The human genome displays a rich fossil record of past gammaretrovirus infections, yet no current epidemic is evident, despite environmental exposure to viruses that infect human cells in vitro Feline leukemia viruses (FeLVs) rank high on this list, but neither domestic nor workplace exposure has been associated with detectable serological responses. Nonspecific inactivation of gammaretroviruses by serum factors appears insufficient to explain these observations. To investigate further, we explored the susceptibilities of primary and established human cell lines to FeLV-B, the most likely zoonotic variant. Fully permissive infection was common in cancer-derived cell lines but was also a feature of nontransformed keratinocytes and lung fibroblasts. Cells of hematopoietic origin were generally less permissive and formed discrete groups on the basis of high or low intracellular protein expression and virion release. Potent repression was observed in primary human blood mononuclear cells and a subset of leukemia cell lines. However, the early steps of reverse transcription and integration appear to be unimpaired in nonpermissive cells. FeLV-B was subject to G→A hypermutation with a predominant APOBEC3G signature in partially permissive cells but was not mutated in permissive cells or in nonpermissive cells that block secondary viral spread. Distinct cellular barriers that protect primary human blood cells are likely to be important in protection against zoonotic infection with FeLV.IMPORTANCE Domestic exposure to gammaretroviruses such as feline leukemia viruses (FeLVs) occurs worldwide, but the basis of human resistance to infection remains incompletely understood. The potential threat is evident from the human genome sequence, which reveals many past epidemics of gammaretrovirus infection, and from recent cross-species jumps of gammaretroviruses from rodents to primates and marsupials. This study examined resistance to infection at the cellular level with the most prevalent human cell-tropic FeLV variant, FeLV-B. We found that blood cells are uniquely resistant to infection with FeLV-B due to the activity of cellular enzymes that mutate the viral genome. A second block, which appears to suppress viral gene expression after the viral genome has integrated into the host cell genome, was identified. Since cells derived from other normal human cell types are fully supportive of FeLV replication, innate resistance of blood cells could be critical in protecting against cross-species infection.

We previously demonstrated that in addition to generating an antigen-specific immune response, Listeria monocytogenes (Lm)-based immunotherapy significantly reduces the ratio of regulatory T cells (Tregs)/CD4+ and myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment. Since Lm-based immunotherapy is able to inhibit the immune suppressive environment, we hypothesized that combining this treatment with agonist antibody to a co-stimulatory receptor that would further boost the effector arm of immunity will result in significant improvement of anti-tumor efficacy of treatment.

The study was conducted between 2000 and 2003 on 750 human subjects, yielding 850 strains of staphylococci from clinical specimens (575), nasal cultures of hospitalized patients (100) and eye & nasal sources of hospital workers (50 & 125 respectively) in order to determine their epidemiology, acquisition and dissemination of resistance genes.

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.

Feline leukemia virus (FeLV) is a naturally transmitted gammaretrovirus that infects domestic cats. FeLV-945, the predominant isolate associated with non-T-cell disease in a natural cohort, is a member of FeLV subgroup A but differs in sequence from the FeLV-A prototype, FeLV-A/61E, in the surface glycoprotein (SU) and long terminal repeat (LTR). Substitution of the FeLV-945 LTR into FeLV-A/61E resulted in pathogenesis indistinguishable from that of FeLV-A/61E, namely, thymic lymphoma of T-cell origin. In contrast, substitution of both FeLV-945 LTR and SU into FeLV-A/61E resulted in multicentric lymphoma of non-T-cell origin. These results implicated the FeLV-945 SU as a determinant of pathogenic spectrum. The present study was undertaken to test the hypothesis that FeLV-945 SU can act in the absence of other unique sequence elements of FeLV-945 to determine the disease spectrum. Substitution of FeLV-A/61E SU with that of FeLV-945 altered the clinical presentation and resulted in tumors that demonstrated expression of CD45R in the presence or absence of CD3. Despite the evident expression of CD45R, a typical B-cell marker, T-cell receptor beta (TCRβ) gene rearrangement indicated a T-cell origin. Tumor cells were detectable in bone marrow and blood at earlier times during the disease process, and the predominant SU genes from proviruses integrated in tumor DNA carried markers of genetic recombination. The findings demonstrate that FeLV-945 SU alters pathogenesis, although incompletely, in the absence of FeLV-945 LTR. Evidence demonstrates that FeLV-945 SU and LTR are required together to fully recapitulate the distinctive non-T-cell disease outcome seen in the natural cohort.

Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named 'iMul-kSite' for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that 'iMul-kSite' can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, 'iMul-kSite' has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .

Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.