Benny Abraham Kaipparettu

Bio: Benny Abraham Kaipparettu is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Cancer & Triple-negative breast cancer. The author has an hindex of 25, co-authored 45 publications receiving 4083 citations. Previous affiliations of Benny Abraham Kaipparettu include Roswell Park Cancer Institute & University of Tübingen.

Graphene quantum dots derived from carbon fibers.

Abstract: Graphene quantum dots (GQDs), which are edge-bound nanometer-size graphene pieces, have fascinating optical and electronic properties. These have been synthesized either by nanolithography or from starting materials such as graphene oxide (GO) by the chemical breakdown of their extended planar structure, both of which are multistep tedious processes. Here, we report that during the acid treatment and chemical exfoliation of traditional pitch-based carbon fibers, that are both cheap and commercially available, the stacked graphitic submicrometer domains of the fibers are easily broken down, leading to the creation of GQDs with different size distribution in scalable amounts. The as-produced GQDs, in the size range of 1–4 nm, show two-dimensional morphology, most of which present zigzag edge structure, and are 1–3 atomic layers thick. The photoluminescence of the GQDs can be tailored through varying the size of the GQDs by changing process parameters. Due to the luminescence stability, nanosecond lifetime, ...

Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways.

Abstract: Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities. Previously, by modeling the gene regulation of cancer metabolism we have reported that cancer cells can acquire a stable hybrid metabolic state in which both glycolysis and OXPHOS can be used. Here, to comprehensively characterize cancer metabolic activity, we establish a theoretical framework by coupling gene regulation with metabolic pathways. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis, respectively, with the activities of three major metabolic pathways: glucose oxidation, glycolysis, and fatty acid oxidation. Our model further characterizes the hybrid metabolic state and a metabolically inactive state where cells have low activity of both glycolysis and OXPHOS. We verify the model prediction using metabolomics and transcriptomics data from paired tumor and adjacent benign tissue samples from a cohort of breast cancer patients and RNA-sequencing data from The Cancer Genome Atlas. We further validate the model prediction by in vitro studies of aggressive triple-negative breast cancer (TNBC) cells. The experimental results confirm that TNBC cells can maintain a hybrid metabolic phenotype and targeting both glycolysis and OXPHOS is necessary to eliminate their metabolic plasticity. In summary, our work serves as a platform to symmetrically study how tuning gene activity modulates metabolic pathway activity, and vice versa.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Functionalization of Graphene: Covalent and Non-Covalent Approaches, Derivatives and Applications

Abstract: Approaches, Derivatives and Applications Vasilios Georgakilas,† Michal Otyepka,‡ Athanasios B. Bourlinos,‡ Vimlesh Chandra, Namdong Kim, K. Christian Kemp, Pavel Hobza,‡,§,⊥ Radek Zboril,*,‡ and Kwang S. Kim* †Institute of Materials Science, NCSR “Demokritos”, Ag. Paraskevi Attikis, 15310 Athens, Greece ‡Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic Center for Superfunctional Materials, Department of Chemistry, Pohang University of Science and Technology, San 31, Hyojadong, Namgu, Pohang 790-784, Korea Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, v.v.i., Flemingovo naḿ. 2, 166 10 Prague 6, Czech Republic

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Abstract: Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Carbon nanodots: synthesis, properties and applications

Abstract: Carbon nanodots (C-dots) have generated enormous excitement because of their superiority in water solubility, chemical inertness, low toxicity, ease of functionalization and resistance to photobleaching. In this review, by introducing the synthesis and photo- and electron-properties of C-dots, we hope to provide further insight into their controversial emission origin (particularly the upconverted photoluminescence) and to stimulate further research into their potential applications, especially in photocatalysis, energy conversion, optoelectronics, and sensing.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

Abstract: The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.