here, we develop a pan-coronavirus phage display library to map antibody binding sites at high resolution within the complete viral proteomes of all known human-infecting coronaviruses in patients with mild or moderate/severe coronavirus disease 2019 (COVID-19). We find that the majority of immune responses to SARS-CoV-2 are targeted to the spike protein, nucleocapsid, and ORF1ab and include sites of mutation in current variants of concern. Some epitopes are identified in the majority of samples, while others are rare, and we find variation in the number of epitopes targeted between individuals. We find low levels of SARS-CoV-2 cross-reactivity in individuals with no exposure to the virus and significant cross-reactivity with endemic human coronaviruses (CoVs) in convalescent sera from patients with COVID-19.

Prediction of Epitope based Peptides for Vaccine Development from Complete Proteome of Novel Corona Virus (SARS-COV-2) Using Immunoinformatics.

COVID-19 is an infectious disease caused by a newly discovered corona virus SARS-COV-2. It is the most dangerous epidemic existing currently all over the world. To date, there is no licensed vaccine and not any particular efficient therapeutic agent available to prevent or cure the disease. So development of an effective vaccine is the urgent need of the time. The proposed study aims to identify potential vaccine candidates by screening the complete proteome of SARS-COV-2 using the computational approach. From 14 protein entries in UniProtKB, 4 proteins were screened for epitope prediction based on consensus antigenicity predictions and various physico-chemical criteria like transmembrane domain, allergenicity, GRAVY value, toxicity, stability index. Comprehensive analysis of these 4 antigens revealed that spike protein (P0DTC2) and nucleoprotein (P0DTC9) show the greatest potential for experimental immunogenicity analysis. These 2 proteins have several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen the matrix protein 1 [Influenza A virus (H5N1)]. In addition, the epitope SIIAYTMSL predicted from spike protein (P0DTC2) and epitope SPRWYFYYL predicted from nucleoprotein (P0DTC9) exhibited more than 60% population coverage in the target populations Europe, North America, South Asia, Northeast Asia taken in this study. These epitopes have also been found to exhibit highly significant TCR–pMHC interactions having a joint Z value of 4.51 and 4.37 respectively. Therefore, this analysis suggests that the predicted epitopes might be suitable vaccine candidates and should be subjected to further in-vivo and in-vitro studies.

Mining oomycete proteomes for metalloproteases leads to identification of candidate virulence factors in Phytophthora infestans.

Pathogens deploy a wide range of pathogenicity factors, including a plethora of proteases, to modify host tissue or manipulate host defences. Metalloproteases (MPs) have been implicated in virulence in several animal and plant pathogens. Here we investigated the repertoire of MPs in 46 stramenopile species including 37 oomycetes, 5 diatoms, and 4 brown algae. Screening their complete proteomes using hidden Markov models (HMMs) trained for MP detection resulted in over 4,000 MPs, with most species having between 65 and 100 putative MPs. Classification in clans and families according to the MEROPS database showed a highly diverse MP repertoire in each species. Analyses of domain composition, orthologous groups, distribution, and abundance within the stramenopile lineage revealed a few oomycete‐specific MPs and MPs potentially related to lifestyle. In‐depth analyses of MPs in the plant pathogen Phytophthora infestans revealed 91 MPs, divided over 21 protein families, including 25 MPs with a predicted signal peptide or signal anchor. Expression profiling showed different patterns of MP gene expression during pre‐infection and infection stages. When expressed in leaves of Nicotiana benthamiana, 12 MPs changed the sizes of lesions caused by inoculation with P. infestans; with 9 MPs the lesions were larger, suggesting a positive effect on the virulence of P. infestans, while 3 MPs had a negative effect, resulting in smaller lesions. To the best of our knowledge, this is the first systematic inventory of MPs in oomycetes and the first study pinpointing MPs as potential pathogenicity factors in Phytophthora.

Top Down Computational Approach: A Vaccine Development Step to Find Novel Superantigenic HLA Binding Epitopes from Dengue Virus Proteome.

Dengue virus (DENV) is a major mosquito vector based human pathogenic flavivirus which is causing major threat worldwide, yet the availability of therapeutic treatment and several vaccines, still called for advance treatment and vaccine development. The present top down computational approach is a vaccine development step to find novel super antigenic HLA binding epitopes from DENV proteome. The approach used sequence based screening to find complete conserve and high population coverage, common epitopes among all DENV serotype. Propred and Immune Epitope Data Base were used for sequence based screening with recommended parameters. Among top 29 identified epitopes, five structural protein epitopes viz. 33LQGRGPLKL41, 249VVVLGSQEG257, 172LVGIVTLYL180, 146MKILIGVVI154, 72YIIVGVEPG80 and one nonstructural protein epitope 18LKNDIPMTG26 were showed high conserve nature and high population coverage from complete DENV proteome. Further structure based study involving docking and molecular dynamic simulation to confirm stable behavior of HLA allele–peptide complex to give potent cell mediated immune response. Docking of epitope 72YIIVGVEPG80–DRB1 0401 allele and epitope 33LQGRGPLKL41–B*5101 allele complexes showed the best binding energy of − 7.71 and − 7.20 kcal/mol, respectively and stable binding pattern over the time window during molecular dynamic simulation. This computational approach resulted novel epitopes which can be used in the design and development of short epitope based vaccines as well as diagnosis tools for dengue infection.

Mining Unknown Porcine Protein Isoforms by Tissue-based Map of Proteome Enhances Pig Genome Annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model. In this study, we developed a tissue-based proteome map using 34 major normal pig tissues. A total of 5841 unknown protein isoforms were identified and systematically characterized, including 2225 novel protein isoforms, 669 protein isoforms from 460 genes symbolized beginning with LOC, and 2947 protein isoforms without clear NCBI annotation in the current pig reference genome. These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing of the same pig tissues, further improving the genome annotation of the corresponding protein-coding genes. Combining the well-annotated genes that have parallel expression pattern and subcellular witness, we predicted the tissue-related subcellularlocations and potential functions for these unknown proteins. Finally, we mined 3081 orthologous genes for 52.7% of unknown protein isoforms across multiple species, referring to 68 KEGG pathways as well as 23 disease signaling pathways. These findings provide valuable insights and a rich resource for enhancing studies of pig genomics and biology, as well as biomedical model application to human medicine.

Telomere length de novo assembly of all 7 chromosomes and mitogenome sequencing of the model entomopathogenic fungus, Metarhizium brunneum, by means of a novel assembly pipeline

BackgroundMore accurate and complete reference genomes have improved understanding of gene function, biology, and evolutionary mechanisms. Hybrid genome assembly approaches leverage benefits of both long, relatively error-prone reads from third-generation sequencing technologies and short, accurate reads from second-generation sequencing technologies, to produce more accurate and contiguous de novo genome assemblies in comparison to using either technology independently. In this study, we present a novel hybrid assembly pipeline that allowed for both mitogenome de novo assembly and telomere length de novo assembly of all 7 chromosomes of the model entomopathogenic fungus, Metarhizium brunneum.ResultsThe improved assembly allowed for better ab initio gene prediction and a more BUSCO complete proteome set has been generated in comparison to the eight current NCBI reference Metarhizium spp. genomes. Remarkably, we note that including the mitogenome in ab initio gene prediction training improved overall gene prediction. The assembly was further validated by comparing contig assembly agreement across various assemblers, assessing the assembly performance of each tool. Genomic synteny and orthologous protein clusters were compared between Metarhizium brunneum and three other Hypocreales species with complete genomes, identifying core proteins, and listing orthologous protein clusters shared uniquely between the two entomopathogenic fungal species, so as to further facilitate the understanding of molecular mechanisms underpinning fungal-insect pathogenesis.ConclusionsThe novel assembly pipeline may be used for other haploid fungal species, facilitating the need to produce high-quality reference fungal genomes, leading to better understanding of fungal genomic evolution, chromosome structuring and gene regulation.

Development of New Antiproliferative Compound against Human Tumor Cells from the Marine Microalgae Nannochloropsis gaditana by Applied Proteomics.

Proteomics is a crucial tool for unravelling the molecular dynamics of essential biological processes, becoming a pivotal technique for basic and applied research. Diverse bioinformatic tools are required to manage and explore the huge amount of information obtained from a single proteomics experiment. Thus, functional annotation and protein–protein interactions are evaluated in depth leading to the biological conclusions that best fit the proteomic response in the system under study. To gain insight into potential applications of the identified proteins, a novel approach named “Applied Proteomics” has been developed by comparing the obtained protein information with the existing patents database. The development of massive sequencing technology and mass spectrometry (MS/MS) improvements has allowed the application of proteomics nonmodel microorganisms, which have been deeply described as a novel source of metabolites. Between them, Nannochloropsis gaditana has been pointed out as an alternative source of biomolecules. Recently, our research group has reported the first complete proteome analysis of this microalga, which was analysed using the applied proteomics concept with the identification of 488 proteins with potential industrial applications. To validate our approach, we selected the UCA01 protein from the prohibitin family. The recombinant version of this protein showed antiproliferative activity against two tumor cell lines, Caco2 (colon adenocarcinoma) and HepG-2 (hepatocellular carcinoma), proving that proteome data have been transformed into relevant biotechnological information. From Nannochloropsis gaditana has been developed a new tool against cancer—the protein named UCA01. This protein has selective effects inhibiting the growth of tumor cells, but does not show any effect on control cells. This approach describes the first practical approach to transform proteome information in a potential industrial application, named “applied proteomics”. It is based on a novel bioalgorithm, which is able to identify proteins with potential industrial applications. From hundreds of proteins described in the proteome of N. gaditana, the bioalgorithm identified over 400 proteins with potential uses; one of them was selected as UCA01, “in vitro” and its potential was demonstrated against cancer. This approach has great potential, but the applications are potentially numerous and undefined.

DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift

The yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run, data-independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement, and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source-dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system readout pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation of targets of the GID E3 ligase. Moreover, this approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole-proteome-based readouts can serve as comprehensive systems-level assays in all cellular systems.

RNA Crosslinking to Analyze the Mitochondrial RNA-Binding Proteome.

Even though the mammalian mitochondrial genome (mtDNA) is very small and only codes for 13 proteins, all being subunits of the oxidative phosphorylation system, it requires several hundred nuclear encoded proteins for its maintenance and expression. These include replication and transcription factors, approximately 80 mitoribosomal proteins and many proteins involved in the posttranscriptional modification, processing, and stability of mitochondrial RNAs. In recent years, many of these factors have been identified and functionally characterized, but the complete mtRNA-interacting proteome is not firmly established. Shotgun proteomics has been used successfully to define whole-cell polyadenylated RNA (poly(A)-RNA) interacting proteomes using the nucleotide analogue 4-thiouridine (4SU) combined with UV crosslinking, poly(A)-RNA isolation and mass spectrometry to identify all poly(A)-RNA bound proteins. Although in this case also a considerable number of mitochondrial proteins were identified, the method was not specifically directed at the mitochondrial poly(A)-RNA bound proteome. Here we describe a method for enrichment of the mitochondrial poly(A)-RNA bound proteome based on 4SU labeling and UV crosslinking. The method can be applied either for isolated mitochondria prior to UV crosslinking or for whole-cell crosslinking followed by mitochondrial isolation.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Proteomic Profiling of the Human Tissue and Biological Fluid Proteome.

In-depth analysis of the human genome sequence has led to the annotation of approximately 20,000 human protein-coding genes. Although mass spectrometry (MS)-based workflows have made a great headway in achieving near genome-wide coverage, an equivalent complete map of the human proteome remains elusive. Delineating the spatial distribution of all human proteins at the organ, tissue, and cellular level can offer insight into health and disease and represents an excellent reference for the discovery of biomarkers and therapeutic targets. Here, we performed label-free liquid chromatography coupled to tandem MS (LC-MS/MS) to profile the normal human proteome. In total, we analyzed 117 samples from 46 normal tissues and organs at autopsy. Our high-resolution MS approach allowed for the quantification of 10,438 unique proteins. In order to expand our coverage of the human proteome, we combined our previously published biological fluid proteomic data from healthy individuals. We considered data from seven biological fluids, including urine, cerebrospinal fluid, synovial fluid, seminal plasma, sweat, cervical vaginal fluid, and nipple aspirate fluid. Overall, we generated tandem mass spectra corresponding to 13,028 unique human protein-coding genes. Although our analysis did not accomplish complete proteome coverage, it should be an important complementary resource for future biomarker discovery.

Genome Modularization Reveals Overlapped Gene Topology Is Necessary for Efficient Viral Reproduction.

Sequence overlap between two genes is common across all genomes, with viruses having high proportions of these gene overlaps. Genome modularization and refactoring is the process of disrupting natural gene overlaps to separate coding sequences to enable their individual manipulation. The biological function and fitness effects of gene overlaps are not fully understood, and their effects on gene cluster and genome-level refactoring are unknown. The bacteriophage φX174 genome has ∼26% of nucleotides involved in encoding more than one gene. In this study we use an engineered φX174 phage containing a genome with all gene overlaps removed to show that gene overlap is critical to maintaining optimal viral fecundity. Through detailed phenotypic measurements we reveal that genome modularization in φX174 causes virion replication, stability, and attachment deficiencies. Quantitation of the complete phage proteome across an infection cycle reveals 30% of proteins display abnormal expression patterns. Taken together, we have for the first time comprehensively demonstrated that gene modularization severely perturbs the coordinated functioning of a bacteriophage replication cycle. This work highlights the biological importance of gene overlap in natural genomes and that reducing gene overlap disruption should be an integral part of future genome engineering projects.

Immuno-Informatics Quest against COVID-19/SARS-COV-2: Determining Putative T-Cell Epitopes for Vaccine Prediction.

Since December 2019, a novel coronavirus, SARS-CoV-2, has caused global public health issues after being reported for the first time in Wuhan province of China. So far, there have been approximately 14.8 million confirmed cases and 0.614 million deaths due to the SARS-CoV-2 infection globally, and still, numbers are increasing. Although the virus has caused a global public health concern, no effective treatment has been developed. One of the strategies to combat the COVID-19 disease caused by SARS-CoV-2 is the development of vaccines that can make humans immune to these infections. Considering this approach, in this study, an attempt has been made to design epitope-based vaccine for combatting COVID-19 disease by analyzing the complete proteome of the virus by using immuno-informatics tools. The protein sequence of the SARS-CoV-2 was retrieved and the individual proteins were checked for their allergic potential. Then, from non-allergen proteins, antigenic epitopes were identified that could bind with MHCII molecules. The epitopes were modeled and docked to predict the interaction with MHCII molecules. The stability of the epitope-MHCII complex was further analyzed by performing a molecular dynamics simulation study. The selected vaccine candidates were also analyzed for their global population coverage and conservancy among SARS-related coronavirus species. The study has predicted 5 peptide molecules that can act as potential candidates for epitope- based vaccine development. Among the 5 selected epitopes, the peptide LRARSVSPK can be the most potent epitope because of its high geometric shape complementarity score, low ACE and very high response towards it by the world population (81.81% global population coverage). Further, molecular dynamic simulation analysis indicated the formation of a stable epitope-MHCII complex. The epitope LRARSVSPK was also found to be highly conserved among the SARS-CoV- -2 isolated from different countries. The study has predicted T-cell epitopes that can elicit a robust immune response in the global human population and act as potential vaccine candidates. However, the ability of these epitopes to act as vaccine candidate needs to be validated in wet lab studies.

Modeling Novel Putative Drugs and Vaccine Candidates against Tick-Borne Pathogens: A Subtractive Proteomics Approach.

Ticks and tick-borne pathogens (TBPs) continuously causing substantial losses to the public and veterinary health sectors. The identification of putative drug targets and vaccine candidates is crucial to control TBPs. No information has been recorded on designing novel drug targets and vaccine candidates based on proteins. Subtractive proteomics is an in silico approach that utilizes extensive screening for the identification of novel drug targets or vaccine candidates based on the determination of potential target proteins available in a pathogen proteome that may be used effectively to control diseases caused by these infectious agents. The present study aimed to investigate novel drug targets and vaccine candidates by utilizing subtractive proteomics to scan the available proteomes of TBPs and predict essential and non-host homologous proteins required for the survival of these diseases causing agents. Subtractive proteome analysis revealed a list of fifteen essential, non-host homologous, and unique metabolic proteins in the complete proteome of selected pathogens. Among these therapeutic target proteins, three were excluded due to the presence in host gut metagenome, eleven were found to be highly potential drug targets, while only one was found as a potential vaccine candidate against TBPs. The present study may provide a foundation to design potential drug targets and vaccine candidates for the effective control of infections caused by TBPs.

Link Your Sites (LYS) Scripts: Automated Search of Protein Structures and Mapping of Sites Under Positive Selection Detected by PAML

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and the understanding of its evolutionary implications. Currently, searches for fast evolving amino acid positions using codon substitution models like those implemented in PAML (Yang and Nielsen in Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17(1):32–43, 2000; Zhang et al. in Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22(12):2472–2479, 2005) are done in almost complete proteomes, generating large numbers of candidate proteins making the analysis of individual protein structures and models very time-consuming. Here we present the package Link Your Sites (LYS) that can be used to reduce the number of analysed targets to those for which structural information can be retrieved. LYS consists of two python wrapper scripts, where the first one (i) mines the RCSB database (Berman et al. in The protein data bank. Nucleic Acids Res 28(1):235–242, 2000) using the BLAST alignment tool to find the best matching homologous sequences, (ii) fetches their domain positions by using Prosites (Hamelryck and Manderick in Pdb file parser and structure class implemented in python. Bioinformatics 19(17):2308–2310, 2003; Sigrist et al. in Prosite: a documented database using patterns and profiles as motif descriptors. Brief Bioinf 3(3):265–274, 2002; Sigrist et al. in New and continuing developments at prosite. Nucleic Acids Res 41(D1):D344–D347, 2012), (iii) parses the output of PAML extracting the positional information of fast-evolving sites and transforms them into the coordinate system of the protein structure, (iv) outputs one file per gene with the equivalence among the positions in the input sequence and homologous structure. The second script produces figures to be used in publications highlighting the positively selected sites mapped on regions that are known to have functional relevance.

Binding affinities of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide.

We report detailed peptide‐binding affinities between 438 HLA Class I and Class II proteins and complete proteomes of seven pandemic human viruses, including coronaviruses, influenza viruses and HIV‐1. We contrast these affinities with HLA allele frequencies across hundreds of human populations worldwide. Statistical modelling shows that peptide‐binding affinities classified into four distinct categories depend on the HLA locus but that the type of virus is only a weak predictor, except in the case of HIV‐1. Among the strong HLA binders (IC50 ≤ 50), we uncovered 16 alleles (the top ones being A*02:02, B*15:03 and DRB1*01:02) binding more than 1% of peptides derived from all viruses, 9 (top ones including HLA‐A*68:01, B*15:25, C*03:02 and DRB1*07:01) binding all viruses except HIV‐1, and 15 (top ones A*02:01 and C*14:02) only binding coronaviruses. The frequencies of strongest and weakest HLA peptide binders differ significantly among populations from different geographic regions. In particular, Indigenous peoples of America show both higher frequencies of strongest and lower frequencies of weakest HLA binders. As many HLA proteins are found to be strong binders of peptides derived from distinct viral families, and are hence promiscuous (or generalist), we discuss this result in relation to possible signatures of natural selection on HLA promiscuous alleles due to past pathogenic infections. Our findings are highly relevant for both evolutionary genetics and the development of vaccine therapies. However they should not lead to forget that individual resistance and vulnerability to diseases go beyond the sole HLA allelic affinity and depend on multiple, complex and often unknown biological, environmental and other variables.

Proteome analysis of secretions from human monocyte-derived macrophages post-exposure to biomaterials and the effect of secretions on cardiac fibroblast fibrotic character

The use of exogenous biomolecules (BM) for the purpose of repairing and regenerating damaged cardiac tissue can yield serious side effects if used for prolonged periods. As well, such strategies can be cost prohibitive depending on the regiment and period of time applied. Alternatively, autologous monocytes/monocyte-derived macrophages (MDM) can provide a viable path towards generating an endogenous source of stimulatory BM. Biomaterials are often considered as delivery vehicles to generate unique profiles of such BM in tissues or to deliver autologous cells, that can influence the nature of BM produced by the cells. MDM cultured on a degradable polar hydrophobic ionic (D-PHI) polyurethane has previously demonstrated a propensity to increase select anti-inflammatory cytokines, and therefore there is good rationale to further investigate a broader spectrum of the cells’ BM in order to provide a more complete proteomic analysis of human MDM secretions induced by D-PHI. Further, it is of interest to assess the potential of such BM to influence cells involved in the reparative state of vital tissues such as those that affect cardiac cell function. Hence, this current study examines the proteomic profile of MDM secretions using mass spectrometry for the first time, along with ELISA, following their culture on D-PHI, and compares them to two important reference materials, poly(lactic-co-glycolic acid) (PLGA) and tissue culture polystyrene (TCPS). Secretions collected from D-PHI cultured MDM led to higher levels of regenerative BM, AGRN, TGFBI and ANXA5, but lower levels of pro-fibrotic BM, MMP7, IL-1β, IL-6 and TNFα, when compared to MDM secretions collected from PLGA and TCPS. In the application to cardiac cell function, the secretion collected from D-PHI cultured MDM led to more human cardiac fibroblast (HCFs) migration. A lower collagen gel contraction induced by MDM secretions collected from D-PHI was supported by gene array analysis for human fibrosis-related genes. The implication of these findings is that more tailored biomaterials such as D-PHI, may lead to a lower pro-inflammatory phenotype of macrophages when used in cardiac tissue constructs, thereby enabling the development of vehicles for the delivery of interventional therapies, or be applied as coatings for sensor implants in cardiac tissue that minimize fibrosis. The general approach of using synthetic biomaterials in order to induce MDM secretions in a manner that will guide favorable regeneration will be critical in making the choice of biomaterials for tissue regeneration work in the future. Statement of significanceImmune modulation strategies currently applied in cardiac tissue repair are mainly based on the delivery of defined exogenous biomolecules. However, the use of such biomolecules may pose wide ranging systemic effects, thereby rendering them clinically less practical. The chemistry of biomaterials (used as a potential targeted delivery modality to circumvent the broad systemic effects of biomolecules) can not only affect acute and chronic toxicity but also alters the timeframe of the wound healing cascade. In this context, monocytes/monocyte-derive macrophages (MDM) can be harnessed as an immune modulating strategy to promote wound healing by an appropriate choice of the biomaterial. However, there are limited reports on the complete proteome analysis of MDM and their reaction of biomaterial related interventions on cardiac tissues and cells. No studies to date have demonstrated the complete proteome of MDM secretions when these cells were cultured on a non-traditional immune modulatory ionomeric polyurethane D-PHI film. This study demonstrated that MDM cultured on D-PHI expressed significantly higher levels of AGRN, TGFBI and ANXA5 but lower levels of MMP7, IL-1β, IL-6 and TNFα when compared to MDM cultured on a well-established degradable biomaterials in the medical field, e.g. PLGA and TCPS, which are often used as the relative standards for cell culture work in the biomaterials field. The implications of these findings have relevance to the repair of cardiac tissues. In another aspect of the work, human cardiac fibroblasts showed significantly lower contractility (low collagen gel contraction and low levels of ACTA2) when cultured in the presence of MDM secretions collected after culturing them on D-PHI compared to PLGA and TCPS. The findings place emphasis on the importance of making the choice of biomaterials for tissue engineering and regenerative medicine applied to their use in cardiac tissue repair.

Genome Subtraction and Comparison for the Identification of Novel Drug Targets against Mycobacterium avium subsp. hominissuis.

Mycobacterium avium complex (MAC) is a major cause of non-tuberculous pulmonary and disseminated diseases worldwide, inducing bronchiectasis, and affects HIV and immunocompromised patients. In MAC, Mycobacterium avium subsp. hominissuis is a pathogen that infects humans and mammals, and that is why it is a focus of this study. It is crucial to find essential drug targets to eradicate the infections caused by these virulent microorganisms. The application of bioinformatics and proteomics has made a significant impact on discovering unique drug targets against the deadly pathogens. One successful bioinformatics methodology is the use of in silico subtractive genomics. In this study, the aim was to identify the unique, non-host and essential protein-based drug targets of Mycobacterium avium subsp. hominissuis via in silico a subtractive genomics approach. Therefore, an in silico subtractive genomics approach was applied in which complete proteome is subtracted systematically to shortlist potential drug targets. For this, the complete dataset of proteins of Mycobacterium avium subsp. hominissuis was retrieved. The applied subtractive genomics method, which involves the homology search between the host and the pathogen to subtract the non-druggable proteins, resulted in the identification of a few prioritized potential drug targets against the three strains of M. avium subsp. Hominissuis, i.e., MAH-TH135, OCU466 and A5. In conclusion, the current study resulted in the prioritization of vital drug targets, which opens future avenues to perform structural as well as biochemical studies on predicted drug targets against M. avium subsp. hominissuis.

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains.

In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.

Frameshifting preserves key physicochemical properties of proteins

Frameshifts in protein coding sequences are widely perceived as resulting in either nonfunctional or even deleterious protein products. Indeed, frameshifts typically lead to markedly altered protein sequences and premature stop codons. By analyzing complete proteomes from all three domains of life, we demonstrate that, in contrast, several key physicochemical properties of protein sequences exhibit significant robustness against +1 and -1 frameshifts. In particular, we show that hydrophobicity profiles of many protein sequences remain largely invariant upon frameshifting. For example, over 2,900 human proteins exhibit a Pearson's correlation coefficient R between the hydrophobicity profiles of the original and the +1-frameshifted variants greater than 0.7, despite an average sequence identity between the two of only 6.5% in this group. We observe a similar effect for protein sequence profiles of affinity for certain nucleobases as well as protein sequence profiles of intrinsic disorder. Finally, analysis of significance and optimality demonstrates that frameshift stability is embedded in the structure of the universal genetic code and may have contributed to shaping it. Our results suggest that frameshifting may be a powerful evolutionary mechanism for creating new proteins with vastly different sequences, yet similar physicochemical properties to the proteins from which they originate.