Complement activation during cardiac and thoracic vascular operations

To study complement activation, we evaluated nine patients who underwent cardiac operations requiring cardiopulmonary bypass (CPB) and nine other patients who underwent thoracic vascular operations without CPB. Concentration of C3, as measured by radioimmunoassay, was used as an indicator of complement activation (C3a is a complement-degradation product). In the CPB patients, the C3a level increased tenfold (from baseline value) after the onset of bypass, and continued to increase during bypass. Protamine produced an additional twofold increase in the C3a value, to a peak of 5461 +/- 1360 ng/ml. By 12 hours after surgery, the C3a level had decreased to normal (400 ng/ml). In the non-CPB patients, C3a remained at baseline levels until the administration of protamine, which caused a tenfold increase to a peak of 2281 +/- 293 ng/ml; C3a levels returned to normal 6 hours after operation. The peak postprotamine C3a levels were significantly higher (p < 0.01) in the CPB group than in the non-CPB group. This finding was due to the fact that, during CPB, complement activation occurs via the alternative pathway; the administration of protamine then causes additional activation via the classical pathway. During thoracic vascular operations, however, complement activation occurs only in response to protamine, via the classical pathway.     

Assessing parasitic infestation of vegetables in selected markets in Metro Manila,Philippines

ObjectiveTo assess the parasitic infestation of vegetables in selected markets in Metro Manila, Philippines.MethodsA total of 80 vegetables were purchased from public and private markets in Munoz, Quezon City, and Alabang, Muntinlupa City. Vegetables were washed, and the washings were collected and examined for parasitic organisms.ResultsIn all vegetables examined, 36 of 80 (45.0%) were infested with parasitic organisms. Vegetables obtained from Muntinlupa City showed that 17 of 40 (42.5%) have parasitic infestation as compared with those obtained from Quezon City with 19 of 40 (47.5%). Significant differences on the parasitic organisms existed between the public and private markets and between the two locations (P<0.05).ConclusionsFindings indicate that vegetables can be potential source of parasitic infection. There is a need to be vigilant in ensuring that foodstuffs sold in our market systems are safe and fit for consumption.     

Synthesis and Biological Evaluations of Electrophilic Steroids Inspired by the Taccalonolides

Natural products have served as inspirational scaffolds for the design and synthesis of novel antineoplastic agents. Here we present our preliminary efforts on the synthesis and biological evaluation of a new class of electrophilic steroids inspired by the naturally occurring taccalonolides. We demonstrate that these simplified analogs exhibit highly persistent antiproliferative properties similar to the taccalonolides and retain activity against resistant cancer cell lines that warrants further preclinical development.     

Comparison of the effectiveness of amitriptyline and gabapentin on chronic neuropathic pain in persons with spinal cord injury

Rintala DH, Holmes SA, Courtade D, Fiess RN, Tastard LV, Loubser PG. Comparison of the effectiveness of amitriptyline and gabapentin on chronic neuropathic pain in persons with spinal cord injury.

Objective

To test the hypotheses that both amitriptyline and gabapentin are more effective in relieving neuropathic pain than an active placebo, diphenhydramine.

Design

Randomized, controlled, double blind, triple crossover 8-week trial.

Setting

Veterans Affairs medical center.

Participants

Community dwelling adults with spinal cord injury (N=38) were recruited by telephone, letters, and flyers.

Intervention

Eight-week trial each of amitriptyline, gabapentin, and diphenhydramine.

Main Outcome Measures

Pain intensity measured with a 10-cm visual analog scale (VAS) and an 11-point (0-10) numeric rating scale (NRS) and depressive symptomatology measured with the Center for Epidemiologic Studies Depression Scale−Short Form (CESD-SF).

Results

Baseline VAS scores for participants with low (<10) CESD-SF scores was 4.61 and for those with high scores (≥10) it was 7.41. At week 8, in participants with high baseline CESD-SF scores, amitriptyline (mean, 4.21) was more effective than diphenhydramine (mean, 6.67; P=.035), and there was a nonsignificant trend suggesting that amitriptyline may be more effective than gabapentin (mean, 6.68; P=.061). Gabapentin was no more effective than diphenhydramine (P=.97). There was no significant difference among the medications for those with lower CESD-SF scores. Results could not be attributed to dropout rates, order or dose of medications, amount of medication taken for breakthrough pain, or side effects.

Conclusions

Amitriptyline is more efficacious in relieving neuropathic pain than diphenhydramine at or below the level of spinal cord injury in people who have considerable depressive symptomatology.     

A full-proteome,interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein–protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Through DNA sequencing of tumor mutations, precision oncology has enabled the identification of cancer drivers, therapy targets, and prognostic mutations that can guide individualized therapies for many cancer patients. For example, what was once defined as melanoma is now delineated as BRAF-positive or BRAF-negative melanoma, a meaningful distinction with respect to therapy with BRAF and MAPK pathway inhibitors. Similarly, whether a tumor has deficient DNA mismatch repair defines whether the patient is eligible for immune checkpoint inhibitor monoclonal antibody therapy. Precision medicine now has become part of mainstream oncology, and in 2019, >80% of oncology drugs in development are personalized medicines (Personalized Medicine Coalition 2019). However, an important current limitation to precision medicine is the overwhelming number of total somatic mutations that accumulate during tumorigenesis and progression. A significant challenge is distinguishing bona fide driver mutations that promote tumor growth from passenger mutations that are neutral and have no mechanistic impact. To date, international efforts in cancer genomics have provided whole-exome sequencing for tens of thousands of human cancers (Forbes et al. 2008; The International Cancer Genome et al. 2010; The Cancer Genome Atlas Research et al. 2013). Subsequent computational analyses have identified cancer driver genes in which mutations occur more frequently than expected (Futreal et al. 2004; Ding et al. 2008; Chapman et al. 2011; Morin et al. 2011; Stransky et al. 2011; Wang et al. 2011; Lawrence et al. 2013). Yet not all mutations on driver genes are driver mutations. This is usually interpreted as the driver–passenger paradigm, in which the few recurrent mutations are viewed as drivers, whereas most mutations, especially rare ones, are passengers that do not contribute to oncogenesis (Stratton et al. 2009; Porta-Pardo et al. 2017). In this regard, statistical models were developed to detect mutational hotspots (highly recurrently mutated residues across tumor samples) as candidate drivers. Such candidate list was quickly populated by more than 1000 hotspots (Chang et al. 2016, 2018), but only a small number of them have well-defined functional consequences. It was recently reported that some hotspot mutations are in fact passengers that arose from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops (Buisson et al. 2019). Thus, given the increasing number of cancer hotspots with uncertain significance, there is an urgent need to characterize their functional relevance toward translating the wealth of genomic data into biological and clinical insights.Although it is now possible to systematically test certain mutations by experiments (Ipe et al. 2017), genome-wide prioritization of candidate driver mutations still involves bioinformatics tools that predict the impact of mutations on protein function at the individual protein level (Adzhubei et al. 2010; Pollard et al. 2010; Kircher et al. 2014). However, not all mutations can be simply interpreted as causing a gross loss of protein. Many cancer mutations exert their oncogenic effects through altering specific aspects of protein activity and give cancer cells a selective advantage. One promising route to decipher this complexity is the view that the cell is a network of interacting biomolecules in which proteins carry out diverse functions by interacting with other proteins. We have previously shown that one key feature in understanding the functional impact of mutations is whether they fall in the binding interfaces that mediate interactions with other proteins and, critically, which specific interactions they mediate (Wang et al. 2012; Chen et al. 2018). Although studies of known disease mutations have already reported a strong association with protein interaction interfaces (Wei et al. 2014; Sahni et al. 2015), application of this feature has been largely limited by low coverage of structural information on interacting proteins; cocrystal structures and homology models together cover merely ∼6% of all known human interactions (Meyer et al. 2018). Here we leverage our newly established, the first human full-proteome 3D interactome with residue-resolution interface predictions (Interactome INSIDER) (Meyer et al. 2018) to systematically identify protein–protein interactions that are affected by mutational hotspots, aiming to offer a biologically informed framework that characterizes the functional relevance of mutational hotspots and nominate new cancer proteins across human cancers, with interaction-specific resolution at the full proteome scale.