Quantitative Proteomics Reveal That CB2R Agonist JWH-133 Downregulates NF-κB Activation, Oxidative Stress, and Lysosomal Exocytosis from HIV-Infected Macrophages.

HIV-associated neurocognitive disorders (HAND) affect 15-55% of HIV-positive patients and effective therapies are unavailable. HIV-infected monocyte-derived macrophages (MDM) invade the brain of these individuals, promoting neurotoxicity. We demonstrated an increased expression of cathepsin B (CATB), a lysosomal protease, in monocytes and post-mortem brain tissues of women with HAND. Increased CATB release from HIV-infected MDM leads to neurotoxicity, and their secretion is associated with NF-κB activation, oxidative stress, and lysosomal exocytosis. Cannabinoid receptor 2 (CB2R) agonist, JWH-133, decreases HIV-1 replication, CATB secretion, and neurotoxicity from HIV-infected MDM, but the mechanisms are not entirely understood. We hypothesized that HIV-1 infection upregulates the expression of proteins associated with oxidative stress and that a CB2R agonist could reverse these effects. MDM were isolated from healthy women donors (n = 3), infected with HIV-1ADA, and treated with JWH-133. After 13 days post-infection, cell lysates were labeled by Tandem Mass Tag (TMT) and analyzed by LC/MS/MS quantitative proteomics bioinformatics. While HIV-1 infection upregulated CATB, NF-κB signaling, Nrf2-mediated oxidative stress response, and lysosomal exocytosis, JWH-133 treatment downregulated the expression of the proteins involved in these pathways. Our results suggest that JWH-133 is a potential alternative therapy against HIV-induced neurotoxicity and warrant in vivo studies to test its potential against HAND.

Plasma Proteins Associated with COVID-19 Severity in Puerto Rico.

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.

Inhibition of Cathepsin B and SAPC Secreted by HIV-Infected Macrophages Reverses Common and Unique Apoptosis Pathways.

Human immunodeficiency virus 1 (HIV-1) infects blood monocytes that cross the blood-brain barrier to the central nervous system, inducing neuronal damage. This is prompted by the secretion of viral and neurotoxic factors by HIV-infected macrophages, resulting in HIV-associated neurocognitive disorders. One of these neurotoxic factors is cathepsin B (CATB), a lysosomal cysteine protease that plays an important role in neurodegeneration. CATB interacts with the serum amyloid P component (SAPC), contributing to HIV-induced neurotoxicity. However, the neuronal apoptosis pathways triggered by CATB and the SAPC remain unknown. We aimed to elucidate these pathways in neurons exposed to HIV-infected macrophage-conditioned media before and after the inhibition of CATB or the SAPC with antibodies using tandem mass tag proteomics labeling. Based on the significant fold change (FC) ≥ |2| and p-value < 0.05 criteria, a total of 10, 48, and 13 proteins were deregulated after inhibiting CATB, SAPC antibodies, and the CATB inhibitor CA-074, respectively. We found that neurons exposed to the CATB antibody and SAPC antibody modulate similar proteins (TUBA1A and CYPA/PPIA) and unique proteins (LMNA and HSPH1 for the CATB antibody) or (CFL1 and PFN1 for the SAPC antibody). CATB, SAPC, or apoptosis-related proteins could become potential targets against HIV-induced neuronal degeneration.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. METHODS: The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). RESULTS: The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. CONCLUSION:  The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Non-Random Enrichment of Single-Nucleotide Polymorphisms Associated with Clopidogrel Resistance within Risk Loci Linked to the Severity of Underlying Cardiovascular Diseases: The Role of Admixture.

Cardiovascular disease (CVD) is one of the leading causes of death in Puerto Rico, where clopidogrel is commonly prescribed to prevent ischemic events. Genetic contributors to both a poor clopidogrel response and the severity of CVD have been identified mainly in Europeans. However, the non-random enrichment of single-nucleotide polymorphisms (SNPs) associated with clopidogrel resistance within risk loci linked to underlying CVDs, and the role of admixture, have yet to be tested. This study aimed to assess the possible interaction between genetic biomarkers linked to CVDs and those associated with clopidogrel resistance among admixed Caribbean Hispanics. We identified 50 SNPs significantly associated with CVDs in previous genome-wide association studies (GWASs). These SNPs were combined with another ten SNPs related to clopidogrel resistance in Caribbean Hispanics. We developed Python scripts to determine whether SNPs related to CVDs are in close proximity to those associated with the clopidogrel response. The average and individual local ancestry (LAI) within each locus were inferred, and 60 random SNPs with their corresponding LAIs were generated for enrichment estimation purposes. Our results showed no CVD-linked SNPs in close proximity to those associated with the clopidogrel response among Caribbean Hispanics. Consequently, no genetic loci with a dual predictive role for the risk of CVD severity and clopidogrel resistance were found in this population. Native American ancestry was the most enriched within the risk loci linked to CVDs in this population. The non-random enrichment of disease susceptibility loci with drug-response SNPs is a new frontier in Precision Medicine that needs further attention.

Zika virus infection of the placenta alters extracellular matrix proteome.

Zika virus (ZIKV) infection has been associated with fetal abnormalities by compromising placental integrity, but the mechanisms by which this occurs are unknown. Flavivirus can deregulate the host proteome, especially extracellular matrix (ECM) proteins. We hypothesize that a deregulation of specific ECM proteins by ZIKV, affects placental integrity. Using twelve different placental samples collected during the 2016 ZIKV Puerto Rico epidemic, we compared the proteome of five ZIKV infected samples with four uninfected controls followed by validation of most significant proteins by immunohistochemistry. Quantitative proteomics was performed using tandem mass tag TMT10plex™ Isobaric Label Reagent Set followed by Q Exactive™ Hybrid Quadrupole Orbitrap Mass Spectrometry. Identification of proteins was performed using Proteome Discoverer 2.1. Proteins were compared based on the fold change and p value using Limma software. Significant proteins pathways were analyzed using Ingenuity Pathway (IPA). TMT analysis showed that ZIKV infected placentas had 94 reviewed differentially abundant proteins, 32 more abundant, and 62 less abundant. IPA analysis results indicate that 45 of the deregulated proteins are cellular components of the ECM and 16 play a role in its structure and organization. Among the most significant proteins in ZIKV positive placenta were fibronectin, bone marrow proteoglycan, and fibrinogen. Of these, fibrinogen was further validated by immunohistochemistry in 12 additional placenta samples and found significantly increased in ZIKV infected placentas. The upregulation of this protein in the placental tissue suggests that ZIKV infection is promoting the coagulation of placental tissue and restructuration of ECM potentially affecting the integrity of the tissue and facilitating dissemination of the virus from mother to the fetus.

DNA Methylation, Deamination, and Translesion Synthesis Combine to Generate Footprint Mutations in Cancer Driver Genes in B-Cell Derived Lymphomas and Other Cancers.

Cancer genomes harbor numerous genomic alterations and many cancers accumulate thousands of nucleotide sequence variations. A prominent fraction of these mutations arises as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases followed by the replication/repair of edited sites by DNA polymerases (pol), as deduced from the analysis of the DNA sequence context of mutations in different tumor tissues. We have used the weight matrix (sequence profile) approach to analyze mutagenesis due to Activation Induced Deaminase (AID) and two error-prone DNA polymerases. Control experiments using shuffled weight matrices and somatic mutations in immunoglobulin genes confirmed the power of this method. Analysis of somatic mutations in various cancers suggested that AID and DNA polymerases η and θ contribute to mutagenesis in contexts that almost universally correlate with the context of mutations in A:T and G:C sites during the affinity maturation of immunoglobulin genes. Previously, we demonstrated that AID contributes to mutagenesis in (de)methylated genomic DNA in various cancers. Our current analysis of methylation data from malignant lymphomas suggests that driver genes are subject to different (de)methylation processes than non-driver genes and, in addition to AID, the activity of pols η and θ contributes to the establishment of methylation-dependent mutation profiles. This may reflect the functional importance of interplay between mutagenesis in cancer and (de)methylation processes in different groups of genes. The resulting changes in CpG methylation levels and chromatin modifications are likely to cause changes in the expression levels of driver genes that may affect cancer initiation and/or progression.

Toward the discovery of biological functions associated with the mechanosensor Mtl1p of Saccharomyces cerevisiae via integrative multi-OMICs analysis.

Functional analysis of the Mtl1 protein in Saccharomyces cerevisiae has revealed that this transmembrane sensor endows yeast cells with resistance to oxidative stress through a signaling mechanism called the cell wall integrity pathway (CWI). We observed upregulation of multiple heat shock proteins (HSPs), proteins associated with the formation of stress granules, and the phosphatase subunit of trehalose 6-phosphate synthase which suggests that mtl1Δ strains undergo intrinsic activation of a non-lethal heat stress response. Furthermore, quantitative global proteomic analysis conducted on TMT-labeled proteins combined with metabolome analysis revealed that mtl1Δ strains exhibit decreased levels of metabolites of carboxylic acid metabolism, decreased expression of anabolic enzymes and increased expression of catabolic enzymes involved in the metabolism of amino acids, with enhanced expression of mitochondrial respirasome proteins. These observations support the idea that Mtl1 protein controls the suppression of a non-lethal heat stress response under normal conditions while it plays an important role in metabolic regulatory mechanisms linked to TORC1 signaling that are required to maintain cellular homeostasis and optimal mitochondrial function.

Hotspots of Sequence Variability in Gut Microbial Genes Encoding Pro-Inflammatory Factors Revealed by Oligotyping.

The gut microbiota has been implicated in a number of normal and disease biological processes. Recent studies have identified a subset of gut bacterial genes as potentially involved in inflammatory processes. In this work, we explore the sequence variability for some of these bacterial genes using a combination of deep sequencing and oligotyping, a data analysis application that identifies mutational hotspots in short stretches of DNA. The genes for pks island, tcpC and usp, all harbored by certain strains of E. coli and all implicated in inflammation, were amplified by PCR directly from stool samples and subjected to deep amplicon sequencing. For comparison, the same genes were amplified from individual bacterial clones. The amplicons for pks island and tcpC from stool samples showed minimal levels of heterogeneity comparable with the individual clones. The amplicons for usp from stool samples, by contrast, revealed the presence of five distinct oligotypes in two different regions. Of these, the oligotype GT was found to be present in the control uropathogenic clinical isolate and also detected in stool samples from individuals with colorectal cancer (CRC). Mutational hotspots were mapped onto the USP protein, revealing possible substitutions around Leu110, Glu114, and Arg115 in the middle of the pyocin domain (Gln110, Gln114, and Thr115 in most healthy samples), and also Arg218 in the middle of the nuclease domain (His218 in the uropathogenic strain). All of these results suggest that a level of variability within bacterial pro-inflammatory genes could explain differences in bacterial virulence and phenotype.

Modelling and molecular docking studies of the cytoplasmic domain of Wsc-family, full-length Ras2p, and therapeutic antifungal compounds.

Saccharomyces cerevisiae, the budding yeast, must remodel initial cell shape and cell wall integrity during vegetative growth and pheromone-induced morphogenesis. The cell wall remodeling is monitored and regulated by the cell wall integrity (CWI) signaling pathway. Wsc1p, together with Wsc2p and Wsc3p, belongs to a family of highly O-glycosylated cell surface proteins that function as stress sensors of the cell wall in S. cerevisiae. These cell surface proteins have the main role of activating the CWI signaling pathway by stimulating the small G-protein Rho1p, which subsequently activates protein kinase C (Pkc1p) and a mitogen activated protein (MAP) kinase cascade that activates downstream transcription factors of stress-response genes. Wsc1p, Wsc2p, and Wsc3p possess a cytoplasmic domain where two conserved regions of the sequence have been assessed to be important for Rom2p interaction. Meanwhile, other research groups have also proposed that these transmembrane proteins could support protein-protein interactions with Ras2p. Molecular structures of the cytoplasmic domain of Wsc1p, Wsc2p and Wsc3p were generated using the standard and fully-automated ORCHESTAR procedures provided by the Sybyl-X 2.1.1 program. The tridimensional structure of full length Ras2p was also generated with Phyre2. These protein models were validated with Procheck-PDBsum and ProSA-web tools and subsequently used in docking-based modeling of protein-protein and protein-compound interfaces for extensive structural and functional characterization of their interaction. The results retrieved from STRING 10.5 suggest that the Wsc-family is involved in protein-protein interactions with each other and with Ras2p. Docking-based studies also validated the existence of protein-protein interactions mainly between Motif I (Wsc3p > Wsc1p > Wsc2p) and Ras2p, in agreement with the data provided by STRING 10.5. Additionally, it has shown that Calcofluor White preferably binds to Wsc1p (-9.5 kcal/mol), meanwhile Caspofungin binds to Wsc3p (-9.1 kcal/mol), Wsc1p (-9.1 kcal/mol) and more weakly Wsc2p (-6.9 kcal/mol). Thus, these data suggests Caspofungin as a common inhibitor for the Wsc-family. MTiOpenScreen database has provided a list of new compounds with energy scores higher than those compounds used in our docking studies, thus suggesting these new compounds have a better affinity towards the cytoplasmic domains and Ras2p. Based on these data, there are new and possibly more effective compounds that should be considered as therapeutic agents against yeast infection.

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.

The Presence of Genotoxic and/or Pro-inflammatory Bacterial Genes in Gut Metagenomic Databases and Their Possible Link With Inflammatory Bowel Diseases.

Background: The human gut microbiota is a dynamic community of microorganisms that mediate important biochemical processes. Differences in the gut microbial composition have been associated with inflammatory bowel diseases (IBD) and other intestinal disorders. In this study, we quantified and compared the frequencies of eight genotoxic and/or pro-inflammatory bacterial genes found in metagenomic Whole Genome Sequences (mWGSs) of samples from individuals with IBD vs. a cohort of healthy human subjects. Methods: The eight selected gene sequences were clbN, clbB, cif, cnf-1, usp, tcpC from Escherichia coli, gelE from Enterococcus faecalis and murB from Akkermansia muciniphila. We also included the sequences for the conserved murB genes from E. coli and E. faecalis as markers for the presence of Enterobacteriaceae or Enterococci in the samples. The gene sequences were chosen based on their previously reported ability to disrupt normal cellular processes to either promote inflammation or to cause DNA damage in cultured cells or animal models, which could be linked to a role in IBD. The selected sequences were searched in three different mWGS datasets accessed through the Human Microbiome Project (HMP): a healthy cohort (N = 251), a Crohn's disease cohort (N = 60) and an ulcerative colitis cohort (N = 17). Results: Firstly, the sequences for the murB housekeeping genes from Enterobacteriaceae and Enterococci were more frequently found in the IBD cohorts (32% E. coli in IBD vs. 12% in healthy; 13% E. faecalis in IBD vs. 3% in healthy) than in the healthy cohort, confirming earlier reports of a higher presence of both of these taxa in IBD. For some of the sequences in our study, especially usp and gelE, their frequency was even more sharply increased in the IBD cohorts than in the healthy cohort, suggesting an association with IBD that is not easily explained by the increased presence of E. coli or E. faecalis in those samples. Conclusion: Our results suggest a significant association between the presence of some of these genotoxic or pro-inflammatory gene sequences and IBDs. In addition, these results illustrate the power and limitations of the HMP database in the detection of possible clinical correlations for individual bacterial genes.

The Characterization of Monoclonal Antibodies to Mouse TLT-1 Suggests That TLT-1 Plays a Role in Wound Healing.

Platelets play a vital role in hemostasis and inflammation. The membrane receptor TREM-like transcript-1 (TLT-1) is involved in platelet aggregation, bleeding, and inflammation, and it is localized in the α-granules of platelets. Upon platelet activation, TLT-1 is released from α-granules both in its transmembrane form and as a soluble fragment (sTLT-1). Higher levels of sTLT-1 have been detected in the plasma of patients with acute inflammation or sepsis, suggesting an important role for TLT-1 during inflammation. However, the roles of TLT-1 in hemostasis and inflammation are not well understood. We are developing the mouse model of TLT-1 to mechanistically test clinical associations of TLT-1 in health and disease. To facilitate our studies, monoclonal murine TLT-1 (mTLT-1) antibodies were produced by the immunization of a rabbit using the negatively charged region of the mTLT-1 extracellular domain 122PPVPGPREGEEAEDEK139. In the present study, we demonstrate that two selected clones, 4.6 and 4.8, are suitable for the detection of mTLT-1 by western blot, immunoprecipitation, immunofluorescent staining, flow cytometry and inhibit platelet aggregation in aggregometry assays. In addition, we found that the topical administration of clone 4.8 delayed the wound healing process in an experimental burn model. These results suggest that TLT-1 plays an important role in wound healing and because both clones specifically detect mTLT-1, they are suitable to further develop TLT-1 based models of inflammation and hemostasis in vivo.