Mapping of Epitopes Recognized by Antibodies Induced by Immunization of Mice with PspA and PspC

Pneumococcal surface protein A (PspA) and pneumococcal surface protein C (PspC) are important candidates for an alternative vaccine against pneumococcal infections. Since these antigens show variability, the use of variants that do not afford broad protection may lead to the selection of vaccine escape bacteria. Epitopes capable of inducing antibodies with broad cross-reactivities should thus be the preferred antigens. In this work, experiments using peptide arrays show that most linear epitopes recognized by antibodies induced in mice against different PspAs were located at the initial 44 amino acids of the mature protein and that antibodies against these linear epitopes did not confer protection against a lethal challenge. Conversely, linear epitopes recognized by antibodies to PspC included the consensus sequences involved in the interaction with human factor H and secretory immunoglobulin A (sIgA). Since linear epitopes of PspA were not protective, larger overlapping fragments containing 100 amino acids of PspA of strain Rx1 were constructed (fragments 1 to 7, numbered from the N terminus) to permit the mapping of antibodies with conformational epitopes not represented in the peptide arrays. Antibodies from mice immunized with fragments 1, 2, 4, and 5 were capable of binding onto the surface of pneumococci and mediating protection against a lethal challenge. The fact that immunization of mice with 100-amino-acid fragments located at the more conserved N-terminal region of PspA (fragments 1 and 2) induced protection against a pneumococcal challenge indicates that the induction of antibodies against conformational epitopes present at this region may be important in strategies for inducing broad protection against pneumococci.     

Association study between clinicopathological variables and periodontal breakdown in gingival pyogenic granuloma

Objective

The aim was to investigate a possible association between the immunoexpression of interleukin (IL)-4 and clinicopathological parameters with the periodontal breakdown observed in gingival pyogenic granuloma (PG).

Materials and methods

Paraffin-embedded samples of gingival PG (n?=?46) were prepared for histological and immunohistochemical assessment. Demographic and clinical parameters were assessed by criteria based on age stratum, gender, smoking habit, evolution course, location, lesion size, macroscopic appearance, predisposing factors, recurrence, and periodontal breakdown. Histological assessment included the appearance of epithelial lining, microvessel density, inflammatory infiltrate density, interstitial fibrosis, and histological arrangement. A staining intensity distribution (SID) score was used to assess IL-4 immunoreactivity. The association between candidate predictor variables and periodontal breakdown was analyzed individually and adjusted for confounding using a bivariate binary logistic regression model.

Results

Mean IL-4 SID values were significantly increased for long-standing and large lesions, presence of periodontal breakdown, high microvessel density, and moderate-to-severe inflammatory infiltrate density. While bivariate and univariate analyses revealed a positive association of the evolution course ≥12 months, lesion size >1 cm, high microvessel density, moderate-to-severe inflammatory infiltrate density, and IL-4 SID score ≥8.04 with periodontal breakdown, after bivariate logistic regression analysis, only the evolution course ≥12 months, moderate-to-severe inflammatory infiltrate density, and IL-4 SID score ≥8.04 remained as robust predictors of periodontal damage. Confounding and interaction effects between candidate predictor variables were also noted.

Conclusion

These findings suggest that while evolution course, inflammatory infiltrate density, and the overexpression of IL-4 may act as predictors of periodontal breakdown in gingival PG, there are mutual confounding and synergistic biological interactive effects with respect to the lesion size and microvessel density in the susceptible host that may be also associated with the bone resorption and tissue destruction.

Clinical relevance

Although the first-line therapy of gingival PG continues to be the surgical excision, this approach poses unwanted complications such as severe mucogingival defects and recurrence. Hence, early diagnosis and detection of these three significant predictor variables as well as timely surgical excision might help prevent the periodontal tissue destruction observed in some of these lesions.     

Effects of isometric handgrip training on cardiac autonomic profile: A systematic review and meta‐analysis study

Meta‐analyses have shown that isometric handgrip training reduces blood pressure in normotensive and hypertensive subjects. However, the effects on cardiac autonomic modulation are still controversial. Thus, the aim of this systematic review and meta‐analysis was to analyse the effects of isometric handgrip training on cardiac autonomic modulation in normotensive and hypertensive subjects. For this, Medline, Cinhal, Embase, Spordiscus and PEdro were searched for relevant studies published until December 2018. Randomized controlled trials investigating the effect of isometric handgrip training on heart rate variability parameters were considered eligible. Parameters were obtained in time (standard deviation of all the RR intervals‐SDNN, root mean square of successive differences between the normal adjacent RR intervals‐RMSSD and the percentage of adjacent intervals with more than 50 ms‐PNN50) and frequency domain (low frequency‐LF, high frequency‐HF and sympathovagal balance‐LF/HF). Mean difference (MD) and 95% confidence interval (95% CI) were calculated using an inverse variance method with a random effects model. Seven trials were included in the systematic review and meta‐analysis, totalling 86 participants. No significant effect was observed in heart rate variability parameters after isometric handgrip training (4 trials to SDNN: MD = ?1.44 ms and 95% CI = ?8.02, 5.14 ms; RMSSD: MD = ?1.48 ms and 95% CI = ?9.41, 6.45 ms; PNN50: MD = 0.85% and 95% CI = ?1.10, 2.81%; 7 trials to LF: ?0.17 n.u. and 95% CI = ?6.32, 5.98 n.u.; HF: MD = 0.17 n.u. and 95% CI = ?5.97, 6.30 n.u.; and LF/HF: MD = 0.13 and 95% CI = ?0.34, 0.59). In conclusion, current literature indicates that isometric handgrip training does not improve heart rate variability.     

Recent synthetic efforts in the preparation of 2-(3,4)-alkenyl (aryl) quinoline molecules towards anti-kinetoplastid agents

Leishmaniasis, Chagas disease and African sleeping sickness have been considered some of the most important tropical protozoan afflictions. As the number of drugs currently available to treat these human illnesses is severely limited and the majority has poor safety profiles and complicated administration schedules, actually there is an urgent need to develop new effective, safe and cost-effective drugs. Because quinoline alkaloids with antiprotozoal activity (quinine, chimanine, cryptolepine or huperzine groups) were historically and are still essential models for drug research to combat these parasitic infections, synthetic or semi-synthetic quinoline-based molecules are important for anti-kinetoplastid drug design approaches and synthetic methods of their preparation become a key task that is the central subject of this review. Its goal is to highlight the advances in the conventional and current syntheses of new 2-(3,4)-alkenyl (aryl) quinoline derivatives, which kill the most important kinetoplastid protozoa, – Leishmania and Trypanosoma and could be useful models for antileishmanial and antitrypanosomal research. An attempt has been made to present and discuss the more recent contributions in this field over the period 2015–2019, paying special attention to molecular design, synthetic efforts to new green reaction conditions for classical methods such as Skraup synthesis, Friedländer synthesis, Conrad–Limpach, Doebner–Miller, as well as contemporary methods like Gould–Jacobs, Meth–Cohn and Povarov reactions. This review includes brief general information on these neglected tropical diseases, their current chemotherapies, and primary natural models (quinoline alkaloids), suitable for development of anti-kinetoplastid quinoline-based agents. The main part of the review comprises critical discussion on the synthesis and chemistry of new quinolines diversely substituted by alkyl (alkenyl, aryl) fragments on the pyridine part of the quinoline skeleton, which could be considered interesting analogues of chimanine alkaloids. The methods described in this review were developed with the aim of overcoming the drawbacks of the traditional protocols using revolutionary precursors and strategies.

Leishmaniasis, Chagas disease and African sleeping sickness have been considered some of the most important tropical protozoan afflictions.     

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

Pooled -omic data from patient samples have enabled construction of cellular interaction modules that provides a system-level understanding of disease etiology. This new conceptualization of disease has led to discoveries of previously unknown mechanisms and has significantly expanded opportunities for therapeutic targeting (Iborra-Egea et al. 2017; Sharma et al. 2018). Specifically, system and network science has pinpointed novel pharmacological targets and opportunities for drug repurposing or drug–drug synergies (Zhao and Iyengar 2012). Advancements owing to system biology have been even more pronounced in oncology. The reconstruction of complex cancer disease modules describes tumor biology at the system level, which is particularly important for such a polygenic and dynamic disease (Zielinski et al. 2017; Lin et al. 2019).Despite these recent advancements, a truly individualized system approach has yet to be applied to individualized medicine. Oncology patients in particular experience highly variable disease phenotypes and drug responses. The need for precision approaches in oncology has therefore been well established, with numerous scientists and clinicians calling for innovation (Aronson and Rehm 2015; Relling and Evans 2015; Carrasco-Ramiro et al. 2017; Werner et al. 2017). Patient-derived xenograft (PDX) models and clinical studies have highlighted the heterogeneity of tumor mechanistic properties and therapeutic responses (Chiron et al. 2014; Dagogo-Jack and Shaw 2018; Xu et al. 2019). Some of this variability can be captured with patient stratification through disease subtype classification or biomarker testing, but the majority of inter-patient variability remains unexplained (Dagogo-Jack and Shaw 2018). The lack of broadly applicable biomarkers indicates that unique system-level interactions are at play within single patients. System and network biology is poised to capture these phenomena well, but new theoretical frameworks and computational approaches must be implemented to make such precision network biology a reality.Existing methodologies extract disease modules (or “disease networks”), which are highly perturbed subnetworks of the larger cellular interactome where disease gene interactions occur (Menche et al. 2015). Previous approaches have attempted to detect and prioritize individualized cancer drivers, but these algorithms infer their individualized analyses from cohort-level disease modules (Bashashati et al. 2012; Cho et al. 2016; Reyna et al. 2018). For example, the LIONESS algorithm uses aggregate disease modules generated by existing approaches to linearly interpolate individual sample modules (Kuijjer et al. 2019). We hypothesize that although some individual patient disease activity is recapitulated in the cohort disease modules, there are additional unexplored interactions detectable only at the individual patient level, which dictate patient-specific mechanisms, phenotypes, and therapeutic responses. We additionally suspect that at the gene level, there are patient-specific variations in pathogenicity. This is because patients possess highly varied basal cellular environments and mutations (The Cancer Genome Atlas Network 2012; Dagogo-Jack and Shaw 2018). Given that current approaches rely heavily or exclusively on cohort-inferred disease modules, we suspect that inter-patient variability and precision have been underrepresented.Although practical, using features inferred across the cohort fail to capture patient individualized features by disregarding rare unique factors within a patient. A new approach is needed to truly infer individualized disease modules that accurately recapitulate individualized disease. In this study, we examined on the collective actions of mutated genes to try to understand individualized disease at a deeper level. We hypothesized that cohort disease modules are poorly representative of individualized disease and that new insights in precision medicine would reveal themselves once we zoomed in on individual patients. Thus, we set out, first, to create a robust pipeline for individualized disease module construction and, second, to use these disease modules to characterize individualize disease pathobiology and therapeutics.     

Effect of Aqueous Cinnamon Extract on the Postprandial Glycemia Levels in Patients with Type 2 Diabetes Mellitus: A Randomized Controlled Trial

Cinnamon is a spice used in traditional cuisine that has been investigated due to hypoglycemic properties. The objective of this study was to investigate the effect of aqueous cinnamon extract on postprandial glycemia levels in type 2 diabetes mellitus (DM2) adults. This clinical trial enrolled 36 adults with DM2, randomly allocated in two groups: the control group (n = 18) took only an oral glucose tolerance test (OGTT) and the intervention group (n = 18) took OGTT immediately followed by aqueous cinnamon extract (6 g/100 mL) ingestion. Blood glucose levels were measured on fasting and after 30, 60, 90 and 120 min in both groups. The chemical analysis of the aqueous cinnamon extract included total phenols content determination and antioxidant activity assessment through FRAP and DPPH methods. The data reveal that aqueous cinnamon extract ingestion did not show a significant difference in the incremental area under the curve (p = 0.834), maximum glucose concentration (p = 0.527) and glucose concentration variation (p = 0.873) compared with the control group. Cinnamon extract possess a total phenol content of 1554.9 mg/L gallic acid equivalent and a strong antioxidant capacity, revealed by the DPPH (5125.0 µmol Trolox/L) and FRAP (3658.8 µmol Trolox/L) tests. Aqueous cinnamon extract did not significantly influence postprandial glucose response in diabetic patients during an OGTT.