Allosteric Inhibition of Factor XIIIa. Non-Saccharide Glycosaminoglycan Mimetics, but Not Glycosaminoglycans, Exhibit Promising Inhibition Profile

Factor XIIIa (FXIIIa) is a transglutaminase that catalyzes the last step in the coagulation process. Orthostery is the only approach that has been exploited to design FXIIIa inhibitors. Yet, allosteric inhibition of FXIIIa is a paradigm that may offer a key advantage of controlled inhibition over orthosteric inhibition. Such an approach is likely to lead to novel FXIIIa inhibitors that do not carry bleeding risks. We reasoned that targeting a collection of basic amino acid residues distant from FXIIIa’s active site by using sulfated glycosaminoglycans (GAGs) or non-saccharide GAG mimetics (NSGMs) would lead to the discovery of the first allosteric FXIIIa inhibitors. We tested a library of 22 variably sulfated GAGs and NSGMs against human FXIIIa to discover promising hits. Interestingly, although some GAGs bound to FXIIIa better than NSGMs, no GAG displayed any inhibition. An undecasulfated quercetin analog was found to inhibit FXIIIa with reasonable potency (efficacy of 98%). Michaelis-Menten kinetic studies revealed an allosteric mechanism of inhibition. Fluorescence studies confirmed close correspondence between binding affinity and inhibition potency, as expected for an allosteric process. The inhibitor was reversible and at least 9-fold- and 26-fold selective over two GAG-binding proteins factor Xa (efficacy of 71%) and thrombin, respectively, and at least 27-fold selective over a cysteine protease papain. The inhibitor also inhibited the FXIIIa-mediated polymerization of fibrin in vitro. Overall, our work presents the proof-of-principle that FXIIIa can be allosterically modulated by sulfated non-saccharide agents much smaller than GAGs, which should enable the design of selective and safe anticoagulants

CDKN1C (p57KIP2) Is a Direct Target of EZH2 and Suppressed by Multiple Epigenetic Mechanisms in Breast Cancer Cells

CDKN1C (encoding tumor suppressor p57KIP2) is a cyclin-dependent kinase (CDK) inhibitor whose family members are often transcriptionally downregulated in human cancer via promoter DNA methylation. In this study, we show that CDKN1C is repressed in breast cancer cells mainly through histone modifications. In particular, we show that CDKN1C is targeted by histone methyltransferase EZH2-mediated histone H3 lysine 27 trimethylation (H3K27me3), and can be strongly activated by inhibition of EZH2 in synergy with histone deacetylase inhibitor. Consistent with the overexpression of EZH2 in a variety of human cancers including breast cancer, CDKN1C in these cancers is downregulated, and breast tumors expressing low levels of CDKN1C are associated with a poor prognosis. We further show that assessing both EZH2 and CDKN1C expression levels as a measurement of EZH2 pathway activity provides a more predictive power of disease outcome than that achieved with EZH2 or CDKN1C alone. Taken together, our study reveals a novel epigenetic mechanism governing CDKN1C repression in breast cancer. Importantly, as a newly identified EZH2 target with prognostic value, it has implications in patient stratification for cancer therapeutic targeting EZH2-mediated gene repression

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.

BACKGROUND: Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS: We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS: The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows

Understanding cognition in older patients with cancer

Cancer and neurocognitive disorders, such as dementia and delirium, are common and serious diseases in the elderly that are accompanied by high degree of morbidity and mortality. Furthermore, evidence supports the under-diagnosis of both dementia and delirium in older adults. Complex questions exist regarding the interaction of dementia and delirium with cancer, beginning with guidelines on how best measure disease severity, the optimal screening test for either disorder, the appropriate level of intervention in the setting of abnormal findings, and strategies aimed at preventing the development or progression of either process. Ethical concerns emerge in the research setting, pertaining to the detection of cognitive dysfunction in participants, validity of consent, disclosure of abnormal results if screening is pursued, and recommended level of intervention by investigators. Furthermore, understanding the ways in which comorbid cognitive dysfunction and cancer impact both cancer and non-cancer-related outcomes is essential in guiding treatment decisions. In the following article, we will discuss what is presently known of the interactions of pre-existing cognitive impairment and delirium with cancer. We will also discuss identified deficits in our knowledge base, and propose ways in which innovative research may address these gaps

Meghdoot—A Mobile App to Access Location-Specific Weather-Based Agro-Advisories Pan India

Timely agricultural advisories to farmers can enhance their decision-making and reduce production risk under challenging weather conditions. To enhance access to relevant climate information services in India, a mobile application called Meghdoot was designed to deliver weather information and crop-specific advisories, as a joint initiative of the India Meteorological Department (IMD), Indian Institution for Tropical Meteorology (IITM), Indian Council for Agricultural Research (ICAR) and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). Building on IMD’s District Agrometeorological Advisory Service (DAAS) which issues crop-specific weather-based agro-advisories twice a week for all districts in India, the Meghdoot app makes available observed weather recordings, forecasts, and warnings generated by IMD and IITM. This working paper describes the concept design, the framework and a preliminary user analysis of the Meghdoot mobile application. Meghdoot mobile app is available on Google Play (Google Play Store) as well as Apple App Store. Since its inception more than two years ago, Meghdoot has received a good response with 200,000+ downloads/installs and an average rating of 3.3/5.0 by 863 app users (as of July 26, 2021) on Google Play(Google Play Store)

Antimony-Doped Tin(II) Sulfide Thin Films

Thin-film solar cells made from earth-abundant, inexpensive, and nontoxic materials are needed to replace the current technologies whose widespread use is limited by their use of scarce, costly, and toxic elements. Tin monosulfide (SnS) is a promising candidate for making absorber layers in scalable, inexpensive, and nontoxic solar cells. SnS has always been observed to be a p-type semiconductor. Doping SnS to form an n-type semiconductor would permit the construction of solar cells with p-n homojunctions. This paper reports doping SnS films with antimony, a potential n-type dopant. Small amounts of antimony (1%) were found to greatly increase the electrical resistance of the SnS. The resulting intrinsic SnS(Sb) films could be used for the insulating layer in a p-i-n design for solar cells. Higher concentrations (5%) of antimony did not convert the SnS(Sb) to low-resistivity n-type conductivity, but instead the films retain such a high resistance that the conductivity type could not be determined. Extended X-ray absorption fine structure analysis reveals that the highly doped films contain precipitates of a secondary phase that has chemical bonds characteristic of metallic antimony, rather than the antimony–sulfur bonds found in films with lower concentrations of antimony.United States. Dept. of Energy. Sunshot Initiative (Contract DE-EE0005329)National Science Foundation (U.S.) (Grant CBET-1032955

Towards Visible Light Hydrogen Generation: Quantum Dot-Sensitization via Efficient Light Harvesting of Hybrid-TiO2

We report pronounced enhancement of photoelectrochemical hydrogen generation of a quantum dot-sensitized hybrid-TiO2 (QD/H-TiO2) electrode that is composed of a mesoporous TiO2 layer sandwiched by a double sided energy harvesting layer consisting of a surface-textured TiO2 inverse opals layer on the bottom and a patterned mesoporous TiO2 layer on the top. CdSe/H-TiO2 exhibits a maximum photocurrent density of similar to 16.2 mA/cm(2), which is 35% higher than that of the optimized control sample (CdSe/P25), achieved by matching of the bandgap of quantum dot-sensitization with the wavelength where light harvesting of H-TiO2 is observed. Furthermore, CdSe/H-TiO2 under filtered exposure conditions recorded current density of similar to 14.2 mA/cm(2), the greatest value in the visible range. The excellent performance of the quantum dot-sensitized H-TiO2 suggests that alteration of the photoelectrodes to suitable nanostructures with excellent light absorption may offer optimal strategies for attaining maximum efficiency in a variety of photoconversion systems.open3

Global Profiling of DNA Replication Timing and Efficiency Reveals that Efficient Replication/Firing Occurs Late during S-Phase in S. pombe

10.1371/journal.pone.0000722PLoS ONE2

Zebrafish Whole-Adult-Organism Chemogenomics for Large-Scale Predictive and Discovery Chemical Biology

The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly), is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated) aromatic hydrocarbons [P(H)AHs] and estrogenic compounds (ECs), we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology