News Article | May 23, 2017
Pluripotent stem cells (PSCs) offer an unlimited source of human cardiovascular cells for research and the development of cardiac regeneration therapies. The development of highly efficient cardiac-directed differentiation methods makes it possible to generate large numbers of cardiomyocytes (hPSC-CMs). Due to varying differentiation efficiencies, further enrichment of CM populations for downstream applications is essential. Recently, a CM-specific cell surface marker called SIRPa (signal-regulatory protein alpha, also termed CD172a) was reported to be a useful tool for flow sorting of human stem cell–derived CMs. However, our expression analysis revealed that SIRPa only labels a subpopulation of CMs indicated by cardiac Troponin T (cTnT) expression. Moreover, SIRPa is also expressed on a sub population of non-CMs, hence making SIRa an inadequate marker to enrich PSC-derived CMs. In this webinar, sponsored by the team at Miltenyi Biotec, participants will have a chance to review human induced pluripotent stem cell derivation, cardiac directed differentiation to human pluripotent stem cell cardiomyocytes (hPSC-CMs), enrichment of hPSC-CMs and subsequent formation of 2D monolayers of electrically connected cells. They will also learn of the generation of purified human induced pluripotent stem cell derived cardiomyocyte. The speaker for this event will be Dr. Todd J. Herron, director of the Frankel Cardiovascular Center's Cardiovascular Regeneration Core Laboratory and Assistant Research Professor at the University of Michigan Center for Arrhythmia Research. Herron currently serves as the director of the Frankel Cardiovascular Center's Cardiovascular Regeneration Core Laboratory, as well as holding a position on the faculty in the University of Michigan Medical School and has appointments in the Department of Internal Medicine and Molecular & Integrative Physiology as Associate Research Scientist. His research is focused on the complex interplay between cardiac electrical excitation and contractile force generation-a process known classically as excitation-contraction coupling. LabRoots will host the event June 7, 2017, beginning at 9 a.m. PDT, 12 p.m. EDT. To read more about this event, learn about the continuing education credits offered, or to register for free, click here. ABOUT MILTENYI BIOTEC Miltenyi Biotec is a global provider of products and services that advance biomedical research and cellular therapy. The company’s innovative tools support research at every level, from basic research to translational research to clinical application. This integrated portfolio enables scientists and clinicians to obtain, analyze, and utilize the cell. Miltenyi Biotec’s technologies cover techniques of sample preparation, cell isolation, cell sorting, flow cytometry, cell culture, molecular analysis, and preclinical imaging. Their more than 25 years of expertise spans research areas including immunology, stem cell biology, neuroscience, and cancer, and clinical research areas like hematology, graft engineering, and apheresis. In their commitment to the scientific community, Miltenyi Biotec also offers comprehensive scientific support, consultation, and expert training. Today, Miltenyi Biotec has more than 1,500 employees in 25 countries – all dedicated to helping researchers and clinicians around the world make a greater impact on science and health. ABOUT LABROOTS LabRoots is the leading scientific social networking website, which provides daily scientific trending news and science-themed apparel, as well as produces educational virtual events and webinars, on the latest discoveries and advancements in science. Contributing to the advancement of science through content sharing capabilities, LabRoots is a powerful advocate in amplifying global networks and communities. Founded in 2008, LabRoots emphasizes digital innovation in scientific collaboration and learning, and is a primary source for current scientific news, webinars, virtual conferences, and more. LabRoots has grown into the world’s largest series of virtual events within the Life Sciences and Clinical Diagnostics community.
News Article | May 23, 2017
Growth estimates in the IVD market range from 3 to 4%, but this number depends on the segment covered. Some segments will grow at half the rate, others at double or triple average. Most areas of the IVD market are competitive. New companies enter the IVD market on a consistent basis, and IVD market revenue growth exceeds device or average pharmaceutical markets. Through 2021, the fastest global growth in IVD procedure volume will emerge in molecular assays followed by histology/cytology processes, and non-glucose POC tests. Reflecting further advances in amplification technologies and the discovery of new genetic and protein markers, molecular assays will expand applications in cancer and infectious disease detection and characterization, transplant matching, donated blood screening, and pharmacodiagnostics. The increasing use of in situ hybridization in the diagnosis, analysis, and monitoring of hematopoietic neoplasms and solid tumors, along with the widening adaptation of immunohistochemistry techniques to the identification of cancerous cell- and tissue-based antigens, will underlie fast-paced growth in the combined worldwide volume of histology and cytology procedures. The continuing diversification of hospital ambulatory and emergency departments, outpatient clinics, and physicians' offices into onsite patient testing will promote strong growth in the number of non-glucose POC procedures conducted on lower volume, desktop immunoassay systems. Demand patterns and growth prospects for IVD procedures will vary widely by country and region. The fastest growth in volume will occur in developing countries that leverage increasing economic prosperity to achieve significant improvements in medical delivery systems and resident healthcare accessibility. By contrast, the impoverished nations of Africa, Asia, and Central and South America, are not expected to achieve a significant upgrading of diagnostic testing capabilities in the near term and will remain dependent on philanthropic organizations to detect and control epidemic threats such as HIV, Ebola, and Zika. Due to maturing markets, the volume of IVD procedures implemented in the developed world will increase more slowly than the average pace of the developing economies. Moreover, in most developed countries, the strengthening of healthcare cost containment initiatives and price controls will result in tighter restrictions being imposed on patient services. This trend will increase pressure on medical providers to avoid prescribing or implementing unnecessary tests, which, in turn, will hold down growth in the volume of IVD procedures. In spite of tighter cost containment measures and slower overall growth in the volume of IVD procedures, the developed countries will provide the best revenue opportunities for high value-added esoteric patient tests. By enabling the earlier detection and more definitive diagnosis of complex diseases and disorders, these tests hold significant potential to improve the quality and reduce the overall cost of patient care. Accordingly, they are expected to build up applications in the U.S. and other developed countries pursuing value-based healthcare strategies. Key Topics Covered: 1: Executive Summary 2: Introduction 3: Regional Market Test Procedure Volume Analysis 4: Point Of Care Test Volume And Pricing Analysis 5: Core Laboratory Test Volume And Pricing Analysis 6: Molecular Assays 7: Hematology Test Volume And Pricing Analysis 8: Coagulation Test Volume And Pricing Analysis 9: Microbiology Test Volume And Pricing Analysis 10: Blood Typing / Grouping Test Volume And Pricing Analysis 11: Histology And Cytology Test Volume And Pricing Analysis 12: Other IVD Competitors For more information about this report visit http://www.researchandmarkets.com/research/hjjktj/worldwide_in To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/worldwide-in-vitro-diagnostics-market-2017-demand-patterns--growth-prospects-for-ivd-procedures-vary-widely-by-country--region---research-and-markets-300462412.html
News Article | May 24, 2017
No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment. Stable cell lines expressing affinity-tagged bait proteins were created according to protocols described previously in detail4. In brief, C-terminally HA–Flag-tagged clones targeting human bait proteins were constructed from clones included in version 8.1 of the human ORFeome (http://horfdb.dfci.harvard.edu)14. All expression clones used in this study are available from the Dana Farber/Harvard Cancer Center DNA Resource Core Facility (http://dnaseq.med.harvard.edu/). After sequence validation, clones were introduced into HEK293T, HCT116, or MCF10A cells (all from American Type Culture Collection) via lentiviral transfection. Cells were expanded under puromycin selection to obtain five 10-cm dishes per cell line before AP–MS. Bait proteins were selected from the ORFeome for high-throughput AP–MS analysis in batches corresponding to individual 96-well plates. Plates were selected for processing in random order. For AP–MS experiments in MCF10A cells, 1.15 × 106 cells per 15 cm dish were collected after 3 days (sub-confluent) or after 14 days in culture (contact inhibited) to allow for expulsion of YAP1 from the nucleus and Hippo pathway activation. MCF10A cells were grown in DMEM/F12 media supplemented with 5% horse serum, 20 ng ml−1 EGF, 10 μg ml−1 insulin, 0.5 μg ml−1 hydrocortisone, 100 ng ml−1 cholera toxin, 50 U ml−1 penicillin, and 50 μg ml−1 streptomycin. All cell lines were found to be free of mycoplasma using Mycoplasma Plus PCR assay kit (Agilent). Karyotyping (GTG-banded karyotype) of HeLa, HCT116, and HEK293T cells for cell line validation was performed by Brigham and Women’s Hospital Cytogenomics Core Laboratory. All AP–MS experiments were performed as presented previously in full4. In brief, cell pellets were lysed in the presence of 50 mM Tris-HCl pH 7.5, 300 mM NaCl, 0.5% (v/v) NP40, followed by centrifugation and filtration to remove debris. Immunoprecipitation was achieved using immobilized and pre-washed mouse monoclonal anti-HA agarose resin (Sigma-Aldrich, clone HA-7) that was incubated with clarified lysate for 4 h at 4 °C before removal of supernatant and four washes with lysis buffer followed by two washes with PBS (pH 7.2). Complexes were eluted in two steps using HA peptide in PBS at 37 °C and subsequently underwent TCA precipitation. Baits were processed in batches corresponding to 96-well plates in the ORFeome collection; plates were processed in random order. In preparation for LC–MS analysis, protein samples were reduced and digested with sequencing-grade trypsin (Promega). Peptides were then de-salted using homemade StageTips30 and approximately 1 μg of peptides were loaded onto C18 reversed-phase microcapillary columns and analysed on Thermo Fisher Q-Exactive mass spectrometers. Data acquisition methods were approximately 70 min long, including sample loading, gradient, and column re-equilibration. Tandem mass spectrometry (MS/MS) spectra were acquired in data-dependent fashion targeting the top 20 precursors for MS2 analysis. Unless noted otherwise, a single biological replicate of each bait was subjected to affinity purification followed by technical duplicate LC–MS analysis. For a complete description of data acquisition parameters, see ref. 4. A brief synopsis of our methods for identifying peptides and proteins from LC–MS data and distinguishing bona fide interacting proteins from background is provided here. For full details, refer to ref. 4. The BioPlex 2.0 network was generated by reanalysing Sequest search results from the BioPlex 1.0 dataset, combined with additional new AP–MS datasets. Sequest31 was used to match MS/MS spectra with peptide sequences from the Uniprot20 human protein database supplemented with sequences of green florescent protein (GFP) (our negative control), our Flag–HA affinity tag, and common contaminant proteins. This version of the UniProt database includes both SwissProt and Trembl entries and was current in 2013, at the outset of this project when the first AP–MS data were collected and searched. All protein sequences were included in forward and reversed orientations. Only fully tryptic peptides with two or fewer missed cleavages were considered, and precursor and product ion mass tolerances were set to 50 p.p.m. and 0.05 Da, respectively. The sole variable modification considered was oxidation of methionine (+15.9949). Target-decoy filtering32 was applied to control FDRs, using a linear discriminant function for peptide filtering and probabilistic scoring at the protein level33. Linear discriminant analysis considered Xcorr, D-Cn, peptide length, charge state, fractions of ions matched, and precursor mass error to distinguish correct from incorrect identifications. Peptide-spectral matches from each run were filtered to a 1% protein-level FDR with additional entropy-based filtering4 to reduce the final dataset protein-level FDR to well under 1%. Protein identifications supported by only a single peptide were discarded as well. These additional post-search filters further reduced the dataset-level FDR by over 100-fold. Scoring to identify HCIPs was performed in multiple stages after combining technical duplicate analyses of each AP–MS experiment and mapping all protein identifiers to Entrez Gene identifiers to minimize technical issues due to protein isoforms. Protein abundances in each immunoprecipitation were quantified using spectral counts averaged across technical replicates. The CompPASS algorithm34, 35 compared abundances of the proteins detected in each immunoprecipitation with their average levels across all other immunoprecipitations, returning a z score that quantified the extent to which a protein’s abundance exceeds its average levels across the dataset as well as the empirical NWD-score that accounted for a protein’s abundance, frequency of detection, and consistency across duplicate analyses. Subsequent filtering based on PSM counts, entropy scoring, and each protein’s frequency of detection within each batch of samples minimized false positives, liquid chromatography carryover, and technical artefacts. Putative bait–prey interactions were further filtered using CompPASS-Plus4, a naive Bayes classifier that learns to distinguish true interacting proteins from non-specific background and false positive identifications on the basis of CompPASS scores and several other metrics described previously. The algorithm modelled true interactions using examples from STRING36 and GeneMania37 databases. False positive protein identifications were modelled using decoy identifications that had survived previous filters. All remaining data were used to model background. Cross-validation was applied by batch, with each 96-well plate of immunoprecipitations scored using a model trained on ~57 different plates. Bait–prey interactions were then assembled across immunoprecipitations to produce a single network, combining scores of reciprocal interactions to increase their weight. BioPlex 2.0 was obtained by pruning this network to retain only those interactions that earned scores above 0.75, as described previously4. See Supplementary Table 1 for a list of baits as well as a complete list of interactions. BioPlex 2.0 interaction data were compared with data from BioGRID38, CORUM15, STRING36, GeneMania37, and MINT39 databases as described previously4. Because the BioPlex 2.0 dataset incorporates the contents of BioPlex 1.0 and data from this project have been deposited directly into BioGRID, released to the scientific community via the project website (http://bioplex.hms.harvard.edu), and otherwise distributed40 at intervals throughout the project, snapshots of these databases predating public disclosure of any BioPlex data were used to ensure that no interactions derived from BioPlex were included in the comparison. In Extended Data Fig. 1a, several data sources were used to determine the fractions of various protein families included as baits or preys in BioPlex 1.0 or 2.0. The list of human kinases was downloaded from kinase.com (http://kinase.com/web/current/human/; December 2007 update). Mitochondrial proteins were taken from MitoCarta 2.0 (ref. 41). Lists of transcription factors and chromatin-remodelling factors were drawn from http://www.bioguo.org. Drug target lists were taken from http://www.drugbank.ca. Cancer genes were taken from ref. 42. Disease genes were extracted from the curated set of disease–gene associations in the DisGeNET database25. ‘Essential’ genes were taken from recent papers describing clustered regularly interspaced palindromic repeat (CRISPR)–Cas9 screening to identify human genes that confer a fitness advantage6, 7. In each case, protein identifiers were converted to Entrez Gene identifiers, if necessary, and compared against those gene products included in either interaction network. Each of these analyses was performed exactly as described previously4. Brief summaries follow. Subcellular localization predictions relied upon localization information provided for a subset of proteins by the UniProt website (http://www.uniprot.org) in March 2016. These localization terms were manually condensed to 13 core localizations: nucleus, cytoplasm, cytoskeleton, endosome, endoplasmic reticulum, extracellular, Golgi, lysosome, mitochondrion, peroxisome, plasma membrane, vesicle, and cell projection. Fisher’s exact test was used to calculate the enrichment of each term among each protein’s primary and secondary neighbours, with multiple testing correction43. Predictions were made when enrichments were significant at an adjusted FDR of 1%. Localization predictions are provided in Supplementary Table 3. Domain–domain associations were uncovered by mapping PFAM domains onto the 56,553 protein–protein interactions in the BioPlex 2.0 network. After counting the numbers of interactions involving each domain individually and the number of interactions in which the domains were brought together within separate proteins, Fisher’s exact test was used to evaluate significance with subsequent correction for multiple hypothesis testing. Domains were considered significantly associated at an adjusted P value less than 0.01. Significant domain–domain associations are summarized in Supplementary Table 4. The enrichment of GO44 terms and PFAM22 domains was determined among each protein’s immediate neighbours and for each network community using Fisher’s exact test with multiple testing correction43. GO and PFAM data were downloaded from the UniProt website (http://www.uniprot.org) in March 2016. Only terms occurring at least twice were considered. Enrichments of GO terms and PFAM domains among each protein’s neighbours are summarized in Supplementary Table 5. The MCL algorithm5 was used to partition the BioPlex 2.0 network into communities of tightly interconnected proteins, using an implementation provided by the algorithm’s creator, S. van Dongen, at http://micans.org/mcl/. The option –force-connected=y was used to ensure that final clusters correspond to connected components. The MCL algorithm requires specification of one parameter, the inflation parameter, which controls the granularity of the clusters that are produced. Clustering of BioPlex 2.0 was repeated for several values of the inflation parameter between 1.5 and 2.5. After comparing experimentally derived clusters with known protein complexes, an inflation parameter of 2.0 was selected for final clustering. Clusters containing fewer than three proteins were discarded, producing a final list of 1,320 protein communities. Each cluster and its members are summarized in Supplementary Table 6; GO terms and PFAM domains enriched in each community are provided in Supplementary Table 7. One important question has been the extent to which each of the clusters observed in BioPlex 2.0 is also visible in BioPlex 1.0. To address this question, we mapped each cluster detected in BioPlex 2.0 onto the BioPlex 1.0 network. If a given cluster was also reflected in the BioPlex 1.0, then we would expect to see an enrichment of interactions; conversely, if interactions were not enriched among the relevant set of proteins above background, then there would be no evidence to support the indicated cluster. After mapping each cluster of tightly interconnected proteins from BioPlex 2.0 onto the BioPlex 1.0 network, we used a binomial test to evaluate the enrichment of BioPlex 1.0 interactions among matching proteins. The probability of interaction was estimated from the fraction of all possible interactions in the BioPlex 1.0 network that was actually detected (8.08 × 10−4); the number of trials was taken to be the maximum number of interactions possible among those proteins within the cluster that were part of the BioPlex 1.0 network; the number of interactions actually observed in this portion of BioPlex 1.0 was taken as the number of successes. A one-sided binomial test was performed and a correction for multiple testing was applied43. Overall, 45% of complexes detected in BioPlex 2.0 did not show any enrichment for protein interactions in BioPlex 1.0, suggesting that these were macromolecular complexes not covered in the first interaction network. Moreover, although the remaining 55% of complexes were at least partly reflected in BioPlex 1.0, the density of their coverage consistently increased with incorporation of additional AP–MS data into the BioPlex 2.0 network. In addition to using MCL clustering to partition the BioPlex 2.0 network into individual clusters of tightly interconnected proteins, we also wanted to explore patterns of interconnection within the network that related these clusters to each other. For this purpose, we searched for pairs of clusters that were connected to each other through interactions among their constituent proteins more often than would be expected. First, the full set of 56,553 interactions was trimmed to include only those interactions connecting one cluster with another, and the set of all cluster pairs connected by one or more interactions was identified. For each of these pairs of clusters, the number of interactions connecting the pair was determined, as were the numbers of interactions involving each cluster individually. Fisher’s exact test was used to identify pairs of clusters that were enriched for interactions among them, followed by multiple testing correction43. The 929 cluster–cluster associations that were accepted at a 1% FDR are displayed in Fig. 3a and Extended Data Fig. 9 and provided in Supplementary Table 6. GO and PFAM enrichments for each community are summarized in Supplementary Table 7. The first step towards examining network properties of fitness proteins was to combine lists of proteins associated with increased cellular fitness from refs 6, 7 into a single composite list. For our purposes, we used the union of both lists to define the set of fitness proteins. Entrez Gene identifiers were associated with proteins on this list and mapped onto the BioPlex 2.0 network. To assess network properties of fitness proteins, the composite list of proteins associated with increased cellular fitness was superimposed onto the BioPlex network, effectively subdividing all proteins in the network into two groups corresponding to fitness and non-fitness proteins. Vertex degrees, local clustering coefficients, and eigenvector centralities were then computed and averaged across all fitness proteins. To evaluate whether these values differed for fitness proteins compared with randomly selected protein subsets of equivalent size, fitness and non-fitness labels were scrambled across the network and a new average was calculated for the randomized list of fitness proteins. This process was repeated 10,000 times to define null distributions for each statistic. Since these distributions were normally distributed, Gaussian distributions were fitted to each and used to assign z scores and P values for each statistic associated with the true set of fitness proteins. To evaluate graph assortativity, the BioPlex network was subdivided into fitness and non-fitness proteins and the assortativity of the partitioned graph was calculated. This process was repeated 10,000 times, randomizing fitness and non-fitness labels, and the resulting distribution was fitted to a Gaussian distribution and used to determine a z score and P value associated with the true assortativity. A second goal was to identify clusters enriched with fitness proteins. For this purpose, a one-sided hypergeometric test was used to evaluate the enrichment of fitness proteins, taking into account the size of the cluster, the size of the BioPlex network, and the fraction of network proteins that were associated with increased cellular fitness. Only clusters containing two or more fitness proteins were considered for this analysis. Once a multiple testing correction43 was applied, 53 communities were found to be enriched with fitness proteins at a 1% FDR. These clusters are summarized in Extended Data Fig. 9. Levels of enrichment are summarized for those communities containing two or more cellular fitness proteins in Supplementary Table 8. To assess the tendency for clusters containing fitness proteins or enriched for fitness proteins to be centrally located within the cluster–cluster association network (Fig. 3a), all clusters were sorted according to their eigenvector centralities. The Kolmogorov–Smirnov test was used to compare distributions of clusters enriched and not enriched with fitness proteins within the ranked list of all clusters. This process was repeated to compare distributions of clusters containing multiple fitness proteins with clusters containing 0 or 1 fitness proteins, as shown in Fig. 3d. The basis for our study of protein complexes and disease was the DisGeNET database of disease–gene associations25. For our analysis we used the full database that relates over 16,000 genes with 13,000 partly redundant disease classifications. Each disease state and its associated proteins were then mapped onto each BioPlex 2.0 complex and evaluated for enrichment using a hypergeometric test, taking into account the size of the complex, the number of disease proteins in the complex, the number of disease proteins within the network, and the total network size. This process was repeated for each community and for each disease state. After multiple testing correction43, those complexes enriched with proteins involved with each disease at a 1% FDR were deemed associated. The resulting disease–complex associations were assembled into a network in which clusters and disease states are both represented as nodes, with edges connecting clusters with significantly associated disease states, depicted in full in Fig. 4a. All significant disease-cluster associations are provided in Supplementary Table 8. The eigenvector centralities assigned to disease states within the composite disease-community network were used to compare across a range of disease states. Disease classifications were taken from the DisGeNET database as reported in their SQLite download. All disease states in the network were ranked according to increasing eigenvector centrality. For each disease classification (for example, ‘neoplasms’), a Kolmogorov–Smirnov test was used to compare the distributions of matching and non-matching disease states within the entire ranked list. After multiple testing correction, disease states that appeared differentially distributed with respect to eigenvector centrality at a 1% FDR were identified and are highlighted in Fig. 4b. HEK293T cells were transfected with Flag–HA–GFP control plasmid, C13orf18–GFP, GFP–BECN1, or RUFY1–Flag–HA plasmids, and, after 48 h, cells were collected in lysis buffer (50 mM Tris pH 7.5, 150 mM NaCl, 1% NP-40), with protease and phosphatase inhibitors (Roche) on ice. Lysates were cleared by centrifugation, and subjected to affinity purification using anti-GFP antibodies (Chromotek, GFP–Trap, GTMA-20) or anti-Flag magnetic beads (Sigma-Aldrich, A2220)) for 2 h at 4 °C. Beads were washed four times with lysis buffer, and subsequently subjected to SDS–PAGE and immunoblotting with the following antibodies: BECN1 (Cell Signaling, clone D40C5), GFP (Roche, mouse IgG clones 7.1 and 13.1), C13orf18 (Proteintech, 21183-1-AP), and HA (Biolegend, clone HA.11). For validation of Hippo pathway interactions within BioPlex 2.0, we performed AP–MS experiments in MCF10A cells. Unlike HEK293T cells, MCF10A cells undergo contact inhibition and activate the Hippo signalling pathway; therefore we used cells under both sub-confluent and confluent conditions wherein YAP1 expulsion from the nucleus was verified by immunofluorescence (see section on ‘Clone construction and cell culture’). Affinity purification was performed essentially as described previously34, but eluted anti-HA immune complexes (Sigma-Aldrich, clone HA-7) were analysed in two ways. First, immune complexes for PDLIM7, MAGI1, YAP1, WWC1, NF2, and MPP5 (replicate 1) were subjected to LC–MS/MS analysis on an LTQ-Velos instrument and HCIPs identified using CompPASS34 in combination with a false positive background dataset derived in MCF10A cells45. The second replicate set for PDLIM7, MAGI1, YAP1, WWC1, NF2, and MPP5, as well as both replicates for PTPN14 and INADL, were processed identically to the first set except that the HA-eluted proteins were reduced and alkylated with DTT and iodoacetamide before trypsin digestion, and all the digested peptides corresponding to one sub-confluent and one confluent anti-HA immunoprecipitation were labelled heavy and light respectively, by reductive dimethylation46. Sub-confluent and confluent sample pairs corresponding to each bait were mixed to normalize the amount of bait present in each heavy and light fraction to 1:1 and analysed on an Orbitrap Elite Hybrid Ion Trap-Orbitrap Mass Spectrometer (ThermoFisher). Complexes from each growth condition were deconvolved using linear discriminant analysis parameters that filtered for either heavy-only or light-only labelled peptides. The heavy- or light-specific search results were subsequently imported into CompPASS for protein interaction analysis. Spectral count and CompPASS score data for the MCF10A dataset is provided in Supplementary Table 10. Anti-PTPN14 antibodies were from Sigma-Aldrich (GW21498A). We used CRISPR–Cas9 gene editing to knockout KIAA0196 using the gRNA sequence (GTCTAAGCCATTTAGACCAA) as described47. The KIAA0196 ORF (a gift from C. Clemen, University of Cologne) was cloned into pLenti-NTAP-IRES-Puro and expressed in KIAA0196−/− cells after selection using puromycin (1 μg ml−1). Immunoprecipitation with anti-Flag (Sigma-Aldrich, M2) antibodies, trypsinization, tandem mass tagging labelling, analysis by mass spectrometry, and quantification were performed as described previously4. Parallel immune complexes or whole-cell lysates were subjected to immunoblotting with anti-WASH1 (Sigma-Aldrich, SAB4200373), anti-KIAA0196 (Santa Cruz Biotechnology, sc-87442), anti-KIAA1033 (Bethyl Labs, A304-919A), anti-CCDC53 (Proteintech, 24445-1-AP), anti-PCNA (Santa Cruz Biotechnology, sc-56), or anti-actin (Santa Cruz Biotechnology, sc-69879) and immunoblot signals quantified using Protein Simple M in biological triplicate. HeLa cells (American Type Culture Collection) were plated on glass coverslips (Zeiss) and transiently transduced with lentiviral vectors expressing C-Flag–HA-tagged baits. At 48 h after infection, cells were fixed with 4% paraformaldehyde for 15 min at room temperature. Cells were washed in PBS, then blocked for 1 h with 5% normal goat serum (Cell Signaling Technology) in PBS containing 0.3% Triton X-100 (Sigma-Aldrich). Coverslips were incubated with anti-HA antibodies (mouse monoclonal, clone HA.11, BioLegend) or anti-HA plus anti-TOMM20 (rabbit polyclonal mitochondrial marker, Santa Cruz Biotechnology, clone FL-145, catalogue number 11415) for 2 h at room temperature in a humidified chamber. Cells were washed three times with PBS, then incubated for 1 h with appropriate Alexa Fluor-conjugated secondary antibodies (ThermoFisher). Nuclei were stained with Hoechst, and cells were washed three times with PBS and mounted on slides using Prolong Gold mounting media (ThermoFisher). All images were collected with a Yokogawa CSU-X1 spinning disk confocal scanner with Spectral Applied Research Aurora Borealis modification on a Nikon Ti-E inverted microscope using a 100 × Plan Apo numerical aperture 1.4 objective lens (Nikon Imaging Center, Harvard Medical School). Confocal images were acquired with a Hamamatsu ORCA-AG cooled CCD (charge-coupled device) camera controlled with MetaMorph 7 software (Molecular Devices). Fluorophores were excited using a Spectral Applied Research LMM-5 laser merge module with acousto-optic tuneable filter (AOTF)-controlled solid-state lasers (488 nm and 561 nm). A Lumencor SOLA fluorescence light source was used for imaging Hoechst staining. z series optical sections were collected with a step size of 0.2 μm, using the internal Nikon Ti-E focus motor, and stacked using MetaMorph to construct maximum intensity projections. We performed three major validation experiments using (1) analysis of a dozen bait proteins in both HCT116 colon cells and HEK293T cells to examine overlap in interaction partners, (2) reciprocal AP–MS experiments directed at interacting proteins for a set of 14-3-3 proteins, and (3) analysis of the PDLIM7–PTPN14–YAP1 adhesion network in MCF10A cells. As a validation approach, we selected 12 largely unstudied proteins displaying a range of interaction partners from 1 to 25 in HEK293T cells and performed AP–MS in HCT116 cells, a cell line of distinct tissue origin from HEK293T cells. After identification of HCIPs for proteins in HCT116 cells, we determined the interactions in common with HEK293T cells (Extended Data Fig. 1b–m). Over the 12 bait proteins identified, we observed 30–100% validation of interactions seen for individual baits in HEK293T cells. Cumulatively, this reflected an overall 60% validation (92 of 147 interactions seen in HEC293T cells were seen in HCT116). This rate of validation is comparable to that seen in focused studies examining F-box protein interactors in these two cell lines (51%)48. Thus, a substantial fraction of interactions seen in HEK293T cells are recapitulated in HCT116 cells. The 14-3-3 proteins represent a well-studied group of seven proteins (YWHAB, YWHAE, YWHAZ, YWHAH, YWHAQ, YWHAG, and SFN) that typically associate with phosphorylated proteins. Thirty-nine baits in BioPlex 2.0 were found to interact with one or more of these 14-3-3 proteins, with YWHAZ being detected most frequently (35 baits) and SFN being detected the least frequently (4 baits) (Extended Data Fig. 2). Seventeen of these proteins are not known to interact with 14-3-3 proteins on the basis of BioGrid. Because only the atypical 14-3-3 protein SFN had been targeted as a bait in BioPlex 2.0, the remaining six 14-3-3 proteins were submitted to our standard AP–MS pipeline using ORFeome 8.1 clones; while the clone for YWHAE failed at the sequence validation stage, the remaining five 14-3-3 proteins were processed successfully, identifying 130–360 HCIPs (Supplementary Table 2). While eight of 39 BioPlex 2.0 baits that had been observed to interact with one or more 14-3-3 proteins were not detected in HEK293T cells and thus may be impossible to detect in reciprocal immunoprecipitations, 63% of interactions eligible for reciprocal detection were confirmed (Extended Data Fig. 2a–c). This demonstrates that BioPlex 2.0 may reliably reveal novel reciprocally interacting partners even for proteins as well studied as 14-3-3 proteins. PTPN14 is a protein phosphatase that has recently been found to associate with several proteins within the Hippo pathway involving the transcription factor YAP1. The Hippo pathway is regulated by contact inhibition, and promotes YAP1 sequestration in the cytoplasm49. BioPlex 2.0 contains a highly connected group of proteins centred on PTPN14, MAGI1, MPP5, LIN7A/C, and INADL (Extended Data Fig. 2d). This network contained several interactions not seen in BioGrid. To validate these interactions, we performed an AP–MS analysis or immunoprecipitation–western analysis of PTPN14, MAGI1, MPP5, PDLIM7, INADL, WWC1, NF2, and YAP1 after stable expression in MCF10A cells in both sub-confluent and confluent states. This series of experiments strongly validated interactions seen in HEK293T cells (Extended Data Fig. 2d, f) with 65% of eligible interactions being seen in both cell lines, further validating our method and the ability of BioPlex 2.0 to robustly identify interactions. Furthermore, 63% of interactions identified in both BioPlex 2.0 and MCF10A cells were novel, having not been previously described in several previous interaction profiling experiments (Extended Data Fig. 2g). Overall, these three lines of study indicate the ability of BioPlex 2.0 to identify interactions that can be validated reciprocally or in other cell lines. The BioPlex 2.0 network and its underlying data are available in several formats. First, all interactions in the BioPlex network have been deposited in the BioGRID protein interaction database. Second, we have created a website devoted to the project (http://bioplex.hms.harvard.edu) which provides tools to download (1) the interactions that make up BioPlex 1.0 and 2.0, (2) a customized viewer that enables browsing of either network to examine the interactions of specific proteins, (3) an interface for download of nearly 12,000 individual RAW files containing mass spectrometry data from individual AP–MS experiments, and (4) an R package and web-based tool for performing CompPASS analyses. Third, the BioPlex 2.0 network as bait–prey pairs has been incorporated into NDEx40, a web-based platform for biological Network Data Exchange. Fourth, our RAW files have been submitted for inclusion in ProteomicsDB50. Finally, all RAW files (3 Tb) from this study will be provided to investigators upon request using investigator-provided hard drives. Finally, a table in.tsv format containing all proteins and spectral count information for all 5,891 AP–MS experiments reported here is available for download at the BioPlex website. All other data are available from the corresponding authors upon reasonable request.
News Article | May 3, 2017
bioMONTR Labs is proud to announce that its Laboratory Director, Dr. Susan Fiscus, has been named as the 2017 recipient of the Ed Nowakowski Senior Memorial Clinical Virology Award presented by the Pan American Society for Clinical Virology. This accolade is awarded to individuals who have made a major impact on the epidemiology, treatment or understanding of the pathogenesis of viral diseases. Prior to joining bioMONTR Labs, Dr. Fiscus served as the director of UNC’s Retrovirology Core Laboratory. Preceding her 25-year career at UNC, Dr. Fiscus earned her bachelor’s degree from Bates College, her master’s degree in botany from Duke University and her doctoral degree in microbiology from Colorado State University. During her time at UNC, Fiscus worked on studies for the AIDS Clinical Trials Group (ACTG), the Pediatric ACTG, the HIV Prevention Trials Network, and the Centers for Disease Control (CDC) among other organizations. Each year, PASCV’s awards program provides an array of awards that recognize the contributions made by individuals to the field of clinical virology. These awards will be presented next week during the 2017 Clinical Virology Symposium in Savannah, Georgia. bioMONTR Labs is a privately owned CLIA Certified lab located in Research Triangle Park, NC. The company offers a comprehensive menu of high-complexity molecular assays as well as other proprietary and esoteric molecular testing. Please visit http://www.biomontr.com for more information on the company, its management, test menu, and capabilities.
Pan N.,Core Laboratory |
Frome W.L.,Core Laboratory |
Dart R.A.,Center for Human Genetics |
Tewksbury D.,Marshfield Clinic Research Foundation
Clinical Medicine and Research | Year: 2013
Background: The renin-angiotensin system (RAS) is present in human placental tissue and participates in regulation of maternal-fetal blood flow during pregnancy. RAS expression in placental tissue is regulated by various hormones and is altered in various disease conditions. An in vitro system is needed to further investigate regulation of the placental RAS. To this end, we studied RAS expression in the human placenta-derived cell line, CRL-7548. Methods: CRL-7548 cells were cultured in plastic plates. Total RNA was extracted, reverse transcribed, and amplified by polymerase chain reaction (PCR) with specific primers. Angiotensin II peptide in the culture media was measured by radioimmunoassay. Renin activity was detected by radioimmunoassay measuring angiotensin I generated. Angiotensin receptor type I was detected by Western blot. Results: Specific mRNA for angiotensin, renin, angiotensin converting enzyme, and angiotensin receptor type I was detected by real-time PCR. Renin activity was detected in the placental cell lysate, and angiotensin II peptide, the final product of the RAS system, was detected in cell culture media by radioimmunoassay. Angiotensin receptor type I was identified as a 41 kDa protein in cell lysates by Western blot. Conclusions: These results demonstrate that all necessary components of the classic RAS are expressed in the human placental cell line CRL-7548. This cell line may prove useful as an in vitro system for studying RAS regulation in the placenta. ©2013 Marshfield Clinic.
News Article | December 9, 2016
Last April, Omar and Natasha Rajani rented a hall, invited 130 guests, and hired a magician to entertain the little ones. In Natasha’s family, first birthday parties are major celebrations. And the Rajanis, who live in Toronto, felt particularly enthusiastic because for a long time they weren’t sure they’d ever be able to throw one. Natasha, 35, struggled for four years to get pregnant. She and Omar, 40, tried naturally at first; then they used hormones, which led to an ectopic pregnancy, in which the fertilized egg implants outside the uterus—usually in the narrow fallopian tube—and must be removed. Then more hormones. Then in vitro fertilization (IVF). Nothing worked. Natasha’s obstetrician next offered an unusual option: the couple could try a new method meant to improve the odds of IVF, offered by a Boston-area company called OvaScience. The approach, called Augment (for Autologous Germline Mitochondrial Energy Transfer), is so far available only in Canada and Japan (OvaScience hasn’t yet sought approval from U.S. regulators). It required the doctor to gather cells from one of Natasha’s ovaries and harvest their mitochondria—the tiny power plants that fuel our cells. These extracted mitochondria would then be injected into one of her eggs along with her husband’s sperm, and the embryo would be transferred to her uterus during a standard IVF procedure. According to OvaScience, the extra energy from the ovarian mitochondria would give her egg a boost, promoting fertilization. “What Natasha and I liked about it was it was kind of like self-treatment,” says Omar. “We thought that it was something that was safe, and it was almost like the body treating and healing itself. We were very, very excited about the opportunity to try it.” In the round of IVF that Natasha had after trying the new procedure, she got pregnant with a boy, Zain, now almost two. It doesn’t really matter, the Rajanis say, whether Augment was the reason for the successful pregnancy. All they know is that it felt like a miracle. They have a toddler with an always-sunny disposition—“He’s just an absolute joy of a child,” Natasha says—and two more frozen embryos that might one day become his siblings. Whether Augment actually made the difference in Zain’s conception could have far-reaching implications for how we think about both infertility and aging. Infertility affects more than 10 percent of American women—a number that is rising as many women wait longer before considering parenthood. Female fertility starts to decline after age 35. Among women who turn to assisted reproduction techniques such as IVF, only 40 percent of attempts by those under 35 result in a live birth, while 2 percent of those among women over 44 do—largely because of a dwindling number of eggs and a decline in their quality. Not only could OvaScience’s procedure help many women whose fertility has declined with age, but it would be one of the first successful efforts to slow the body’s relentlessly ticking clock, providing tantalizing clues for ways to halt aging more generally. Company cofounder and Harvard University genetics professor David Sinclair says conquering the overall aging process is a matter of when, not if. “We are at a point where we know how to extend life span in mammals, and now there’s a race to see who can prove that we can do this in humans,” Sinclair says. Female fertility, he says, is one of the first bodily systems to break down with age, and he sees reversing infertility as a gateway to reversing aging itself. The goal, Sinclair proclaims, is “to have revolutionary technologies like OvaScience available to everybody—and not to just treat fertility, but another 2,000 age-related diseases, from diabetes through Alzheimer’s.” Despite Sinclair’s enthusiasm, it’s possible—even likely, some scientists say—that OvaScience’s procedure did nothing at all. For one thing, IVF is notoriously unpredictable. The Rajanis might have just gotten lucky the second time, just as they were unlucky the first. More than a dozen interviews with experts in fertility and early development reveal little scientific justification for what was done to Natasha Rajani’s eggs and those of the 300 other women who have gone through the procedure, which costs an IVF clinic from $6,000 to $7,000. (The fee that clinics charge patients will vary.) The company harvests the mitochondria from what it believes are immature egg cells found in the ovarian lining; the idea is that these so-called egg-precursor cells have fresher mitochondria than the aging mature eggs. But there is little convincing evidence that they are what OvaScience says they are: cells with the power to turn into eggs. And even if such egg-precursor cells exist and their mitochondria are more youthful than those in a woman’s eggs, does it prove that such an energy boost can improve fertility? “There is very little data supporting the benefit of these procedures, and often the biological rationale is incoherent,” says Jacob Hanna, an expert in embryonic stem cells at the Weizmann Institute of Science in Israel, who reviewed OvaScience’s information at the request of MIT Technology Review. “I hope the company can provide solid data and experimentation on these approaches… It sounds more at the moment like voodoo, or alchemy.” So is OvaScience leading a breakthrough in battling one of the most basic processes of aging, or selling false hopes with little scientific justification? Youthful Marriage The founding of OvaScience came about as a marriage of two of medicine’s most audacious and often controversial areas: anti-aging research and infertility research. The company specifically traces its scientific origins to the work of the reproductive biologist Jonathan Tilly, now at Northeastern University in Boston. Beginning with a 2004 paper, Tilly has been challenging decades of scientific dogma that girls are born with their whole life’s supply of “primordial” egg cells, which will eventually mature into eggs. After puberty, this stock of eggs matures at the rate of about one a month, and it never renews. The decline in female fertility around 35 occurs as this supply dries up, and menopause strikes when the eggs run out. But Tilly’s research suggested—first in mice and then in people—that the lining of the ovary contains the makings of a new supply. If Tilly is right about his conclusions, solving infertility might be just a matter of finding these egg-precursor cells and triggering them to mature (see 10 Breakthrough Technologies 2012: Egg Stem Cells). Sinclair says it was natural for him to collaborate with Tilly, who was then at Harvard. Tilly’s work touched on subjects that fascinated Sinclair: how the body ages and what might be done to slow that process. “I’d been trying to figure out what are the major reasons we grow old and why don’t cells function the older we get,” Sinclair says. Sinclair introduced Tilly to two biotech entrepreneurs, Rich Aldrich and Michelle Dipp, with whom Sinclair had previously run an anti-aging company called Sirtris Pharmaceuticals. That company was based on Sinclair’s research into sirtuins, proteins that may slow the aging process and can be activated by resveratrol, a compound most found in red wine. Sirtris was sold to GlaxoSmithKline in 2008 for $720 million (GSK closed down its Sirtris facility in 2013, absorbing the sirtuin work into its own research efforts), and the biotech investors were looking for their next big play. When the potential partners asked Tilly how he might commercialize his research, Sinclair says, Tilly came up with the idea of Augment, using the precursor cells to rejuvenate aging eggs. (Tilly declined to comment for this story.) That was enough for the group to create OvaScience, where Dipp served as CEO until last summer. Sinclair hypothesizes that mitochondria are crucial to aging. The idea is simple. Aging cells have old, slow mitochondria; young mitochondria equal young cells. Hence the Augment program to rejuvenate eggs with mitochondria from cells that are younger and more energetic. Sinclair has also cofounded two other companies, MetroBiotech of Boston and CohBar of Menlo Park, California, to develop drugs related to mitochondrial functions. CohBar hopes peptides made by mitochondria could be useful against diabetes, obesity, and Alzheimer’s, among other diseases, while MetroBiotech is pursuing a therapy to treat diseases associated with malfunctioning mitochondria. It is testing a drug that boosts levels of nicotinamide adenine dinucleotide, NAD, a compound involved in energy metabolism in the mitochondria. “The same molecules [in the drug] we think will treat aging itself,” Sinclair says, citing a 2013 paper his team published in Cell. Sinclair’s interest in aging has become personal. Now 47 and working in a high-stress job at Harvard, he has time to exercise “barely more than once a week.” In addition to his academic and commercial duties, he also sits on the advisory board of InsideTracker, a company based in Cambridge, Massachusetts, that uses levels of glucose, vitamin D, and other blood factors to determine a client’s “inner age,” as opposed to the chronological one. In 2011, Sinclair says, he clocked in at 57, a decade and a half beyond his actual age. In July 2015, convinced he was going to die young, he upped his daily doses of resveratrol. He also added MetroBiotech’s NAD precursor, which has yet to be tested in people and is too expensive for anyone who’s not making it to use. Sinclair says InsideTracker’s aging markers now put him at 31. He’s lost the weight he’d been carrying since college and has been allowing himself to eat dessert again, because his body can handle it. (Weight loss isn’t his goal, he says, but mitochondria are also responsible for burning fat, so weight loss “might be a side effect” of the treatment.) “The results in mice and my single-person experiment indicate that aging is more reversible than we thought,” he says. Too Early In a pristine lab overlooking a busy highway in the Boston suburbs, OvaScience researchers identify and count what they believe are egg-precursor cells. These constitute, OvaScience says, about 6 percent of the cells on the surface of the ovarian cortex. In the Augment procedure, an IVF surgeon laparoscopically removes a section of this layer about half the size of a dime. The tissue is shipped to an OvaScience lab, where the mitochondria are extracted and shipped back to the fertility clinic. Just before fertilization, the mitochondria are inserted into the egg alongside the sperm. Then IVF proceeds as usual. Preliminary data suggests that the procedure improves fertility. In its latest study, released at a conference in November, OvaScience reported a 31 percent success rate among 75 patients who had undergone at least one previous round of IVF before trying Augment. It’s notoriously difficult to get good data on fertility clinic results, but in a 2015 study in the Journal of the American Medical Association, British researchers found that about 30 percent of women are successful in their first round of IVF and 16 to 25 percent are successful in each subsequent round (without Augment). So if the results for Augment prove to be real, it increases success rates from about 20 percent to 30 percent per round—a significant, if modest, improvement. However, those results simply record the experience of Augment patients. As is the case in many early research studies, they were not compared with controls, so there’s no convincing evidence that the procedure made the difference. OvaScience expects to get data from two more trials, including about 300 patients, in the second half of 2017. However, OvaScience’s patents on the cells and procedures protect the company’s business interests and prevent outsiders from testing its protocol. So there have been no independent tests. I asked one scientist to examine and comment on OvaScience’s Augment research. After looking at the material the company had presented to me, he declined to say anything. There wasn’t any science to review, he said—just anecdotes. OvaScience plans two other projects for these egg-precursor cells. In a program it’s calling OvaPrime, the cells are extracted from the outer rind of the ovary, isolated, and then reimplanted into the main part of the ovary, where they are projected to mature into healthy, viable eggs. The procedure is designed to help women who don’t make enough eggs—about 30 percent of infertile women, according to the Centers for Disease Control and Prevention. The company is doing safety and feasibility trials now and expects to soon decide whether to pursue this approach commercially. In another program, called OvaTure, OvaScience hopes eventually to perform IVF without hormones. Hormones are now needed to stimulate a woman’s body to release as many eggs as possible. But for many women, hormone injections are the worst part of IVF, with the potential to cause mood swings, nausea, vomiting, abdominal pain, and a very small risk of death. With OvaTure, the woman would have some precursor cells removed, and they would be coaxed in a lab dish to mature into fully functional eggs, all without hormones. The company, however, is still studying whether this technique will work. These projects will largely determine just how important OvaScience’s contribution to fertility and anti-aging science will be. Augment might have a limited effect even if the precursor egg cells are not truly capable of turning into eggs, as many scientists believe. And Stock says at around $7,000 per treatment, Augment is a good deal if it saves families from another round of IVF, which can easily run $10,000 to $15,000 per cycle. But the two more ambitious efforts, OvaPrime and OvaTure, will never work unless Tilly’s conclusions are right. His research was roundly criticized by colleagues in 2004, and his later publications did not erase the skepticism. Mice may very well have these egg-precursor cells, several scientists say. But large, long-lived animals are quite different from mice in terms of reproduction—and Tilly hasn’t yet convinced other researchers that women carry around cells capable of extending their fertility. Still, more scientists are coming around to the possibility that egg-precursor cells exist, says Evelyn Telfer, a reproductive biologist at the University of Edinburgh. Initially quite dubious of Tilly’s findings, she changed her mind after touring his lab, welcoming him into her own, and working with the egg-precursor cells herself. “As with all things that are new, it takes time to get into the consciousness of people,” says Telfer, who now collaborates with OvaScience. A small study she has recently finished suggests that egg-precursor cells may help women regenerate their egg supply after experiencing a catastrophe, like chemotherapy for cancer. “It’s an observation we’ve made, and we have to do a lot more work to find out what these cells are doing to the ovary and why we’re seeing an increased number of eggs,” she says. Regardless of what these cells are, the dozen scientists interviewed—most of whom didn’t want their names associated with the company—questioned the idea of using them to “rejuvenate” older eggs. It’s not scientifically obvious that adding extra energy to egg cells would make them more fertile. Carol Hanna, a staff scientist for the Assisted Reproductive Technology Core Laboratory at the Oregon Health & Science University in Portland, says she and others in the field truly hope that Tilly’s science is accurate, but they feel it shouldn’t have moved so quickly to commercialization. “I think a lot of people fall in that middle—they want to believe it but haven’t seen that one piece of information that convinces them,” she says. Renee Reijo Pera, a reproductive and stem-cell biologist at Montana State University, is even more blunt: “Almost everybody thinks that the commercial side of the whole enterprise got way out ahead of the science.” In most areas of medicine other than fertility, it’s standard practice to prove that something works before offering it to patients. Regulations in many countries, however, allow fertility clinics to try a procedure first and test it years later. As a result, dozens of so-called add-on procedures to IVF are available to women with very little scientific justification. Industry leaders defend this approach; the first test-tube baby would never have been born if there had been more regulations. But this lack of rigorous oversight also makes patients vulnerable to abuse, says Carl Heneghan, director of the University of Oxford’s Centre for Evidence-Based Medicine. “The sheer number of treatments that are available tells you they all can’t work,” suggests Heneghan. “People will try anything. That’s where the problem starts.” But there actually aren’t many alternatives available to infertile couples, says Jake Anderson-Bialis, a venture capitalist turned fertility advocate who cofounded the patient community FertilityIQ. International adoptions have become much more difficult; IVF is costly and puts women on a hormonal roller coaster; and buying another woman’s eggs if their own are too old can add $30,000 or more to that cost. Anderson-Bialis says he doesn’t blame OvaScience for taking its products to market before the science is firmly established. The infertility business has always been that way. And in his view, the problem of infertility is so big that it justifies some risk-taking. Improving the odds This has been a busy few months for OvaScience. In 2016, the company signed on seven new clinics in Canada and Japan, bringing its total to nine worldwide. Harald Stock, who jumped from the board into the CEO’s chair in July, says company officials have begun speaking with the U.S. Food and Drug Administration to explore what it would take to bring Augment to the market in the United States. He will soon decide whether to proceed with the OvaPrime and OvaTure programs. And the company, which had more than $130 million in cash as of September 30, decided to move away from its initial business plan of installing small labs in each of the clinics that use its products, instead relying on a centralized lab, which is cheaper and easier for quality control. Launching a product and a company takes time and personnel, so Stock says he’s committed to moving slowly and deliberately. “We need to stay disciplined to not get overwhelmed,” he says. “We’re still a 100-some-person company and can’t be everywhere.” The company has chosen to build its business in Canada first, because it can cover most of the country from just a few cities, Stock says, meaning there’s no need for a massive sales force. He’s waiting to start marketing until enough clinics have been trained, so that anyone who wants Augment can get it. IVF is a growing business. It’s projected to expand from about $10 billion today to $22 billion globally by 2020. Augment, he says, could help women who fail to get pregnant in a first round of IVF. A bigger prize for the company could be in its other projects. OvaPrime could make it possible for women who lack viable eggs to have biological children, he says. And anyone undergoing IVF would prefer to skip the hormones. In the end, though, OvaScience’s market may not turn out to be very big. IVF has been getting markedly better over the last few years. And freezing embryos and even eggs, which costs about the same as IVF plus an annual storage fee of $500 to $1,000, has recently made it much easier for women to preserve high-quality eggs into their late 30s and 40s. It’s the age of the egg—not the woman—that seems to matter: women in their 40s fare just as well as younger women if the quality of their frozen eggs is high, says Hal Danzer, cofounder of the Southern California Reproductive Center, a fertility clinic in Beverly Hills, California. Freezing embryos, meanwhile, allows labs to select those that are most likely to succeed, and transfer them after the hormones needed to stimulate egg production have left the body. Improved IVF success rates leave less room for Augment to shine. Still, boosting the odds even somewhat will entice some prospective parents. Danzer says his patients, many of whom put off parenthood for their careers, are desperate to get pregnant. He has referred several patients to clinics in Canada so they can try Augment, though when asked whether he’d use it in his own clinic, he says: “I think it’s a little too early to say.” Karen Weintraub is a freelance health and science writer in Cambridge, Massachusetts. This story was updated to include additional detail about ongoing studies of Augment.
News Article | December 23, 2016
In findings they call counterintuitive, a team of UCLA-led researchers suggests that blocking a protein, which is crucial to initiating the immune response against viral infections, may actually help combat HIV. Findings from a study in animals appear to demonstrate that temporarily blocking a type of protein, called type I interferon, can restore immune function and speed up viral suppression during treatment with anti-viral drugs for people with chronic infection of the virus that causes AIDS. This is the first study to show the role that type I interferon plays in driving the body's immune destruction during HIV infection, said Scott Kitchen, associate professor of medicine in the division of hematology/oncology at the David Geffen School of Medicine at UCLA and senior author of the study published in the peer-reviewed Journal of Clinical Investigation. "This finding is completely counterintuitive, because many believe that the more interferon at work, the better," said Kitchen, a member of the UCLA AIDS Institute. "We show that the type of interferon being produced during chronic stages of HIV infection has detrimental effects on the body's ability to fight off HIV and other types of infection or cancer and could actually be contributing to accelerated HIV disease." HIV cripples the immune system by destroying immune cells called CD4 T cells, which are activated during early HIV infection by type I interferon. CD4 T cells are also known as "helper" cells because they signal another type of T cell, the CD8, to destroy HIV-infected cells. Also, HIV evades the body's CD8 cells by constantly mutating, escaping recognition by CD8 cells and making them ineffective. The chronically heightened state of inflammation and activation eventually leads to what is known as immune exhaustion when the immune cells can no longer function properly to clear infected cells. This, along with the loss of CD4 T cells ultimately leads to the destruction of the immune system. The researchers' idea is to block type I interferon to reduce chronic activation of the immune cells, which could give the exhausted CD8 T cells the opportunity to restore their abilities to fighting strength. Combine that with antiretroviral therapy and it may be possible to both restore immune function and eradicate HIV throughout the body. The researchers used "humanized mice," which have had their immune systems replaced with human immune system cells, thymus tissue and bone marrow. They treated HIV-infected mice with antibodies that blocked type I interferons, which allowed the mice's immune systems to revert from the state of exhaustion. This made it possible for their immune systems to produce sufficient amounts of CD8 T cells that were primed to attack and kill HIV-infected cells. When combined with antiretroviral therapy, the treatment accelerated the effect of antiretroviral therapy in suppressing HIV. "We found -- counterintuitively -- that blocking this immune response against the virus had beneficial effects in lowering the amounts of virus and increasing the ability of the immune response to clear out the virus," said Kitchen, who is also director of the UCLA Humanized Mouse Core Laboratory. Kitchen noted that these findings offer a proof of principle in a humanized mouse system and are not definitive. More experiments are needed in non-human primates before moving on to human clinical trials to determine whether the researchers' theory holds up and this treatment is safe in humans. But the findings offer a new perspective into the function of type I interferon during untreated and treated HIV chronic infection, said Anjie Zhen, a postdoctoral scholar and member of the UCLA AIDS Institute who led the study. "This could have profound implications for the development of therapies that include such approaches as interferon alpha therapy," Zhen said. "This shows that a proper balance is required when administering this type of therapy, where too much can have detrimental effects in suppressing important immune responses." Study co-authors are Valerie Rezek, Cindy Youn, Brianna Lam, Nelson Chang, Jonathan Rick, Mayra Carrillo, Heather Martin, Saro Kasparian, Philip Syed, and Nicholas Rice of UCLA, and David Brooks of the Princess Margaret Cancer Center in Toronto, Canada and of the University of Toronto. Grants from the National Institutes of Health (AI078806, AI110306-01, AI085043, T32AI060567), the UCLA AIDS Institute (P30AI28697), the California Center for Regenerative Medicine (TR4-06845), the UC Multicampus Research Program and Initiatives, the California Center for Antiviral Drug Discovery, the California HIV/AIDS Research Program (F12-LA-215) and the UCLA Center for AIDS Research (AI28697) funded this study.
News Article | October 31, 2016
INDIANAPOLIS, Oct. 31, 2016 (GLOBE NEWSWIRE) -- The Indiana Biosciences Research Institute (IBRI) today announced Michael Pugia, Ph.D., as a Research Fellow and Director of the new Bioanalytics Core Laboratory. Pugia comes to the IBRI following a successful 30-year career in the biomedical in-vitro diagnostic industry. There he contributed to more than 20 new product launches for Bayer and Siemens and spent 15 years as a director of research and development working on next generation analytical and diagnostic technologies in collaboration with leading institutions and companies. His primary research interest is the development of single-cell bioanalytical technology for proteomic biomarkers discovery in the fields of endocrinology and oncology. “Creating new capabilities that enable IBRI researchers and others to do unique research is a key focus for us,” said David Broecker, President and CEO of the IBRI. “Establishing the new single-cell, bioanalytics core laboratory will provide researchers with tools to isolate individual cells for evaluation under a variety of different biological conditions to identify new targets for the development of novel diagnostics and therapeutics.” “The technology goal for the new core laboratory is to combine pioneering microfluidics methods and systems for single cell isolation alongside next generation mass spectrometry and immune and molecular assays,” said Pugia. “Joining the IBRI will enable me to create something that is extremely novel, building off my experiences and research interests. In my early discussions with other IBRI researchers and corporate stakeholders, I have found tremendous support for my plans and ideas in establishing these capabilities.” In 2009 Pugia was awarded the Siemens Inventor of the Year for his work on a miniaturized “lab-on-a-chip” diagnostic tool. He also was recognized with 9 Bayer Science and Technology Awards including the Outstanding Bayer Technology Award, the Bayer Corp Quality Excellence Award, and the Near Patient Testing Segment, General Manager Award for Exceptional Leadership. The American Association of Chemistry honored him as the Samuel Natelson Senior Investigator in recognition of outstanding service for the advancement of clinical chemistry, and as the winner of the 1st Annual AI Free Memorial Lectureship. Pugia holds 367 U.S. and foreign patents and has 72 pending patents, and has 55 manuscripts, 13 book chapters and hundreds of conference papers and lectures in a wide variety of chemistry disciplines to his name. He earned his Ph.D. in chemistry from Texas Tech University and his bachelor’s degree in chemistry from Clarkson University. While working in industry, he has held adjunct positions as a Visiting Scholar at the University of Notre Dame and as a Clinical Research Professor at the University of Louisville Medical School. Zane Baird will join Pugia’s lab as Staff Scientist. He graduated from Purdue University in 2016 with a Ph.D. in analytical chemistry and holds a bachelor’s degree in chemistry from Southern Utah University. The Indiana Biosciences Research Institute (IBRI) is an independent, nonprofit discovery science and applied research institute focused on innovation targeting cardio-metabolic diseases, diabetes and poor nutrition. Inspired by the state and Indiana’s leading life sciences companies, research universities and philanthropic community, the IBRI is building a world-class organization of researchers, innovators, and entrepreneurs that will catalyze scientific discovery and its application, resulting in improved health outcomes for patients. For more information about IBRI and donation or collaboration opportunities, please visit www.indianabiosciences.org.