Availability of Receptors for Advanced Glycation End-Products (RAGE) Influences Differential Transcriptome Expression in Lungs from Mice Exposed to Chronic Secondhand Smoke (SHS).

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states including chronic obstructive pulmonary disease (COPD). RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain ambiguous. In this study, we assessed transcriptional outcomes in mice exposed to chronic SHS in the context of RAGE expression. RAGE knockout (RKO) and wild-type (WT) mice were delivered nose-only SHS via an exposure system for six months and compared to control mice exposed to room air (RA). We specifically compared WT + RA, WT + SHS, RKO + RA, and RKO + SHS. Analysis of gene expression data from WT + RA vs. WT + SHS showed FEZ1, Slpi, and Msln as significant at the three-month time point; while RKO + SHS vs. WT + SHS identified cytochrome p450 1a1 and Slc26a4 as significant at multiple time points; and the RKO + SHS vs. WT + RA revealed Tmem151A as significant at the three-month time point as well as Gprc5a and Dynlt1b as significant at the three- and six-month time points. Notable gene clusters were functionally analyzed and discovered to be specific to cytoskeletal elements, inflammatory signaling, lipogenesis, and ciliogenesis. We found gene ontologies (GO) demonstrated significant biological pathways differentially impacted by the presence of RAGE. We also observed evidence that the PI3K-Akt and NF-κB signaling pathways were significantly enriched in DEGs across multiple comparisons. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities.

Monolithic affinity columns in 3D printed microfluidics for chikungunya RNA detection.

Mosquito-borne pathogens plague much of the world, yet rapid and simple diagnosis is not available for many affected patients. Using a custom stereolithography 3D printer, we created microfluidic devices with affinity monoliths that could retain, noncovalently attach a fluorescent tag, and detect oligonucleotide and viral RNA. We optimized the fluorescent binding and sample load times using an oligonucleotide sequence from chikungunya virus (CHIKV). We also tested the specificity of CHIKV capture relative to genetically similar Sindbis virus. Moreover, viral RNA from both viruses was flowed through capture columns to study the efficiency and specificity of the column for viral CHIKV. We detected ~107 loaded viral genome copies, which was similar to levels in clinical samples during acute infection. These results show considerable promise for development of this platform into a rapid mosquito-borne viral pathogen detection system.

Comparative meta-analysis of host transcriptional response during Streptococcus pneumoniae carriage or infection.

Infection with Streptococcus pneumoniae causes over 150,000 cases of pneumonia annually in the United States alone. We performed a meta-analysis of publicly available RNA-sequencing data to compare the host-specific intracellular transcriptional responses that differ between infection and carriage with S. pneumoniae in humans and mice. We applied an automated preprocessing workflow to collections of publicly available fastq files that were categorized as either of two phenotypes-infection or carriage in humans and mice. We identified the differentially expressed genes and intracellular signaling pathways that changed in host cells during infection or carriage and whether these human phenotypes could be appropriately modeled at the molecular level in mice. Although we observed multiple differentially expressed genes shared among multiple comparisons, we found no overlapping significant signaling pathways between the mouse and human studies in either phenotype. Based on the samples included in this secondary analysis, we identified a minimal number of generalized transcriptional relationships between host infection and carriage phenotypes of S. pneumoniae that were consistently shared between the mouse and human hosts. Our findings suggest that additional controlled datasets in mouse and human carriage or infection models are needed to better understand the underlying mechanism(s) of invasion and pathogenesis. This knowledge could then contribute to the development of improved prophylactics and/or therapeutics against this pathogen.

Characterization of Staphylococcus aureus biofilms via crystal violet binding and biochemical composition assays of isolates from hospitals, raw meat, and biofilm-associated gene mutants.

Staphylococcus aureus (SA) is a gram-positive coccus and an opportunistic pathogen of humans. The ability of SA to form biofilms is an important virulence mechanism because biofilms are protected from host immune responses and antibiotic treatment. This study examines the relative biofilm strength of a variety of hospital and meat-associated strains of SA, using a crystal violet (CV) staining assay. Biofilms were treated with either DNase or proteinase K prior to CV staining, and compared to mock-treated results, to better understand the biochemical composition. Biofilm polysaccharide concentration was also measured using the phenol sulfuric-acid assay which was normalized to base biofilm strength. We found that hospital-associated isolates have biofilms that bind significantly more CV than for meat isolates and are significantly more protein and polysaccharide-based while meat isolates have significantly more DNA-based biofilms. This study also investigates the effects that biofilm-related genes have on biofilm formation and composition by analyzing specific transposon mutants of genes previously shown to play a role in biofilm development. agrA, atl, clfA, fnbA, purH, and sarA mutants produce significantly weaker biofilms (bind less CV) as compared to a wild-type control, whereas the acnA mutant produces a significantly stronger biofilm. Biofilms formed from these mutant strains were treated (or mock-treated) with DNase or proteinase K and tested with phenol and sulfuric acid to determine what role these genes play in biofilm composition. The acnA, clfA, fnbA, and purH mutants showed significant reduction in biofilm staining after either proteinase K or DNase treatment, agrA and sarA mutants showed significant biofilm reduction after only proteinase K treatment, and an atl mutant did not show significant biofilm reduction after either proteinase K or DNase treatment. These data suggest that biofilms that form without acnA, clfA, fnbA, and purH are DNA- and protein-based, that biofilms lacking agrA and sarA are mainly protein-based, and biofilms lacking atl are mainly polysaccharide-based. These results help to elucidate how these genes affect biofilm formation and demonstrate how mutating biofilm-related genes in SA can cause a change in biofilm composition.

Meta-Analysis of Two Human RNA-seq Datasets to Determine Periodontitis Diagnostic Biomarkers and Drug Target Candidates.

Periodontitis is a chronic inflammatory oral disease that affects approximately 42% of adults 30 years of age or older in the United States. In response to microbial dysbiosis within the periodontal pockets surrounding teeth, the host immune system generates an inflammatory environment in which soft tissue and alveolar bone destruction occur. The objective of this study was to identify diagnostic biomarkers and the mechanistic drivers of inflammation in periodontitis to identify drugs that may be repurposed to treat chronic inflammation. A meta-analysis comprised of two independent RNA-seq datasets was performed. RNA-seq analysis, signal pathway impact analysis, protein-protein interaction analysis, and drug target analysis were performed to identify the critical pathways and key players that initiate inflammation in periodontitis as well as to predict potential drug targets. Seventy-eight differentially expressed genes, 10 significantly impacted signaling pathways, and 10 hub proteins in periodontal gingival tissue were identified. The top 10 drugs that may be repurposed for treating periodontitis were then predicted from the gene expression and pathway data. The efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics to treat inflammation in gingival tissue affected by periodontitis.

Alphaviruses: Host pathogenesis, immune response, and vaccine & treatment updates.

Human pathogens belonging to the Alphavirus genus, in the Togaviridae family, are transmitted primarily by mosquitoes. The signs and symptoms associated with these viruses include fever and polyarthralgia, defined as joint pain and inflammation, as well as encephalitis. In the last decade, our understanding of the interactions between members of the alphavirus genus and the human host has increased due to the re-appearance of the chikungunya virus (CHIKV) in Asia and Europe, as well as its emergence in the Americas. Alphaviruses affect host immunity through cytokines and the interferon response. Understanding alphavirus interactions with both the innate immune system as well as the various cells in the adaptive immune systems is critical to developing effective therapeutics. In this review, we summarize the latest research on alphavirus-host cell interactions, underlying infection mechanisms, and possible treatments.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans.

UNLABELLED: Although a large number of immune epitopes have been identified in the influenza A virus (IAV) hemagglutinin (HA) protein using various experimental systems, it is unclear which are involved in protective immunity to natural infection in humans. We developed a data mining approach analyzing natural H1N1 human isolates to identify HA protein regions that may be targeted by the human immune system and can predict the evolution of IAV. We identified 16 amino acid sites experiencing diversifying selection during the evolution of prepandemic seasonal H1N1 strains and found that 11 sites were located in experimentally determined B-cell/antibody (Ab) epitopes, including three distinct neutralizing Caton epitopes: Sa, Sb, and Ca2 [A. J. Caton, G. G. Brownlee, J. W. Yewdell, and W. Gerhard, Cell 31:417-427, 1982, http://dx.doi.org/10.1016/0092-8674(82)90135-0]. We predicted that these diversified epitope regions would be the targets of mutation as the 2009 H1N1 pandemic (pH1N1) lineage evolves in response to the development of population-level protective immunity in humans. Using a chi-squared goodness-of-fit test, we identified 10 amino acid sites that significantly differed between the pH1N1 isolates and isolates from the recent 2012-2013 and 2013-2014 influenza seasons. Three of these sites were located in the same diversified B-cell/Ab epitope regions as identified in the analysis of prepandemic sequences, including Sa and Sb. As predicted, hemagglutination inhibition (HI) assays using human sera from subjects vaccinated with the initial pH1N1 isolate demonstrated reduced reactivity against 2013-2014 isolates. Taken together, these results suggest that diversifying selection analysis can identify key immune epitopes responsible for protective immunity to influenza virus in humans and thereby predict virus evolution. IMPORTANCE: The WHO estimates that approximately 5 to 10% of adults and 20 to 30% of children in the world are infected by influenza virus each year. While an adaptive immune response helps eliminate the virus following acute infection, the virus rapidly evolves to evade the established protective memory immune response, thus allowing for the regular seasonal cycles of influenza virus infection. The analytical approach described here, which combines an analysis of diversifying selection with an integration of immune epitope data, has allowed us to identify antigenic regions that contribute to protective immunity and are therefore the key targets of immune evasion by the virus. This information can be used to determine when sequence variations in seasonal influenza virus strains have affected regions responsible for protective immunity in order to decide when new vaccine formulations are warranted.

ViPR: an open bioinformatics database and analysis resource for virology research.

The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal 'Workbenches' for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses.

Genomic Analyses of Major SARS-CoV-2 Variants Predicting Multiple Regions of Pathogenic and Transmissive Importance.

The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the orf8 and N genes demonstrating such evidence with real-world impacts on pathogenicity and transmissivity.

Diversity and conservation of the genome architecture of phages infecting the Alphaproteobacteria.

IMPORTANCE: This study reports the results of the largest analysis of genome sequences from phages that infect the Alphaproteobacteria class of bacterial hosts. We analyzed over 100 whole genome sequences of phages to construct dotplots, categorize them into genetically distinct clusters, generate a bootstrapped phylogenetic tree, compute protein orthologs, and predict packaging strategies. We determined that the phage sequences primarily cluster by the bacterial host family, phage morphotype, and genome size. We expect that the findings reported in this seminal study will facilitate future analyses that will improve our knowledge of the phages that infect these hosts.

Different Lengths of Gestational Exposure to Secondhand Smoke or e-Cigarette Vapor Induce the Development of Placental Disease Symptoms.

Exposure to cigarette smoke is known to induce disease during pregnancy. Recent evidence showed that exposure to secondhand smoke (SHS) negatively impacts fetal and placental weights, leading to the development of intrauterine growth restriction (IUGR). Electronic cigarettes (eCigs) represent a phenomenon that has recently emerged, and their use is also steadily rising. Even so, the effects of SHS or eCigs during gestation remain limited. In the present study, we wanted to characterize the effects of SHS or eCig exposure at two different important gestational points during mouse pregnancy. C57/Bl6 mice were exposed to SHS or eCigs via a nose-only delivery system for 4 days (from 14.5 to 17.5 gestational days (dGA) or for 6 days (from 12.5 dGA to 17.5 dGA)). At the time of necropsy (18.5 dGA), placental and fetal weights were recorded, maternal blood pressure was determined, and a dipstick test to measure proteinuria was performed. Placental tissues were collected, and inflammatory molecules in the placenta were identified. Treatment with SHS showed the following: (1) a significant decrease in placental and fetal weights following four days of exposure, (2) higher systolic and diastolic blood pressure following six days of exposure, and (3) increased proteinuria after six days of exposure. Treatment with eCigs showed the following: (1) a significant decrease in placental weight and fetal weight following four or six days of exposure, (2) higher systolic and diastolic blood pressure following six days of exposure, and (3) increased proteinuria after six days of exposure. We also observed different inflammatory markers associated with the development of IUGR or PE. We conclude that the detrimental effects of SHS or eCig treatment coincide with the length of maternal exposure. These results could be beneficial in understanding the long-term effects of SHS or eCig exposure in the development of placental diseases.

Secondary analysis of Staphylococcus aureus whole genomes reveals diverse antimicrobial resistance profiles.

Antimicrobial resistance (AMR) in microorganisms is an ongoing threat to human health across the globe. To better characterize the AMR profiles of six strains of Staphylococcus aureus , we performed a secondary analysis that consisted of the following steps: 1) download fastq files from the Sequence Read Archive, 2) perform a de novo genome assembly from the sequencing reads, 3) annotate the assembled contigs, 4) predict the presence of antimicrobial resistance genes. We predicted the presence of 75 unique genes that conferred resistance against 22 unique antimicrobial compounds.

Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction.

Genetic drift of influenza virus genomic sequences occurs through the combined effects of sequence alterations introduced by a low-fidelity polymerase and the varying selective pressures experienced as the virus migrates through different host environments. While traditional phylogenetic analysis is useful in tracking the evolutionary heritage of these viruses, the specific genetic determinants that dictate important phenotypic characteristics are often difficult to discern within the complex genetic background arising through evolution. Here we describe a novel influenza virus sequence feature variant type (Flu-SFVT) approach, made available through the public Influenza Research Database resource (www.fludb.org), in which variant types (VTs) identified in defined influenza virus protein sequence features (SFs) are used for genotype-phenotype association studies. Since SFs have been defined for all influenza virus proteins based on known structural, functional, and immune epitope recognition properties, the Flu-SFVT approach allows the rapid identification of the molecular genetic determinants of important influenza virus characteristics and their connection to underlying biological functions. We demonstrate the use of the SFVT approach to obtain statistical evidence for effects of NS1 protein sequence variations in dictating influenza virus host range restriction.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Comparative sequence analysis elucidates the evolutionary patterns of Yersinia pestis in New Mexico over thirty-two years.

Background: Yersinia pestis, a Gram-negative bacterium, is the causative agent of plague. Y. pestis is a zoonotic pathogen that occasionally infects humans and became endemic in the western United States after spreading from California in 1899. Methods: To better understand evolutionary patterns in Y. pestis from the southwestern United States, we sequenced and analyzed 22 novel genomes from New Mexico. Analytical methods included, assembly, multiple sequences alignment, phylogenetic tree reconstruction, genotype-phenotype correlation, and selection pressure. Results: We identified four genes, including Yscp and locus tag YPO3944, which contained codons undergoing negative selection. We also observed 42 nucleotide sites displaying a statistically significant skew in the observed residue distribution based on the year of isolation. Overall, the three genes with the most statistically significant variations that associated with metadata for these isolates were sapA, fliC, and argD. Phylogenetic analyses point to a single introduction of Y. pestis into the United States with two subsequent, independent movements into New Mexico. Taken together, these analyses shed light on the evolutionary history of this pathogen in the southwestern US over a focused time range and confirm a single origin and introduction into North America.

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets.

Background: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Association between Religious Beliefs and HPV Vaccination Attitudes among College Students.

OBJECTIVE AND PARTICIPANTS: The authors sought an updated examination of attitudes toward Human Papillomavirus (HPV) catch-up vaccination among college students at a private religious university. METHODS: A total of 1557 college students completed a 62-question survey of religious and HPV vaccination attitudes during the fall of 2021. Students' willingness to receive catch-up HPV vaccination and willingness to vaccinate a future child against HPV were recorded. RESULTS: Of the 46.8% of students who reported being unvaccinated or unaware of vaccination status, ~26% reported being uninterested in receiving catch-up HPV vaccination; ~22% of all students surveyed reported being unwilling to vaccinate a future child against HPV. The strongest predictors of vaccine hesitancy included religious concerns about sexual abstinence and safety concerns. CONCLUSIONS: College health professionals can increase the rate of HPV vaccination among college students and subsequent future generations by addressing the safety and utility of the vaccine regardless of intentions for sexual abstinence prior to marriage. Additionally, rather than a uniform approach to all students who self-identify as Christian, an effort to identify and discuss the unique religiously influenced beliefs of individual students is recommended when discussing HPV vaccination.

A Novel Application of Spinning Disk Technology to Collect Plasma from Whole Blood Prior to Quantifying Plasma RNA.

The spinning disk technology has previously been utilized to isolate bacterial components from blood in hours instead of days. We hypothesized that this platform could be applied as an alternative approach to isolating plasma RNA from a whole blood sample. We consequently tested the efficacy of the spinning disk technology to extract plasma from whole blood upstream of RNA isolation and analysis. To do so, we collected plasma using either the spinning disk or the typical two-spin centrifuge method. We found that the spinning disk method results in significantly more hemolysis during collection than the conventional two-spin centrifuge method. However, when plasma RNA recovered from both collection methods was quantified using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), we found that the spinning disk method yielded a higher plasma RNA concentration than the two-spin centrifuge method. This suggests that the spinning disk may be an efficient alternative method to recover plasma RNA. Further work is needed to determine whether red blood cell RNA contamination is present in the plasma RNA extracted from spinning disk-processed plasma.

TK1 expression influences pathogenicity by cell cycle progression, cellular migration, and cellular survival in HCC 1806 breast cancer cells.

Breast cancer is the most common cancer diagnosis worldwide accounting for 1 out of every 8 cancer diagnoses. The elevated expression of Thymidine Kinase 1 (TK1) is associated with more aggressive tumor grades, including breast cancer. Recent studies indicate that TK1 may be involved in cancer pathogenesis; however, its direct involvement in breast cancer has not been identified. Here, we evaluate potential pathogenic effects of elevated TK1 expression by comparing HCC 1806 to HCC 1806 TK1-knockdown cancer cells (L133). Transcriptomic profiles of HCC 1806 and L133 cells showed cell cycle progression, apoptosis, and invasion as potential pathogenic pathways affected by TK1 expression. Subsequent in-vitro studies confirmed differences between HCC 1806 and L133 cells in cell cycle phase progression, cell survival, and cell migration. Expression comparison of several factors involved in these pathogenic pathways between HCC 1806 and L133 cells identified p21 and AKT3 transcripts were significantly affected by TK1 expression. Creation of a protein-protein interaction map of TK1 and the pathogenic factors we evaluated predict that the majority of factors evaluated either directly or indirectly interact with TK1. Our findings argue that TK1 elevation directly increases HCC 1806 cell pathogenicity and is likely occurring by p21- and AKT3-mediated mechanisms to promote cell cycle arrest, cellular migration, and cellular survival.