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Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.

The current standard of inflammatory bowel disease (IBD), especially, Crohn’s disease (CD), diagnosis is set through an invasive endoscopy procedure. However, serum metabolites hold potential as useful biomarkers for non-invasive diagnosis and treatment of IBDs. The goal of this research was to elucidate the biomarkers including metabolites and genes related to IBDs, to show their distinguishing and common features, and to create a machine-learning (ML) model for recognition of each disease. We explored metabolic pathways and gene–metabolite networks related to unspecified-IBD (uIBD), Crohn’s diseaseand ulcerative colitis (UC). P38 MAPK, ERK1/2, AMPK, and proinsulin were found to be closely related to the pathology of IBDs. The best performing ML model, trained on filtered disease-specific metabolite datasets, was able to predict metabolite class with 92.17% accuracy. Through examination of IBD-related serum, significant relationships between the inputted metabolites and certain metabolic and signaling pathways were found, which can be pinpointed and used to increase accuracy of disease diagnoses. Development of a ML model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of the IBDs. Our results elucidate a large variety of metabolites, genes, and pathways that could be used for better understanding of IBDs’ molecular mechanisms.

Objective The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine-learning model for elucidation of these diseases based on saliva metabolites of patients. Methods Data mining, metabolomic pathways analysis, study of metabolite-gene networks related to these diseases. Machine-learning and deep-learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva. Results The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10-fold cross-validations. The best results were achieved by the deep-learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy. Conclusion Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine-learning methods. These findings may be useful in the development of a non-invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.

Gastric cancer is one of the most prevalent and deadly cancers in the world. One of the biggest factors for this disease is Helicobacter pylori (H. pylori), infecting roughly half of the world’s population. However, there is a limited understanding of the H. pylori infection at single-cell level. In this study, single-cell RNA-Seq datasets from intestinal metaplasia samples were analyzed. Using bioinformatics methods, the cells were clustered and cell types were identified with cell type specific marker genes. For each cell type, H. pylori infected cells were compared with control cells using statistical analysis in order to find significant genes and pathways. Then, machine-learning (ML) approaches were used to build models to distinguish H. pylori positive and negative cells, and the severity of infection. It is found that H. pylori infection is linked to an increase in enterocytes and a decrease in pit mucous cells (PMCs). These changes may promote disease progression from gastritis to gastric cancer. Significantly differentially expressed genes and several pathways such as the MHC class II antigen presentation pathway and the PD-1 pathway were identified. The random forest-based models achieved an accuracy of higher than 97% for detecting positivity and severity. We identified the specific type of the host cells along with signaling pathways related to H. pylori infection and signaling pathways leading to gastric cancer. We demonstrated that ML methods are useful in detection of the affected by H. pylori PMC cells.

Bcl2-associated athanogene 3 (BAG3) is a multifunctional cochaperone responsible for protein quality control within cells. BAG3 interacts with chaperones HSPB8 and Hsp70 to transport misfolded proteins to the Microtubule Organizing Center (MTOC) and degrade them in autophagosomes in a process known as Chaperone Assisted Selective Autophagy (CASA). Mutations in the second conserved IPV motif of BAG3 are known to cause Dilated Cardiomyopathy (DCM) by inhibiting adequate removal of non-native proteins. The proline 209 to leucine (P209L) BAG3 mutant in particular causes the aggregation of BAG3 and misfolded proteins as well as the sequestration of essential chaperones. The exact mechanisms of protein aggregation in DCM are unknown. However, the similar presence of insoluble protein aggregates in Charcot-Marie-Tooth disease type 2 (CMT2) induced by the proline 182 to leucine (P182L) HSPB1 mutant points to a possible avenue for future research: IPV motif. In this review, we summarize the molecular mechanisms of CASA and the currently known pathological effects of mutated BAG3 in DCM. Additionally, we will provide insight on the importance of the IPV motif in protein aggregation by analyzing a potential association between DCM and CMT2.

In COVID-19, patients in severe condition often suffer from a major complication that leads to lung injury, ARDS and possibly death: the cytokine storm. The cytokine storm is composed of many cytokines, including IL-6, IL2, and TNF-? for COVID-19. To combat such effects, a cocktail of cytokine-inhibiting drugs are administered. However, a combination of drugs can be overly taxing upon the patient, thus creating the demand for a drug that targets multiple cytokines. This project identifies multi-cytokine inhibiting compounds from FDA-approved drugs with machine-learning methods. Many machine-learning algorithms were applied to the task and Support Vector Machines proved best with strong performances across all cytokines. Under the constraints of limited data (30–60 samples) for some cytokines, we significantly boosted modeling power and accuracy with the application of data dimension reduction technique, Principle Component Analysis. After exhaustive exploration, the FDAapproved hepatitis-C drug—glecaprevir—was identified with confidences of 80.52% for TNF-?, 99.04% for IL-2, and 98.23% for IL-6.

Background: The current standard for Alzheimer’s disease (AD) diagnosis is often imprecise, as with memory tests, and invasive or expensive, as with brain scans. However, the dysregulation patterns of miRNA in blood hold potential as useful biomarkers for the non-invasive diagnosis and even treatment of AD. Objective: The goal of this research is to elucidate new miRNA biomarkers and create a machine-learning (ML) model for the diagnosis of AD. Methods: We utilized pathways and target gene networks related to confirmed miRNA biomarkers in AD diagnosis and created multiple models to use for diagnostics based on the significant differences among miRNA expression between blood profiles (serum and plasma). Results: The best performing serum-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 92.0% accuracy and the best performing plasma-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 90.9% accuracy. Through analysis of AD implicated miRNA, thousands of descriptors reliant on target gene and pathways were created which can then be used to identify novel biomarkers and strengthen disease diagnosis. Conclusion: Development of a ML model including miRNA and their genomic and pathway descriptors made it possible to achieve considerable accuracy for the prediction of AD.

Successful control of the COVID-19 pandemic depends on vaccines that prevent transmission. The full-length Spike protein is highly immunogenic but the majority of antibodies do not target the virus: ACE2 interface. In an effort to concentrate the antibody response to the receptor-binding motif (RBM) we generated a series of conformationally-constrained immunogens by inserting solvent-exposed RBM amino acid residues into hypervariable loops of an immunoglobulin molecule. Priming C57BL/6 mice with plasmid (p)DNA encoding these constructs yielded a rapid memory response to booster immunization with recombinant Spike protein. Immune sera antibodies bound strongly to the purified receptor-binding domain (RBD) and Spike proteins. pDNA primed for a consistent response with antibodies efficient at neutralizing authentic WA1 virus and two variants of concern (VOC), B.1.351 and B.1.617.2. These findings demonstrate that immunogens built on structure selection can focus the response to conserved sites of vulnerability shared between wildtype virus and VOCs and induce neutralizing antibodies across variants.

Members of a large family of Ankyrin Repeat Domain (ANKRD) proteins regulate numerous cellular processes by binding to specific protein targets and modulating their activity, stability, and other properties. The same ANKRD protein may interact with different targets and regulate distinct cellular pathways. The mechanisms responsible for switches in the ANKRDs' behavior are often unknown. We show that cells' metabolic state can markedly alter interactions of an ANKRD protein with its target and the functional outcomes of this interaction. ANKRD9 facilitates degradation of inosine monophosphate dehydrogenase 2 (IMPDH2), the rate-limiting enzyme in GTP biosynthesis. Under basal conditions ANKRD9 is largely segregated from the cytosolic IMPDH2 in vesicle-like structures. Upon nutrient limitation, ANKRD9 loses its vesicular pattern and assembles with IMPDH2 into rodlike filaments, in which IMPDH2 is stable. Inhibition of IMPDH2 activity with ribavirin favors ANKRD9 binding to IMPDH2 rods. The formation of ANKRD9/IMPDH2 rods is reversed by guanosine, which restores ANKRD9 associations with the vesicle-like structures. The conserved Cys(109)Cys(110) motif in ANKRD9 is required for the vesicle-to-rods transition as well as binding and regulation of IMPDH2. Oppositely to overexpression, ANKRD9 knockdown increases IMPDH2 levels and prevents formation of IMPDH2 rods upon nutrient limitation. Taken together, the results suggest that a guanosine-dependent metabolic switch determines the mode of ANKRD9 action toward IMPDH2.

Numerous molecular processes conduct epigenetic regulation of protein transcription to maintain cell specification. In this review, we discuss molecular mechanisms of the Polycomb group of proteins and its enzymatic role in epigenetics. More specifically, we focus on the Polycomb repressive complex 2 (PRC2) and the effects of its repressive marker. We have compiled information regarding the biological structure and how that impacts the stability of the complex. In addition, we examined functions of the individual core proteins of PRC2 in relation to the accessory proteins that interact with the complex. Lastly, we discuss the implications of unregulated and downregulated PRC2 activity in Alzheimer's disease and cancer and possible methods of treatment related to PRC2 regulation.

P38-alpha (MAPK14) is a protein kinase that is implicated in the pathological mechanisms of BAG3 P209L myofibrillar myopathy, cancers, Alzheimer’s disease and other diseases like rheumatoid arthritis. Inhibition of p38 has shown promise as treatment for these diseases. Traditional drug discovery methods were unable to create both effective and safe small molecule inhibitors, so we used machine learning to elucidate potential p38 blockers from existing FDA-approved drugs. Using available bioactivity data, we determined the best existing p38 inhibitors and applied fingerprint clustering to isolate the compounds with similar structures. Descriptors were calculated for these clustered compounds and the most important of these descriptors were determined through a machine-learning based feature selection algorithm. This data served as the training set for a deep neural network that was fine-tuned to a 92% validation accuracy. The neural network model was applied to a database of FDA-approved drugs, revealing 149 potential p38 inhibitors, whose efficacy were confirmed by docking simulations to be statistically significantly higher than random FDA drugs and slightly higher than known inhibitors. Our study not only reveals potential treatments for p38-mediated diseases but also demonstrates the capability of integrating various machine-learning techniques and computational algorithms to predict novel functions of existing pharmaceuticals.

Mutant superoxide dismutase 1 (SOD1) may form cyclic structures due to its greater instability from aberrant demetallization and oxidation of cysteine bonds. This cyclic structure may allow SOD1 to form ion channels on membranes such as the mitochondrial membrane, causing imbalances in the concentration of intracellular ions as a potential mechanism for the progressive neuron death involved in amyotrophic lateral sclerosis (ALS). Using docking programs within modeling software, models of mutant SOD1 dimers and eventually ring oligomers were constructed based on known descriptions of such structures in addition to information on the orientation of the models associated with a membrane. The resulting structure consists of a ring of four demetallated mutant SOD1 dimers with cross-linked disulfide bonds. Stability of the octamer model was supported by the molecular dynamics simulations. Further analysis of the octamer model indicated that its inner- and outer-pore diameters were stable, matching the dimensions of known SOD1 ion channels.

Authors: Valentina L. Kouznetsova, Caroline Kellogg, Aidan Zhang, Mahidhar Tatineni, Mark A. Miller, Igor F. Tsigelny Background: SARS-CoV-2 has caused tens of millions of infections worldwide and millions of deaths. Currently, no effective treatment has been identified against the virus. Of its viral proteins, the RNA-dependent RNA polymerase (RdRp) is a promising target for drug design because of its importance in the replication of the virus. Material and Methods: After the identification of an RdRp pocket site based on the crystal structure of the RdRp– nsp7–nsp8 complex and the triphosphate form of remdesivir (PDB ID: 7BV2), we created a pharmacophore model consisting of 11 different features. These features include two acceptors, three donors, one acceptor and donor, three donor or acceptor, and one hydrophobic; an excluded volume of R=1.1 Å was also added. We then ran a pharmacophore search on our conformational database (DB) of approximately 2500 FDA-approved drugs and 600 000 conformations to identify potential drug-candidates. To determine the drugs that bound the best, we conducted multi conformational docking of these results to the previously identified pocket site. Results: The pharmacophore search found 315 different potential inhibitors of RdRp, of which 85 were chosen based on the number of H-bonds and hydrophobic interactions in the best docking pose. Several of the drugs selected, including ritonavir, dasatinib, imatinib, and sofosbuvir, have previously been shown to be effective against other viruses. Conclusions: These findings highlight compounds that could lead to both in vitro and in vivo studies to identify potential treatments against SARS-CoV-2.

Polyomaviruses such as Simian Virus 40 (SV40) and John Cunningham Virus (JCV) have been extensively studied for their potential role in aiding oncogenic transformation. One of the mechanisms through which they do this is by inactivating p53, a known tumor suppressor, through one of their viral proteins, large T-antigen (LT). However, these two viruses represent only a fraction of existing polyomaviruses. Using Clustal Omega, we aligned the protein sequences of LT for 12 different polyomaviruses and found high similarity across polyomavirus LT. We then utilized Molecular Operating Environment (MOE) v2019.01 to compare the binding of SV40 LT to p53 and p53 to DNA to more precisely define the mechanism with which SV40 LT inactivates p53. By binding to p53 residues essential to DNA binding, SV40 LT prevents the proper interaction of p53 with DNA and consequently its fulfillment of transcription factor functions. To further explore the possibility for other polyomavirus LT to do the same, we either retrieved existing 3D structures from RCSB Protein Data Bank or generated 3D homology models of other polyomavirus LT and modeled their interactions with p53. These models interacted with p53 in a similar manner as SV40 LT and provide further evidence of the potential of other polyomavirus LT to inactivate p53. This work demonstrates the importance of investigating the oncogenic potential of polyomaviruses and elucidates future targets for cancer treatment.Communicated by Ramaswamy H. Sarma.

Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin-the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

Since the identification of the first human oncogenic virus in 1964, viruses have been studied for their potential role in aiding the development of cancer. Through the modulation of cellular pathways associated with proliferation, immortalization, and inflammation, viral proteins can mimic the effect of driver mutations and contribute to transformation. Aside from the modulation of signaling pathways, the insertion of viral DNA into the host genome and the deregulation of cellular miRNAs represent two additional mechanisms implicated in viral oncogenesis. In this review, we will discuss the role of twelve different viruses on cancer development and how these viruses utilize the abovementioned mechanisms to influence oncogenesis. The identification of specific mechanisms behind viral transformation of human cells could further elucidate the process behind cancer development.

Severe acute respiratory syndrome coronavirus (SARS-CoV) 2 infection has caused millions of deaths worldwide, pushing the urgent need for an efficient treatment. Nonstructural protein 15 (NSP15) is a promising target due to its importance for SARS-CoV-2’s evasion of the host’s innate immune response.

Background The mitochondrial protein p32 is a validated therapeutic target of cancer overexpressed in glioma. Therapeutic targeting of p32 with monoclonal antibody or p32-binding LyP-1 tumor-homing peptide can limit tumor growth. However, these agents do not specifically target mitochondrial-localized p32 and would not readily cross the blood–brain barrier to target p32-overexpressing gliomas. Identifying small molecule inhibitors of p32 overexpressed in cancer is a more rational therapeutic strategy. Thus, in this study we employed a pharmacophore modeling strategy to identify small molecules that could bind and inhibit mitochondrial p32. Methods A pharmacophore model of C1q and LyP-1 peptide association with p32 was used to screen a virtual compound library. A primary screening assay for inhibitors of p32 was developed to identify compounds that could rescue p32-dependent glutamine-addicted glioma cells from glutamine withdrawal. Inhibitors from this screen were analyzed for direct binding to p32 by fluorescence polarization assay and protein thermal shift. Affect of the p32 inhibitor on glioma cell proliferation was assessed by Alamar Blue assay, and affect on metabolism was examined by measuring lactate secretion. Results Identification of a hit compound (M36) validates the pharmacophore model. M36 binds directly to p32 and inhibits LyP-1 tumor homing peptide association with p32 in vitro. M36 effectively inhibits the growth of p32 overexpressing glioma cells, and sensitizes the cells to glucose depletion. Conclusions This study demonstrates a novel screening strategy to identify potential inhibitors of mitochondrial p32 protein overexpressed in glioma. High throughput screening employing this strategy has potential to identify highly selective, potent, brain-penetrant small molecules amenable for further drug development.

The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, multiple hypothesized binding modes, and small amounts of activity data. The main objective of this study was to demonstrate the efficacy of deep-learning (DL) techniques on small bioactivity datasets, and to identify candidate inhibitors of RAGE. We applied transfer learning in the form of a semi-supervised molecular representation in order to address small dataset problems. To validate the candidate inhibitors, we examined them using more computationally expensive pharmacophore-modeling and docking techniques. We created a strong classifier of RAGE activity, producing 79 candidate inhibitors. These candidates agreed with docking models and were shown to have no significant statistical difference from pharmacophore-based results. The transfer-learning techniques used allow DL to generalize chemical features from small bioactivity datasets to a broader library of compounds with high accuracy. Furthermore, the DL model is able to handle multiple binding modes without explicit instructions. Our results demonstrate the potential of a broad family of DL techniques on bioactivity predictions.

Oligodendrocyte lineage transcription factor 2 (OLIG2) plays a pivotal role in glioma development. Here we conducted a comprehensive study of the critical gene regulatory networks involving OLIG2. These include the networks responsible for OLIG2 expression, its translocation to nucleus, cell cycle, epigenetic regulation, and Rho-pathway interactions. We described positive feedback loops including OLIG2: loops of epigenetic regulation and loops involving receptor tyrosine kinases. These loops may be responsible for the prolonged oncogenic activity of OLIG2. The proposed schemes for epigenetic regulation of the gene networks involving OLIG2 are confirmed by patient survival (Kaplan–Meier) curves based on the cancer genome atlas (TCGA) datasets. Finally, we elucidate the Coherent-Gene Modules (CGMs) networks—framework of OLIG2 involvement in cancer. We showed that genes interacting with OLIG2 formed eight CGMs having a set of intermodular connections. We showed also that among the genes involved in these modules the most connected hub is EGFR, then, on lower level, HSP90 and CALM1, followed by three lower levels including epigenetic genes KDM1A and NCOR1. The genes on the six upper levels of the hierarchy are involved in interconnections of all eight CGMs and organize functionally defined gene-signaling subnetworks having specific functions. For example, CGM1 is involved in epigenetic control. CGM2 is significantly related to cell proliferation and differentiation. CGM3 includes a number of interconnected helix–loop–helix transcription factors (bHLH) including OLIG2. Many of these TFs are partially controlled by OLIG2. The CGM4 is involved in PDGF-related: angiogenesis, tumor cell proliferation and differentiation. These analyses provide testable hypotheses and approaches to inhibit OLIG2 pathway and relevant feed-forward and feedback loops to be interrogated. This broad approach can be applied to other TFs.

Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

Feline cancers have not been studied as extensively as canine cancers, though they may offer similar advantages, with cats being immunocompetent animals subject to similar conditions as their human counterparts. The most common feline cancers include lymphoma, squamous cell carcinoma, sarcoma, and mammary tumors, though mast cell tumors were also investigated in this review. As the pathogenesis of many feline cancers remains unclear, this study seeks to elucidate some molecular mechanisms behind feline cancers. Feline lymphoma has been commonly associated with feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV), though in recent years it has appeared more as lymphoma of the gastrointestinal tract. Chromosomal alterations (translocations) due to the virus-associated lymphoma, as well as aberrant gene expression (such as in COX-2 and MDR1) have been identified in the past. While feline lymphoma may be divided into many subtypes, feline sarcoma may be divided into two broad classifications of feline injection site associated (FISS) sarcoma and spontaneous sarcoma, with FISS being a potential model for inflammation leading to tumorigenesis in humans. Previous studies have found multiple chromosomal alterations (including aneuploidy and chromosomal translocations), as well as aberrations in gene expression in feline sarcoma. In the past, oral squamous cell carcinoma has been proposed as a model for human head and neck cancer, and mammary tumors have been proposed as a model for human breast cancers due to similar prognosis and phenotype, as well as higher rate of occurrence in cats than in humans. Mutations have been identified in genes such as TP53, ERBB2, and TWIST1 in feline mammary tumors. c-KIT mutations were commonly located in feline mast cell tumors, though these findings were not particularly significant in terms of correlation to other prognostic indicators. This review seeks to elucidate pathways and treatments for feline cancers for the field of comparative genomics and oncology.

Bovine leukemia virus (BLV) is a virus that infects cattle around the world and is very similar to the human T-cell leukemia virus (HTLV), which causes adult T-cell leukemia/lymphoma (ATL). Recently, presence of BLV DNA and protein was demonstrated in commercial bovine products and in humans. BLV DNA is generally found at higher rates in humans who have or will develop breast cancer, according to research done with subjects from several countries. These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis in humans. Here we summarize the current knowledge in the field., Highlights • Bovine leukemia virus (BLV) DNA is found at higher rates in humans who have or will develop breast cancer. • Global analysis of breast cancer rates links the frequency of this cancer cases to consumption of milk and beef in the countries studied. • These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis in humans. • There are contradicting results to this hypothesis that in majority of cases can be explained by different experimental methods used.

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function. The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa. Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity® Pathway Analysis (IPA®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function. We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set. By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

// Igor F. Tsigelny 1,2,3,* , Rajesh Mukthavaram 3,* , Valentina L. Kouznetsova 2,3,* , Ying Chao 3 , Ivan Babic 3 , Elmar Nurmemmedov 4 , Sandra Pastorino 3 , Pengfei Jiang 3 , David Calligaris 5 , Nathalie Agar 5 , Miriam Scadeng 6 , Sandeep C. Pingle 3 , Wolfgang Wrasidlo 1,3 , Milan T. Makale 3 and Santosh Kesari 1,3,7 1 Department of Neurosciences, University of California San Diego, La Jolla, CA, USA 2 San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA 3 Translational Neuro-oncology Laboratories, Moores Cancer Center, University of California San Diego, La Jolla, CA, USA 4 The Scripps Research Institute, La Jolla, CA, USA 5 Harvard Medical School, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA, USA 6 FMRI Research Center, Department of Radiology, University of California San Diego, La Jolla, CA, USA 7 Current Address: John Wayne Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA * These authors have contributed equally to this work Correspondence to: Igor F. Tsigelny, email: // Santosh Kesari, email: // Keywords : in silico rational drug design, pharmacophore, inhibitor scaffold, transcription factors, OLIG2 Received : September 01, 2015 Accepted : October 14, 2015 Published : October 30, 2015 Abstract Transcription factors (TFs) are a major class of protein signaling molecules that play key cellular roles in cancers such as the highly lethal brain cancer—glioblastoma (GBM). However, the development of specific TF inhibitors has proved difficult owing to expansive protein-protein interfaces and the absence of hydrophobic pockets. We uniquely defined the dimerization surface as an expansive parental pharmacophore comprised of several regional daughter pharmacophores. We targeted the OLIG2 TF which is essential for GBM survival and growth, we hypothesized that small molecules able to fit each subpharmacophore would inhibit OLIG2 activation. The most active compound was OLIG2 selective, it entered the brain, and it exhibited potent anti-GBM activity in cell-based assays and in pre-clinical mouse orthotopic models. These data suggest that (1) our multiple pharmacophore approach warrants further investigation, and (2) our most potent compounds merit detailed pharmacodynamic, biophysical, and mechanistic characterization for potential preclinical development as GBM therapeutics.