Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy
Realistic modeling: new insight into the functions of the cerebellar network
Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work. In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.
This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.
Correspondence should be addressed to
Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it
Acknowledgments
This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).
Nader Pourmand, Ph.D.
Director, UCSC Genome Technology Center,University of California, Santa Cruz
Biosensor and Single Cell Manipulation using Nanopipettes
Approaching sub-cellular biological problems from an engineering perspective begs for the incorporation of electronic readouts. With their high sensitivity and low invasiveness, nanotechnology-based tools hold great promise for biochemical sensing and single-cell manipulation. During my talk I will discuss the incorporation of electrical measurements into nanopipette technology and present results showing the rapid and reversible response of these subcellular sensors to different analytes such as antigens, ions and carbohydrates. In addition, I will present the development of a single-cell manipulation platform that uses a nanopipette in a scanning ion-conductive microscopy technique. We use this newly developed technology to position the nanopipette with nanoscale precision, and to inject and/or aspirate a minute amount of material to and from individual cells or organelle without comprising cell viability. Furthermore, if time permits, I will show our strategy for a new, single-cell DNA/ RNA sequencing technology that will potentially use nanopipette technology to analyze the minute amount of aspirated cellular material.
R. Manjunath, Ph.D.
Associate Professor, Dept of Biochemistry, Indian Institute of Science, Bengaluru, India
REGULATION OF THE MHC COMPLEX AND HLA SOLUBILISATION BY THE FLAVIVIRUS, JAPANESE ENCEPHALITIS VIRUS
Viral encephalitis caused by Japanese encephalitis virus (JEV) and West Nile Virus (WNV) is a mosquito-borne disease that is prevalent in different parts of India and other parts of South East Asia. JEV is a positive single stranded RNA virus that belongs to the Flavivirus genus of the family Flaviviridae. The genome of JEV is about 11 kb long and codes for a polyprotein which is cleaved by both host and viral encoded proteases to form 3 structural and 7 non-structural proteins. It is a neurotropic virus which infects the central nervous system (CNS) and causes death predominantly in newborn children and young adults. JEV follows a zoonotic life-cycle involving mosquitoes and vertebrate, chiefly pigs and ardeid birds, as amplifying hosts. Humans are infected when bitten by an infected mosquito and are dead end hosts. Its structural, pathological, immunological and epidemiological aspects have been well studied. After entry into the host following a mosquito bite, JEV infection leads to acute peripheral neutrophil leucocytosis in the brain and leads to elevated levels of type I interferon, macrophage-derived chemotactic factor, RANTES,TNF-α and IL-8 in the serum and cerebrospinal fluid.
Major Histocompatibility Complex (MHC) molecules play a very important role in adaptive immune responses. Along with various classical MHC class I molecules, other non-classical MHC class I molecules play an important role in modulating innate immune responses. Our lab has shown the activation of cytotoxic T-cells (CTLs) during JEV infection and CTLs recognize non-self peptides presented on MHC molecules and provide protection by eliminating infected cells. However, along with proinflammatory cytokines such as TNFα, they may also cause immunopathology within the JEV infected brain. Both JEV and WNV, another related flavivirus have been shown to increase MHC class I expression. Infection of human foreskin fibroblast cells (HFF) by WNV results in upregulation of HLA expression. Data from our lab has also shown that JEV infection upregulates classical as well as nonclassical (class Ib) MHC antigen expression on the surface of primary mouse brain astrocytes and mouse embryonic fibroblasts.
There are no reports that have discussed the expression of these molecules on other cells like endothelial and astrocyte that play an important role in viral invasion in humans. We have studied the expression of human classical class I molecules HLA-A, -B, -C and the non-classical HLA molecules, HLA-E as well as HLA-F in immortalized human brain microvascular endothelial cells (HBMEC), human endothelial cell line (ECV304), human glioblastoma cell line (U87MG) and human foreskin fibroblast cells (HFF). Nonclassical MHC molecules such as mouse Qa-1b and its human homologue, HLA-E have been shown to be the ligand for the inhibitory NK receptor, NKG2A/CD94 and may bridge innate and adaptive immune responses. We show that JEV infection of HBMEC and ECV 304 cells upregulates the expression of HLA-A, and –B antigens as well as HLA-E and HLA-F. Increased expression of total HLA-E upon JEV infection was also observed in other human cell lines as well like, human amniotic epithelial cells, AV-3, FL and WISH cells. Further, we show for the first time that soluble HLA-E (sHLA-E) was released from infected ECV and HBMECs. In contrast, HFF cells showed only upregulation of cell-surface HLA-E expression while U87MG, a human glioblastoma cell line neither showed any cell-surface induction nor its solubilization. This shedding of sHLA-E was found to be dependent on matrix metalloproteinase (MMP) and an important MMP, MMP-9 was upregulated during JEV infection. Treatment with IFNγ resulted in the shedding of sHLA-E from ECV as well as U87MG but not from HFF cells. Also, sHLA-E was shed upon treatment with IFNβ and both IFNβ and TNFα, when present together caused an additive increase in the shedding of sHLA-E. HLA-E is an inhibitory ligand for CD94/NKG2A receptor of Natural Killer cells. Thus, MMP mediated solubilization of HLA-E from infected endothelial cells may have important implications in JEV pathogenesis including its ability to compromise the blood brain barrier.
Lalitha Subramanian, Ph.D.
Chief Scientific Officer & VP, Services at Scienomics, USA
Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery
Lalitha Subramanian, Dora Spyriouni, Andreas Bick, Sabine Schweizer, and Xenophon Krokidis Scienomics
The discovery of a compound which is potent in activity against a target is a major milestone in Pharmaceutical and Biotech industry. However, a potent compound is only effective as a therapeutic agent when it can be administered such that the optimal quantity is transported to the site of action at an optimal rate. The active pharmaceutical ingredient (API) has to be tested for its physicochemical properties before the appropriate dosage form and formulation can be designed. Some of the commonly evaluated parameters are crystal forms and polymorphs, solubility, dissolution behavior, stability, partition coefficient, water sorption behavior, surface properties, particle size and shape, etc. Pharmaceutical development teams face the challenge of quickly and efficiently determining a number of properties with small quantities of the expensive candidate compounds. Recently the trend has been to screen these properties as early as possible and often the candidate compounds are not available in sufficient quantities. Increasingly, these teams are leveraging nanoscale simulations similar to those employed by drug discovery teams for several decades. Nanoscale simulations are used to predict the behavior using very little experimental data and only if this is promising further experiments are done. Another aspect where nanoscale simulations are being used in drug development and drug delivery is to get insights into the behavior of the system so that process failures can be remediated and formulation performance can be improved. Thus, the predictive screening and the in-depth understanding leads to experimental efficiency resulting in far-reaching business impacts.
With specific examples, this talk will focus on the different types of nanoscale simulations used to predict properties of the API in excipients and also provide insight into system behavior as a function of shelf life, temperature, mechanical stress, etc.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
Gokul Das, Ph.D.
Co-Director, Breast Disease Site Research Group, Roswell Park Cancer Institute, Buffalo, NY
Probing Estrogen Receptor−Tumor Suppressor p53 Interaction in Cancer: From Basic Research to Clinical Trial
Tumor suppressor p53 and estrogen receptor have opposite roles in the onset and progression of breast cancer. p53 responds to a variety of cellular of stresses by restricting the proliferation and survival of abnormal cells. Estrogen receptor plays an important role in normal mammary gland development and the preservation of adult mammary gland function; however, when deregulated it becomes abnormally pro-proliferative and greatly contributes to breast tumorigenesis. The biological actions of estrogens are mediated by two genetically distinct estrogen receptors (ERs): ER alpha and ER beta. In addition to its expression in several ER alpha-positive breast cancers and normal mammary cells, ER beta is usually present in ER alpha-negative cancers including triple-negative breast cancer. In spite of genetically being wild type, why p53 is functionally debilitated in breast cancer has remained unclear. Our recent finding that ER alpha binds directly to p53 and inhibits its function has provided a novel mechanism for inactivating genetically wild type p53 in human cancer. Using a combination of proliferation and apoptosis assays, RNAi technology, quantitative chromatin immunoprecipitation (qChIP), and quantitative real-time PCR (qRT-PCR), in situ proximity ligation assay (PLA), and protein expression analysis in patient tissue micro array (TMA), we have demonstrated binding of ER alpha to p53 and have delineated the domains on both the proteins necessary for the interaction. Importantly, ionizing radiation inhibits the ER-p53 interaction in vivo both in human cancer cells and human breast tumor xenografts in mice. In addition, antiestrogenstamoxifen and faslodex/fulvestrant (ICI 182780) disrupt the ER-p53 interaction and counteract the repressive effect of ER alpha on p53, whereas 17β-estradiol (E2) enhances the interaction. Intriguingly, E2 has diametrically opposite effects on corepressor recruitment to a p53-target gene promoter versus a prototypic ERE-containing promoter. Thus, we have uncovered a novel mechanism by which estrogen could be providing a strong proliferative advantage to cells by dual mechanisms: enhancing expression of ERE-containing pro-proliferative genes while at the same time inhibiting transcription of p53-dependent anti-proliferative genes. Consistently, ER alpha enhances cell cycle progression and inhibits apoptosis of breast cancer cells. Correlating with these observations, our retrospective clinical study shows that presence of wild type p53 in ER-positive breast tumors is associated with better response to tamoxifen therapy. These data suggest ER alpha-p53 interaction could be one of the mechanisms underlying resistance to tamoxifen therapy, a major clinical challenge encountered in breast cancer patients. We have launched a prospective clinical trial to analyze ER-p53 interaction in breast cancer patient tumors at Roswell Park Cancer Institute. Our more recent finding that ER beta has opposite functions depending on the mutational status of p53 in breast cancer cells is significant in understanding the hard-to-treat triple-negative breast cancer and in developing novel therapeutic strategies against it. Our integrated approach to analyze ER-p53 interaction at the basic, translational, and clinical research levels has major implications in the diagnosis, prognosis, and treatment of breast cancer.
Akhilesh Pandey, Ph.D.
Professor, Johns Hopkins University School of Medicine, Baltimore, USA
A draft map of the human proteome
We have generated a draft map of the human proteome through a systematic and comprehensive analysis of normal human adult tissues, fetal tissues and hematopoietic cells as an India-US initiative. This unique dataset was generated from 30 histologically normal adult tissues, fetal tissues and purified primary hematopoietic cells that were analyzed at high resolution in the MS mode and by HCD fragmentation in the MS/MS mode on LTQ-Orbitrap Velos/Elite mass spectrometers. This dataset was searched against a 6-frame translation of the human genome and RNA-Seq transcripts in addition to standard protein databases. In addition to confirming a large majority (>16,000) of the annotated protein-coding genes in humans, we obtained novel information at multiple levels: novel protein-coding genes, unannotated exons, novel splice sites, proof of translation of pseudogenes (i.e. genes incorrectly annotated as pseudogenes), fused genes, SNPs encoded in proteins and novel N-termini to name a few. Many proteins identified in this study were identified by proteomic methods for the first time (e.g. hypothetical proteins or proteins annotated based solely on their chromosomal location). We have generated a catalog of proteins that show a more tissue-restricted pattern of expression, which should serve as the basis for pursuing biomarkers for diseases pertaining to specific organs. This study also provides one of the largest sets of proteotypic peptides for use in developing MRM assays for human proteins. Identification of several novel protein-coding regions in the human genome underscores the importance of systematic characterization of the human proteome and accurate annotation of protein-coding genes. This comprehensive dataset will complement other global HUPO initiatives using antibody-based as well as MRM mass spectrometry-based strategies. Finally, we believe that this dataset will become a reference set for use as a spectral library as well as for interesting interrogations pertaining to biomedical as well as bioinformatics questions.