Integrative Bioinformatics-Gene Network Approach Reveals Linkage between Estrogenic Endocrine Disruptors and Vascular Remodeling in Peripheral Arterial Disease.

Vascular diseases, including peripheral arterial disease (PAD), pulmonary arterial hypertension, and atherosclerosis, significantly impact global health due to their intricate relationship with vascular remodeling. This process, characterized by structural alterations in resistance vessels, is a hallmark of heightened vascular resistance seen in these disorders. The influence of environmental estrogenic endocrine disruptors (EEDs) on the vasculature suggests a potential exacerbation of these alterations. Our study employs an integrative approach, combining data mining with bioinformatics, to unravel the interactions between EEDs and vascular remodeling genes in the context of PAD. We explore the molecular dynamics by which EED exposure may alter vascular function in PAD patients. The investigation highlights the profound effect of EEDs on pivotal genes such as ID3, LY6E, FOS, PTP4A1, NAMPT, GADD45A, PDGF-BB, and NFKB, all of which play significant roles in PAD pathophysiology. The insights gained from our study enhance the understanding of genomic alterations induced by EEDs in vascular remodeling processes. Such knowledge is invaluable for developing strategies to prevent and manage vascular diseases, potentially mitigating the impact of harmful environmental pollutants like EEDs on conditions such as PAD.

Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. METHODS: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. RESULTS: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. CONCLUSIONS: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. METHODS: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. RESULTS: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the "golden ratio" of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. CONCLUSIONS: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutics to human ß cells.

The ability to detect and target ß cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human ß cell mass and the delivery of therapeutics in clinical settings. Here, we report the identification of two RNA aptamers that specifically and selectively recognize mouse and human ß cells. The putative targets of the two aptamers are transmembrane p24 trafficking protein 6 (TMED6) and clusterin (CLUS). When given systemically in immune deficient mice, these aptamers recognize the human islet graft producing a fluorescent signal proportional to the number of human islets transplanted. These aptamers cross-react with endogenous mouse ß cells and allow monitoring the rejection of mouse islet allografts. Finally, once conjugated to saRNA specific for X-linked inhibitor of apoptosis (XIAP), they can efficiently transfect non-dissociated human islets, prevent early graft loss, and improve the efficacy of human islet transplantation in immunodeficient in mice.

Identification of blood glucose patterns in patients with type 1 diabetes using continuous glucose monitoring and clustering technique.

OBJECTIVE: To show that statistical techniques allow for obtaining a reduced number of four-hour glucose profiles that can identify any glucose behavior in patients with type 1 diabetes mellitus. PATIENTS AND METHODS: A retrospective study of 10 patients with type 1 diabetes mellitus was conducted using data collected by continuous glucose monitoring. A data mining technique based on decision trees called CHAID (Chi-square Automatic Interaction Detection) was used to classify glucose profiles into groups using two decision criteria. These were 1, the seven days of the week and 2, different time slots, the day being divided into six sections of four hours each. Clustering was performed according to the glucose levels recorded using the statistically significant differences found. RESULTS: Significant differences (P-value <.05) and dependencies were seen between the glucose profiles classified depending on the independent variables 'day of the week' and 'time slot'. The relationships found were different for each patient, showing the need for individualized studies. CONCLUSIONS: The results obtained will facilitate mathematical modeling of glucose, and can be used to develop an individualized classifier for each patient that categorizes glucose profiles based on the day of the week and time slot variables. Using this classifier, it will be possible to predict the glucose levels of the patient knowing on which day of the week and in which time slot he/she is, leading to more precise models. Healthcare professionals will also be able to improve patient habits and therapies.

Small-Molecule Inhibitors of the Coronavirus Spike: ACE2 Protein-Protein Interaction as Blockers of Viral Attachment and Entry for SARS-CoV-2.

Inhibitors of the protein-protein interaction (PPI) between the SARS-CoV-2 spike protein and human ACE2 (hACE2), which acts as a ligand-receptor pair that initiates the viral attachment and cellular entry of this coronavirus causing the ongoing COVID-19 pandemic, are of considerable interest as potential antiviral agents. While blockade of such PPIs with small molecules is more challenging than that with antibodies, small-molecule inhibitors (SMIs) might offer alternatives that are less strain- and mutation-sensitive, suitable for oral or inhaled administration, and more controllable/less immunogenic. Here, we report the identification of SMIs of this PPI by screening our compound library focused around the chemical space of organic dyes. Among promising candidates identified, several dyes (Congo red, direct violet 1, Evans blue) and novel druglike compounds (DRI-C23041, DRI-C91005) inhibited the interaction of hACE2 with the spike proteins of SARS-CoV-2 as well as SARS-CoV with low micromolar activity in our cell-free ELISA-type assays (IC50's of 0.2-3.0 µM), whereas control compounds, such as sunset yellow FCF, chloroquine, and suramin, showed no activity. Protein thermal shift assays indicated that the SMIs of interest identified here bind SARS-CoV-2-S and not hACE2. While dyes seemed to be promiscuous inhibitors, DRI-C23041 showed some selectivity and inhibited the entry of two different SARS-CoV-2-S expressing pseudoviruses into hACE2-expressing cells in a concentration-dependent manner with low micromolar IC50's (6-7 µM). This provides proof-of-principle evidence for the feasibility of small-molecule inhibition of PPIs critical for SARS-CoV-2 attachment/entry and serves as a first guide in the search for SMI-based alternative antiviral therapies for the prevention and treatment of diseases caused by coronaviruses in general and COVID-19 in particular.

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.

BACKGROUND: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant ß-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. METHODS: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. RESULTS: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-ß, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. CONCLUSIONS: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

Identification of blood glucose patterns in patients with type1 diabetes using continuous glucose monitoring and clustering techniques. / Identificación de patrones de glucemia en pacientes con diabetes tipo1 mediante monitorización continua de glucosa y técnicas de clusterización.

OBJECTIVE: To show that statistical techniques allow for obtaining a reduced number of four-hour glucose profiles that can identify any glucose behavior in patients with type1 diabetes mellitus. MATERIAL AND METHODS: A retrospective study of 10 patients with type1 diabetes mellitus was conducted using data collected by continuous glucose monitoring. A data mining technique based on decision trees called CHAID (Chi-square Automatic Interaction Detection) was used to classify glucose profiles into groups using two decision criteria. These were: 1, the seven days of the week, and 2, different time slots, the day being divided into six sections of four hours each. Clustering was performed according to the glucose levels recorded using the statistically significant differences found. RESULTS: Significant differences (P<.05) and dependencies were seen between the glucose profiles classified depending on the independent variables 'day of the week' and 'time slot'. The relationships found were different for each patient, showing the need for individualized studies. CONCLUSIONS: The results obtained will facilitate mathematical modeling of glucose, and can be used to develop an individualized classifier for each patient that categorizes glucose profiles based on the day of the week and time slot variables. Using this classifier, it will be possible to predict the glucose levels of the patient knowing on which day of the week and in which time slot he/she is, leading to more precise models. Healthcare professionals will also be able to improve patient habits and therapies.

Longitudinal proteomics analysis in the immediate microenvironment of islet allografts during progression of rejection.

The applicability and benefits of pancreatic islet transplantation are limited due to various issues including the need to avoid immune-mediated rejection. Here, we used our experimental platform of allogeneic islet transplant in the anterior chamber of the eye (ACE-platform) to longitudinally monitor the progress of rejection in mice and obtain aqueous humor samples representative of the microenvironment of the graft for accurately-timed proteomic analyses. LC-MS/MS-based proteomics performed on such mass-limited samples (~5 µL) identified a total of 1296 proteins. Various analyses revealed distinct protein patterns associated with the mounting of the inflammatory and immune responses and their evolution with the progression of the rejection. Pathway analyses indicated predominant changes in cytotoxic functions, cell movement, and innate and adaptive immune responses. Network prediction analyses revealed transition from humoral to cellular immune response and exacerbation of pro-inflammatory signaling. One of the proteins identified by this localized proteomics as a candidate biomarker of islet rejection, Cystatin 3, was further validated by ELISA in the aqueous humor. This study provides (1) experimental evidence demonstrating the feasibility of longitudinal localized proteomics using small aqueous humor samples and (2) proof-of-concept for the discovery of biomarkers of impending immune attack from the immediate local microenvironment of ACE-transplanted islets. SIGNIFICANCE: The combination of the ACE-platform and longitudinal localized proteomics offers a powerful approach to biomarker discovery during the various stages of immune reactions mounted against transplanted tissues including pancreatic islets. It also supports proteomics-assisted drug discovery and development efforts aimed at preventing rejection through efficacy assessment of new agents by noninvasive and longitudinal graft monitoring.

The Effect of Recovery Warm-up Time Following Cold Storage on the Dynamic Glucose-stimulated Insulin Secretion of Isolated Human Islets.

Standardized islet characterization assays that can provide results in a timely manner are essential for successful islet cell transplantation. A critical component of islet cell quality is ß-cell function, and perifusion-based assessments of dynamic glucose-stimulated insulin secretion (GSIS) are the most informative method to assess this, as they provide the most complex in vitro evaluation of GSIS. However, protocols used vary considerably among centers and investigators as they often use different low- and high-glucose concentrations, exposure-times, flow-rates, oxygen concentrations, islet numbers, analytical methods, measurement units, and instruments, which result in different readouts and make comparisons across platforms difficult. Additionally, the conditions of islet storage and shipment prior to assessment may also affect islet function. Establishing improved standardized protocols for perifusion GSIS assays should be an integral part of the ongoing effort to increase the rigor of human islet studies. Here, we performed detailed evaluation of GSIS of human islets using a fully automated multichannel perifusion instrument following various warm-up recovery times after cold storage that corresponds to current shipping conditions (8°C). We found that recovery times shorter than 18 h (overnight) resulted in impaired insulin secretion. While the effects were relatively moderate on second-phase insulin secretion, first-phase peaks were restored only following 18-h incubation. Hence, the biphasic profile of dynamic GSIS was considerably affected when islets were not allowed to recover for a sufficient time after being maintained in cold. Accordingly, while cold storage might improve islet cell survival during shipment and prolong the length of culture, functional assessments should be performed only after allowing for at least overnight recovery at physiological temperatures.

Methylene Blue Inhibits the SARS-CoV-2 Spike-ACE2 Protein-Protein Interaction-a Mechanism that can Contribute to its Antiviral Activity Against COVID-19.

Due to our interest in the chemical space of organic dyes to identify potential small-molecule inhibitors (SMIs) for protein-protein interactions (PPIs), we initiated a screen of such compounds to assess their inhibitory activity against the interaction between SARS-CoV-2 spike protein and its cognate receptor ACE2, which is the first critical step initiating the viral attachment and entry of this coronavirus responsible for the ongoing COVID-19 pandemic. As part of this, we found that methylene blue, a tricyclic phenothiazine compound approved by the FDA for the treatment of methemoglobinemia and used for other medical applications (including the inactivation of viruses in blood products prior to transfusion when activated by light), inhibits this interaction. We confirmed that it does so in a concentration-dependent manner with a low micromolar half-maximal inhibitory concentration (IC50 = 3 µM) in our protein-based ELISA-type setup, while chloroquine, siramesine, and suramin showed no inhibitory activity in this assay. Erythrosine B, which we have shown before to be a promiscuous SMI of PPIs, also inhibited this interaction. Methylene blue inhibited the entry of a SARS-CoV-2 spike bearing pseudovirus into ACE2-expressing cells with similar IC50 (3.5 µM). Hence, this PPI inhibitory activity could contribute to its antiviral activity against SARS-CoV-2 even in the absence of light by blocking its attachment to ACE2-expressing cells and making this inexpensive and widely available drug potentially useful in the prevention and treatment of COVID-19 as an oral or inhaled medication.

Concentration-Dependency and Time Profile of Insulin Secretion: Dynamic Perifusion Studies With Human and Murine Islets.

The detailed characterization and quantification of the kinetics of glucose-stimulated insulin secretion (GSIS) by normal pancreatic islets is of considerable interest for characterizing ß-cell dysfunction, assessing the quality of isolated islets, and improving the design of artificial pancreas devices. Here, we performed dynamic evaluation of GSIS by human and mouse islets at high temporal resolution (every minute) in response to different glucose steps using an automated multichannel perifusion instrument. In both species, insulin responses were biphasic (a transient first-phase peak followed by a sustained second-phase), and the amount of insulin released showed a sigmoid-type dependence on glucose concentration. However, compared to murine islets, human islets have (1) a less pronounced first-phase response, (2) a flat secretion rate during second-phase response, (3) a left-shifted concentration response (reaching half-maximal response at 7.9 ± 0.4 vs. 13.7 ± 0.6 mM), and (4) an ~3-fold lower maximal secretion rate (8.3 ± 2.3 vs. 23.9 ± 5.1 pg/min/islet at 30 mM glucose). These results can be used to establish a more informative protocol for the calculation of the stimulation index, which is widely used for islet assessment in both research and clinical applications, but without an accepted standard or clear evidence as to what low- to high-glucose steps can provide better characterization of islet function. Data obtained here suggest that human islet functionality might be best characterized with a dynamic stimulation index obtained with a glucose step from a low of 4-5 to a high of 14-17 mM (e.g., G4 → G16).

Feasibility of Localized Metabolomics in the Study of Pancreatic Islets and Diabetes.

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of "localized metabolomics" where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

A Double Fail-Safe Approach to Prevent Tumorigenesis and Select Pancreatic ß Cells from Human Embryonic Stem Cells.

The transplantation of human embryonic stem cell (hESC)-derived insulin-producing ß cells for the treatment of diabetes is finally approaching the clinical stage. However, even with state-of-the-art differentiation protocols, a significant percentage of undefined non-endocrine cell types are still generated. Most importantly, there is the potential for carry-over of non-differentiated cell types that may produce teratomas. We sought to modify hESCs so that their differentiated progeny could be selectively devoid of tumorigenic cells and enriched for cells of the desired phenotype (in this case, ß cells). Here we report the generation of a modified hESC line harboring two suicide gene cassettes, whose expression results in cell death in the presence of specific pro-drugs. We show the efficacy of this system at enriching for ß cells and eliminating tumorigenic ones both in vitro and in vivo. Our approach is innovative inasmuch as it allows for the preservation of the desired cells while eliminating those with the potential to develop teratomas.

Design, Synthesis, and Evaluation of Novel Immunomodulatory Small Molecules Targeting the CD40⁻CD154 Costimulatory Protein-Protein Interaction.

We report the design, synthesis, and testing of novel small-molecule compounds targeting the CD40⁻CD154 (CD40L) costimulatory interaction for immunomodulatory purposes. This protein-protein interaction (PPI) is a TNF-superfamily (TNFSF) costimulatory interaction that is an important therapeutic target since it plays crucial roles in the activation of T cell responses, and there is resurgent interest in its modulation with several biologics in development. However, this interaction, just as all other PPIs, is difficult to target by small molecules. Following up on our previous work, we have now identified novel compounds such as DRI-C21091 or DRI-C21095 that show activity (IC50) in the high nanomolar to low micromolar range in the binding inhibition assay and more than thirty-fold selectivity versus other TNFSF PPIs including OX40⁻OX40L, BAFFR-BAFF, and TNF-R1-TNFα. Protein thermal shift (differential scanning fluorimetry) assays indicate CD154 and not CD40 as the binding partner. Activity has also been confirmed in cell assays and in a mouse model (alloantigen-induced T cell expansion in a draining lymph node). Our results expand the chemical space of identified small-molecule CD40⁻CD154 costimulatory inhibitors and provide lead structures that have the potential to be developed as orally bioavailable immunomodulatory therapeutics that are safer and less immunogenic than corresponding biologics.

A negative allosteric modulator of PDE4D enhances learning after traumatic brain injury.

Traumatic brain injury (TBI) significantly decreases cyclic AMP (cAMP) signaling which produces long-term synaptic plasticity deficits and chronic learning and memory impairments. Phosphodiesterase 4 (PDE4) is a major family of cAMP hydrolyzing enzymes in the brain and of the four PDE4 subtypes, PDE4D in particular has been found to be involved in memory formation. Although most PDE4 inhibitors target all PDE4 subtypes, PDE4D can be targeted with a selective, negative allosteric modulator, D159687. In this study, we hypothesized that treating animals with D159687 could reverse the cognitive deficits caused by TBI. To test this hypothesis, adult male Sprague Dawley rats received sham surgery or moderate parasagittal fluid-percussion brain injury. After 3 months of recovery, animals were treated with D159687 (0.3 mg/kg, intraperitoneally) at 30 min prior to cue and contextual fear conditioning, acquisition in the water maze or during a spatial working memory task. Treatment with D159687 had no significant effect on these behavioral tasks in non-injured, sham animals, but did reverse the learning and memory deficits in chronic TBI animals. Assessment of hippocampal slices at 3 months post-TBI revealed that D159687 reversed both the depression in basal synaptic transmission in area CA1 as well as the late-phase of long-term potentiation. These results demonstrate that a negative allosteric modulator of PDE4D may be a potential therapeutic to improve chronic cognitive dysfunction following TBI.

Osteopontin has a protective role in prostate tumor development in mice.

Osteopontin (OPN) is a protein, generally considered to play a pro-tumorigenic role, whereas several reports have demonstrated the anti-tumorigenic function of OPN during tumor development. These opposing anti- and pro-tumorigenic functions are not fully understood. Here, we report that host-derived OPN plays an anti-tumorigenic role in the transgenic adenocarcinoma of the mouse prostate (TRAMP) model and a TRAMP tumor transplant model. Tumor suppression mediated by OPN in Rag2-/- mice suggests that OPN is dispensable in the adaptive immune response. We found that host-derived OPN enhanced infiltration of natural killer (NK) cells into TRAMP tumors. The requirement of OPN in NK cell migration towards TRAMP cells was confirmed by an ex vivo cell migration assay. In contrast to TRAMP cells, in vivo B16 tumor development was not inhibited by OPN, and B16 tumors did not show OPN-mediated cell recruitment. It is possible that low levels of chemokine expression by B16 cells do not allow OPN to enhance immune cell recruitment. In addition to demonstrating the anti-tumorigenic role of OPN in TRAMP tumor development, this study also suggests that the contribution of OPN to tumor development depends on the type of tumor as well as the source and isoform of OPN.

Differential effects of low-dose decitabine on immune effector and suppressor responses in melanoma-bearing mice.

BACKGROUND: Low doses of the demethylating agent decitabine have been shown to enhance the sensitivity of tumors to immune effector cells and molecules through upregulation of tumor antigen presentation and apoptotic pathways. Effects on host immune effector and suppressor responses have not been well characterized. METHODS: Mice bearing B16 melanoma were treated with low-dose decitabine, cytokine, interleukin-2 (IL-2), toll-like receptor 9 agonist ODN1826, and/or a viral vectored vaccine targeting the melanoma antigen Trp2. Lymphoid and myeloid effector and suppressor cells were examined both systemically and intratumorally with functional, flow cytometric, and polymerase chain reaction-based assays. RESULTS: Enhancement of tumor growth delay was observed when decitabine was applied sequentially but not concurrently with IL-2. In contrast, complete responses and prolonged survival were observed when decitabine was applied with ODN1826 as therapy and with ODN1826 as a Trp2 vaccine adjuvant. Decitabine decreased natural killer and antigen-specific cellular immune responses when administered concurrently with IL-2 and with ODN1826; the Th1-associated transcription factor Tbet also decreased. T regulatory cells were not affected. When applied concurrently with ODN1826, decitabine increased macrophage cytotoxicity, M1 polarization, and dendritic cell activation. Myeloid-derived suppressor cells were reduced. CONCLUSION: Low-dose decitabine promotes both anti- and pro-tumor host immune responses to immunotherapeutics in melanoma-bearing mice. Macrophage effector and dendritic cell activation increase, and myeloid suppressor cells decrease. Lymphoid effector responses, however, can be inhibited.