date: 2009-04-10
title: Findings of scientific misconduct.

# Findings of Scientific Misconduct

Notice Number: NOT-OD-09-082

Key Dates**
**Release Date: April 9, 2009

Issued by**
**Department of Health and Human Services

Notice is hereby given that the Office of Research Integrity (ORI) and the Assistant Secretary for Health have taken final action in the following case:

Robert B. Fogel, M.D., Harvard Medical School and Brigham and Women's Hospital: Based on information that the Respondent volunteered to his former mentor on November 7, 2006, and detailed in a written admission on September 19, 2007, and ORI's review of Joint Inquiry and Investigation reports by Harvard Medical School (HMS) and the Brigham and Women's Hospital (BWH), the U.S. Public Health Service (PHS) found that Dr. Robert B. Fogel, former Assistant Professor of Medicine and Associate Physician at HMS, and former Co-Director of the Fellowship in Sleep Medicine at BWH, engaged in scientific misconduct in research supported by National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), awards P50 HL60292, R01 HL48531, K23 HL04400, and F32 HL10246, and National Center for Research Resources (NCRR), NIH, award M01 RR02635.

PHS found that Respondent engaged in scientific misconduct by falsifying and fabricating baseline data from a study of sleep apnea in severely obese patients published in the following paper: Fogel, R.B., Malhotra, A., Dalagiorgou, G., Robinson, M.K., Jakab, M., Kikinis, R., Pittman, S.D., and White, D.P. \`\`Anatomic and physiologic predictors of apnea severity in morbidly obese subjects.' Sleep 2:150-155, 2003 (hereafter referred to as the \`\`Sleep paper'); and in a preliminary abstract reporting on this work.

Specifically, PHS found that for the data reported in the Sleep paper, the Respondent:

Changed/falsified roughly half of the physiologic data

Fabricated roughly 20% of the anatomic data that were supposedly obtained from Computed Tomography (CT) images

Changed/falsified 50 to 80 percent of the other anatomic data

Changed/falsified roughly 40 to 50 percent of the sleep data so that those data would better conform to his hypothesis.

Respondent also published some of the falsified and fabricated data in an abstract in Sleep 24, Abstract Supplement A7, 2001.

Dr. Fogel has entered into a Voluntary Settlement Agreement in which he has voluntarily agreed, for a period of three (3) years, beginning on March 16, 2009:

\(1\) To exclude himself from serving in any advisory capacity to PHS, including but not limited to service on any PHS advisory committee, board, and/or peer review committee, or as a consultant;

\(2\) That any institution that submits an application for PHS support for a research project on which the Respondent's participation is proposed or that uses the Respondent in any capacity on PHS supported research, or that submits a report of PHS-funded research in which the Respondent is involved, must concurrently submit a plan for supervision of the Respondent's duties to the funding agency for approval; the supervisory plan must be designed to ensure the scientific integrity of the Respondent's research contribution; a copy of the supervisory plan must also be submitted to ORI by the institution; the Respondent agrees that he will not participate in any PHS-supported research until such a supervisory plan is submitted to ORI; and

\(3\) To ensure that any institution employing him submits, in conjunction with each application for PHS funds or report, manuscript, or abstract of PHS-funded research in which the Respondent is involved, a certification that the data provided by the Respondent are based on actual experiments or are otherwise legitimately derived and that the data, procedures, and methodology are accurately reported in the application or report. The Respondent must ensure that the institution sends the certification to ORI.

FOR FURTHER INFORMATION CONTACT:**
** Director**
** Division of Investigative Oversight**
** Office of Research Integrity**
** 1101 Wootton Parkway, Suite 750**
** Rockville, MD 20852**
** (240) 453-8800.


author: Govindarajan Rajagopalan; Yogish C. Kudva; Chella S. DavidCorresponding author: Chella S. David, <firstname.lastname@example.com>
date: 2012-07
references:
title: Is HOT a Cool Treatment for Type 1 Diabetes?

Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by immune-mediated destruction of the insulin-producing β-cells of the islets of Langerhans that results in life-long insulin dependence (1). Given the immunological nature of the disease, numerous antigen-specific as well as nonantigen-specific tolerance induction or immune deviation strategies have been developed as treatments for T1D. Although successful in experimental models, results of studies to translate these strategies to humans have been discouraging (2–5). This has prompted researchers to explore safer, broader, and more effective immunotherapeutic approaches to prevent, treat, and/or revert T1D. In this issue of *Diabetes*, Faleo et al. (6) show compelling data regarding use of hyperbaric oxygen therapy in autoimmune diabetes using nonobese diabetic (NOD) mice. This model spontaneously develops T1D, a feature that closely mimics human disease. Faleo et al. (6) show that hyperbaric oxygen therapy (HOT) significantly protects from T1D when initiated early in the disease course, but not after its onset, suggesting that this approach could be useful in high-risk individuals.

According to the Undersea and Hyperbaric Medical Society, HOT is a process wherein patients breathe 100% oxygen (atmospheric air contains \~21% oxygen) while inside a treatment chamber at a pressure higher than at sea level (usually 2.5 times greater pressure). In 1662, Henshaw, a British clergyman, first used pressurized atmospheric air to treat certain ailments. Since that time, HOT has been used to treat various conditions such as decompression sickness, arterial gas embolism, and carbon monoxide poisoning (with or without cyanide poisoning), and to facilitate wound healing (7). It should be noted that inhalation of 100% oxygen at 1 normal atmosphere of pressure or exposing isolated parts of the body to 100% oxygen does not constitute HOT.

Faleo et al. (6) exposed prediabetic NOD mice to pressurized 100% oxygen (HOT-100%), 21% oxygen (HOT-21%), or oxygen-depleted air supplemented with pure oxygen (HOT-12%) for 60 min every day for varying periods of time. T1D risk was assessed using different models. In the cyclophosphamide-accelerated model, more than 75% of control, HOT-12%, and HOT-21% mice developed hyperglycemia, compared with less than 50% of HOT-100% mice. The severity of insulitis was also reduced in the HOT-100% group. Even though HOT was not administered on the day of cyclophosphamide therapy, the possibility that HOT could have influenced the metabolism of cyclophosphamide (8), and thereby indirectly influenced the incidence of accelerated hyperglycemia, was not addressed by the authors.

In the spontaneous model, when HOT was initiated at 4 weeks of age, 85% of untreated control NOD mice developed hyperglycemia by 35 weeks compared with 65% of mice in the HOT-100% group. Although this finding was statistically significant, a considerable percentage—65%—of this inbred, homogenous mouse strain still developed hyperglycemia with HOT-100%, an observation that raises questions about the likely effectiveness of HOT among more heterogeneous human T1D populations. Data from a key experiment—the effect of HOT-100% on the incidence of hyperglycemia when administered close to onset of diabetes in NOD mice (13 weeks of age)—were not reported. In this group, HOT-100% attenuated insulitis and significantly delayed the onset of hyperglycemia when administered along with glucagon-like peptide (GLP)-1 analog and exenatide (EXN) delivered by mini-osmotic pumps. Inclusion of a group receiving HOT-100% and vehicle or a scrambled peptide, instead of EXN, would have strengthened the inference that HOT-100% is more effective when combined with EXN. Overall, the findings in this report suggest that only prolonged exposure to HOT-100% that is initiated at a very young age reduced T1D risk in this mouse model.

The mechanisms by which HOT provided its beneficial effects in T1D or even in other models of inflammation are not fully understood. HOT can suppress inflammation by modulating the expression of integrins (9) and probably other adhesion molecules. This would interfere with homing of inflammatory cells to islets, thereby reducing insulitis. In support of this hypothesis, HOT-100%–treated mice had reduced insulitis. Furthermore, expression of CD62L, a lymphocyte homing marker, was altered in the spontaneous, but not cyclophosphamide-accelerated, T1D model.

HOT could modulate inflammation through hypoxia-inducible factor (HIF)-1 (10). Hypoxia increases HIF expression, whereas hyperoxia represses HIF. HIF-1 can induce interleukin (IL)-17 and inhibit Foxp3^+^ regulatory T-cell (Treg) development (11). Therefore, by reducing the expression of HIF-1 (12,13), HOT could inhibit the Th17 pathway and promote Treg development, thereby protecting from T1D. Although HIF was not investigated in this report, increased Treg in HOT-100% mice supports this theory. However, it remains to be elucidated whether HOT-100% mediates its effects directly through increasing Treg numbers and functions and/or through suppressing IL-17. HIF is also induced rapidly in transplanted islets and promotes islet apoptosis (14,15). Therefore, repression of HIF by HOT may promote β-cell survival and play a beneficial role in diabetes. Reduced apoptosis and increased proliferation of β-cells in HOT-100% mice support this theory. Paradoxically, HOT can also increase HIF expression (16), and HIF can have anti-inflammatory activities in T1D (17). Therefore, the interplay between HOT and HIF in the T1D settings remains to be determined and is a topic ripe for further investigation.

HOT is also known to increase levels of reactive oxygen species (ROS) as well as reactive nitrogen species (RNS) (18). Both ROS and RNS participate in innumerable physiological and pathological processes (19). Of relevance, ROS are known to damage β-cells and participate in inflammatory processes (20), which could be counterproductive in T1D patients receiving HOT. Therefore, the roles of ROS and RNS in HOT need to be thoroughly investigated. Furthermore, this study showed that HOT-100% suppressed the responses of T cells to a potent mitogenic stimulation (anti-CD3) and elevated the levels of IL-10. Induction of such generalized immune suppression by HOT could be a potential drawback in humans, particularly in children, because this might predispose to infections, diminish the ability to clear infections, and mount adequate protective immunity after immunizations.

In conclusion, this descriptive work by Faleo et al. (6) addresses the potential benefits of HOT in T1D. Molecular oxygen is a fundamental component of various biochemical processes, and its use as a therapeutic agent could have diverse effects (21), both positive and negative. A detailed investigation is warranted to understand its mechanisms of actions as well as possible side effects (Fig. 1). In T1D, HOT is ineffective once the autoimmunity has progressed to a prediabetic stage, making its utility in patients with existing T1D questionable. When started early in the disease course, HOT reduced the incidence of T1D only by \~20% in NOD mice. However, in humans, this could be a significant number. Therefore, given the established safety and limited side effects of HOT in humans, its translation to a pilot human clinical trial, either alone or with other immunomodulatory agents, at least in high-risk individuals (those with a first-degree relative with T1D or who express susceptible HLA alleles, etc.), is a possibility.

## ACKNOWLEDGMENTS

No potential conflicts of interest relevant to this article were reported.

# REFERENCES


date: 2004
title: Editorial

Following an overwhelming vote by the US House of Representatives urging the National Institutes of Health (NIH) to develop an Open Access strategy, the NIH has recently invited comment on its plans to enhance access to the research that it funds. Under the proposed scheme, NIH-funded researchers would have to provide electronic copies of the final accepted versions of each of their manuscripts, for archiving along with any supplementary information in PubMed Central \[<http://www.pubmedcentral.nih.gov>\]. Six months after publication of the research in question - or sooner if the publisher agrees - the provisional copies would be made publicly available at no charge to readers.

*Journal of Biology* heartily supports the NIH proposal, which brings us one step closer to the immediate availability of all peer-reviewed research free of charge. Indeed, as Open Access pioneers, BioMed Central and *Journal of Biology* already provide PubMed Central with final full text and PDF versions of all published research articles immediately, and we encourage all publishers to follow suit. In an ideal future, the electronic version of each research article would be the final and definitive form - easily archived, centrally searchable and available at the click of a mouse to all who would read it, be they scientists or members of the public. Of course, print would still play an important role, but printed articles will no longer constitute the historic record of the work. And moving away from the printed article in favor of its online incarnation makes sense for other reasons too: electronically, researchers can display all relevant data instead of an edited subset, and moving images and other web-only formats can be easily integrated. It will no longer be possible to represent a complete research article accurately on the printed page.

In keeping with this shift in emphasis, *Journal of Biology* is now presenting its printed articles in a new light. Since its inception, the journal has eschewed the rigidity of producing regular issues, instead producing collections of articles grouped by their focus on a key piece of research, not necessarily by publication date. Each printed collection serves to draw readers' attention to the definitive online content, which is freely available on the journal's website or in central archives such as PubMed Central. In recognition of the non-traditional way that *Journal of Biology* has always conceptually clustered its articles, the cover of the bound printed articles now sports a bold new design - do please look out for it in print and on the journal's website.

Since its launch in June 2002, nearly a million copies of *Journal of Biology* have been distributed in print to life scientists worldwide, entirely free of charge. But the journal and its publisher, BioMed Central, are committed to building a sustainable Open Access business model. This should primarily ensure that research articles are immediately and permanently available online without charge, as well as being deposited into permanent repositories - both of which provide far more efficient ways of disseminating, retrieving, and searching for scientific information. For this reason, the journal has now changed its approach to print distribution. Some readers will continue receiving complimentary copies as before, either with *The Scientist* or individually, but for others who wish to receive articles in print, a modest annual subscription charge of \$50 (£30/€40) will be levied - see \[<http://jbiol.com/subscribe>\]. These funds will help to maintain the quality of the reprints and to cover mailing costs. The journal also offers special print rates to librarians and institutions, so please encourage your library or head of institute to sign up. Of course, all of the content of *Journal of Biology* will continue to be available free of charge online.

*Journal of Biology* urges other funding bodies and policy makers to follow the lead of the NIH, the UK Parliament, the Howard Hughes Medical Institute, The Wellcome Trust and the signatories of the Berlin Declaration \[<http://www.zim.mpg.de/openaccess-berlin>\], and to encourage the researchers they fund to publish their results in a way that promotes public availability of scientific information. At the same time, it should be recognized that we still have a long way to go before every research article is free for anyone to read online on the day it is published. You can play a part in achieving this goal by submitting your next important article to *Journal of Biology*.


abstract: Decreased collateral vessel formation in diabetic peripheral limbs is characterized by abnormalities of the angiogenic response to ischemia. Hyperglycemia is known to activate protein kinase C (PKC), affecting the expression and activity of growth factors such as vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF). The current study investigates the role of PKCδ in diabetes-induced poor collateral vessel formation and inhibition of angiogenic factors expression and actions. Ischemic adductor muscles of diabetic *Prkcd*^+/+^ mice exhibited reduced blood reperfusion, vascular density, and number of small vessels compared with nondiabetic *Prkcd*^+/+^ mice. By contrast, diabetic *Prkcd*^−/−^ mice showed significant increased blood flow, capillary density, and number of capillaries. Although expression of various PKC isoforms was unchanged, activation of PKCδ was increased in diabetic *Prkcd*^+/+^ mice. VEGF and PDGF mRNA and protein expression were decreased in the muscles of diabetic *Prkcd*^+/+^ mice and were normalized in diabetic *Prkcd*^−/−^ mice. Furthermore, phosphorylation of VEGF receptor 2 (VEGFR2) and PDGF receptor-β (PDGFR-β) were blunted in diabetic *Prkcd*^+/+^ mice but elevated in diabetic *Prkcd*^−/−^ mice. The inhibition of VEGFR2 and PDGFR-β activity was associated with increased SHP-1 expression. In conclusion, our data have uncovered the mechanisms by which PKCδ activation induced poor collateral vessel formation, offering potential novel targets to regulate angiogenesis therapeutically in diabetic patients.
author: Farah Lizotte; Martin Paré; Benoit Denhez; Michael Leitges; Andréanne Guay; Pedro GeraldesCorresponding author: Pedro Geraldes, <firstname.lastname@example.com>.
date: 2013-08
references:
title: PKCδ Impaired Vessel Formation and Angiogenic Factor Expression in Diabetic Ischemic Limbs

The main long-term complications from diabetes are vascular diseases, which are in turn the main causes of morbidity and mortality in diabetic patients (1). Diabetic vascular complications affect several important organs, including the retina, kidney, and arteries (2,3). Peripheral vascular diseases are the major risk factor for nontraumatic lower limb amputation in patients with diabetes (4), characterized by collateral vessel development insufficient to support the loss of blood flow through occluded arteries in the ischemic limbs (5). Multiple abnormalities in the angiogenic response to ischemia have been documented in the diabetic state and depend on complex interactions of multiple growth factors and vascular cells.

Experiments to improve angiogenesis and vascular cell survival by local infusion of vascular endothelial growth factor (VEGF) or angiopoietin by increasing its expression have also been reported in nondiabetic animal models (6,7). Moreover, animal studies have used platelet-derived growth factor (PDGF) to improve collateral vessel formation and vascular healing in the diabetic state (8). Clinical trials using recombinant growth factors have noted transient improvement of myocardial and distal leg circulation (9–11). However, these favorable vascular effects appeared to produce limited clinical benefits (12). Local administration of growth factors, such as VEGF by gene therapy in the setting of diabetes, does not appear to have the beneficial long-term effects seen in the absence of diabetes or to improve quality of life (13,14). One potential problem with normalizing VEGF or PDGF action alone is that a variety of growth factors may be needed to establish and maintain the capillary bed.

Various studies have clearly identified that the expression of growth factors, such as VEGF, PDGF, and stromal-derived factor-1 (SDF-1), are critically important in the formation of collateral vessels in response to ischemia (15–17). Previous studies suggested that hyperglycemia attenuates VEGF production and levels in myocardial tissue and in animal models of wound repair (5,18). Furthermore, decreased VEGF and PDGF expression in the peripheral limbs and nerves of diabetic animals and rodents has been reported (19–21). Although the underlying mechanism of reduction of VEGF and PDGF expression in diabetes is not clear, it is well-known that the major inducers of VEGF and PDGF (i.e., hypoxia and oxidants) can both play a role in diabetes. We and other researchers have reported that variation in PDGF signaling, rather than expression, is linked to morphological abnormalities in the retina and in collateral capillary formation in an ischemic limb model of diabetic animals (22,23). Clearly, poor collateral vessel formation during diabetes-induced ischemia is attributable to the lack of production and/or action of critical growth factors such as VEGF and PDGF. Therefore, further studies of the basic mechanisms of hyperglycemia-induced activation of toxic metabolites, such as activation of protein kinase C (PKC), are needed to identify how these proteins contribute to growth factor deregulation.

PKC, a member of a large family of serine/threonine kinases, is involved in the pathophysiology of vascular complications. When activated, PKC phosphorylates specific serine or threonine residues on target proteins that vary, depending on cell type. PKC has multiple isoforms that function in a wide variety of biological systems (24). PKC activation increases endothelial permeability and decreases blood flow and the production and response of angiogenic growth factors that contribute to the loss of capillary pericytes, retinal permeability, ischemia, and neovascularization (25–29).

Previous data have demonstrated that high glucose levels in smooth muscle cells activate PKCα, -β, -δ, and -ε but not the atypical PKCζ (30,31). In general, high levels of glucose-induced PKC activation cause vascular dysfunction by altering the expressions of growth factors such as VEGF, PDGF, transforming growth factor-β, and others (32–34). PKCδ has been proposed to participate in smooth muscle cell apoptosis, and deletion of this PKC isoform led to increased arteriosclerosis (35). Moreover, we previously demonstrated that diabetes-induced PKCδ activation generates PDGF unresponsiveness, causing pericyte apoptosis, acellular capillaries, and diabetic retinopathy (23). We therefore hypothesized that PKCδ activation could be involved in proangiogenic factor inhibition that triggers poor collateral vessel formation in diabetes.

# RESEARCH DESIGN AND METHODS

## Reagents and antibodies.

Primary antibodies for immunoblotting were obtained from commercial sources, including actin (horseradish peroxidase \[HRP\]; I-19), SHP-1 (C19), VEGF (147), PKCα (C-20), PKCβ (C-18), PKCε (C-15), and nitric oxide synthase (NOS) 3 (C-20) antibodies from Santa Cruz Biotechnology Inc.; phospho (p)-tyrosine, p-PKCδ (Thr 505), PKCδ, p-VEGF receptor 2 (VEGFR2; Y1175), VEGFR2, p-PDGF receptor-β (PDGFR-β; Tyr 1009), and PDGFR-β antibodies from Cell Signaling; anti-α smooth muscle actin from Abcam; polyclonal antibody against protein-tyrosine phosphatase 1B (PTP1B) and CD31 from BD Bioscience; SHP-2 and SHP-1 antibodies from Millipore; and rabbit and mouse peroxidase-conjugated secondary antibody from GE Healthcare Bio-Sciences. All other reagents used, including EDTA, leupeptin, phenylmethylsulfonyl fluoride, aprotinin, and Na~3~VO~4~, were purchased from Sigma-Aldrich (St. Louis, MO) unless otherwise stated.

## Animal and experimental design.

C57BL/6J mice (6 weeks old) were purchased from The Jackson Laboratory (Bar Harbor, ME) and bred in our animal facility. *Prkcd*^−/−^ mice, described previously and provided by Dr. Michael Leitges (35), were generated by the insertion of a LacZ/neo cassette into the first transcribed exon of the PKCδ gene. This insertion abolished the transcription of PKCδ, leading to a null allele. *Prkcd*^−/−^ mice with mixed background of 129SV and C57BL/6J strains were crossbred for 10 generations (F12) with wild-type C57BL/6J background from The Jackson Laboratory. Animals were rendered diabetic for a 2-month period by intraperitoneal streptozotocin injection (50 mg/kg in 0.05 mol/L citrate buffer, pH 4.5; Sigma) on 5 consecutive days after an overnight fast; control mice were injected with citrate buffer. Blood glucose was measured by Glucometer (Contour, Bayer Inc.). Throughout the study period, animals were provided with free access to water and standard rodent chow (Harlan Teklad, Madison, WI). All experiments were conducted in accordance with the Canadian Council of Animal Care and University of Sherbrooke guidelines.

## Hind limb ischemia model.

We assessed blood flow in nondiabetic and *Prkcd*^+/+^ and *Prkcd*^−/−^ mice diabetic for 2 months. Animals were anesthetized, and the entire lower extremity of each mouse was shaved. A small incision was made along the thigh all the way to inguinal ligament and extending superiorly toward the mouse abdomen. The femoral artery was isolated from the femoral nerve and vein and ligated distally to the origin of the arteria profunda femoris. The incision was closed by interrupted 5-0 sutures (Syneture).

## Laser Doppler perfusion imaging and physical examination.

Hind limb blood flow was measured using PIM3 laser Doppler perfusion imaging (Perimed Inc.). Consecutive perfusion measurements were obtained by scanning the region of interest (hind limb and foot) of anesthetized animals. Measurements were performed before and after artery ligation and on postoperative days 7, 14, 21, and 28. To account for variables that affect blood flow temporally, the results at any given time were expressed as a ratio against simultaneously obtained perfusion measurements of the right (ligated) and left (nonligated) limb. Tissue necrosis was scored to assess mice that had to be killed during the course of the experiment due to necrosis/loss of toes.

## Histopathology and TUNEL assay.

Right and left abductor muscles from *Prkcd*^+/+^ and *Prkcd*^−/−^ mice were harvested for pathological examination. Sections were fixed in 4% paraformaldehyde (Sigma-Aldrich) for 18 h and then transferred to 90% ethanol for light microscopy and immunohistochemistry. Paraformaldehyde-fixed tissue was embedded in paraffin, and 6-µm sections were stained with hematoxylin and eosin (Sigma). Apoptotic cells were detected using the TACS 2 Tdt-Fluor in situ apoptosis detection kit (Trevigen, Gaithersburg, MD) according to the manufacturer's instructions.

## Immunofluorescence.

Adductor muscles were blocked with 10% goat serum for 1 h and exposed in sequence to primary antibodies (CD31 and α-smooth muscle actin, 1:100) overnight, followed by incubation with secondary antibodies Alexa-647 conjugated anti-rabbit IgG and Alexa-594 conjugated anti-mouse (1:500; Jackson ImmunoResearch Laboratories). Confocal images were captured on a Zeiss LSM 410 microscope; images of one experiment were taken at the same time under identical settings and handled in Adobe Photoshop similarly across all images.

## Immunoblot analysis.

Adductor muscles were lysed in Laemmli buffer (50 mmol/L Tris \[pH 6.8\], 2% SDS, and 10% glycerol) containing protease inhibitors (1 mmol/L phenylmethylsulfonyl fluoride, 2 μg/mL aprotinin, 10 μg/mL leupeptin, 1 mmol/L Na~3~VO~4~; Sigma). Protein amount was measured with a BCA kit (Bio-Rad). The lysates (10–20 µg protein) were separated by SDS-PAGE, transferred to polyvinylidene fluoride membrane, and blocked with 5% skim milk. Antigens were detected using anti-rabbit HRP-conjugated antibody for other Western blotting and detected with the ECL system (Pierce Thermo Fisher, Piscataway, NJ). Protein content quantification was performed using computer-assisted densitometry with Image J software (National Institutes of Health).

## Real-time PCR analysis.

Real-time PCR was performed to evaluate mRNA expressions of PKCα, PKCβ, PKCδ, PKCε, VEGF, PDGF, KDR/Flk-1, PDGFR-β, endothelial NOS (eNOS), SDF-1, FGF2, SHP-1, SHP-2, and PTP1B of nonischemic and ischemic limbs. Total RNA was extracted from adductor muscles with TRI-REAGENT, as described by the manufacturer and RNeasy mini kit (Qiagen, Valencia, CA). The RNA was treated with DNase I (Invitrogen) to remove any genomic DNA contamination. Approximately 1 μg RNA was used to generate cDNA using SuperScript III reverse transcriptase and random hexamers (Invitrogen) at 50°C for 60 min. PCR primers and probes are listed in [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1432/-/DC1). Glyceraldehyde-3-phosphate dehydrogenase and 18S ribosomal RNA expression were used for normalization. PCR products were gel purified, subcloned using a QIA quick PCR Purification kit (Qiagen), and sequenced in both directions to confirm identity.

## Nuclear extract and nonradioactive transcription factor assay.

Adductor muscles were lysed and nuclear-specific proteins isolated using the NucBuster Protein Extraction Kit (Novagen, Madison, WI) according to the manufacturer's instructions. Detection of hypoxia-inducible factor-1α (HIF-1α) in the nucleus was quantified using the nonradioactive transcription factor assay kit (Cayman, Ann Arbor, MI). Briefly, nuclear protein (20 µg) was incubated for 24 h in a 96-well plate containing immobilized specific double-stranded DNA consensus sequence of the HIF-1α response element. HIF-1α contained in the nuclear extract was linked specifically to the HIF-1α response element. Wells were washed five times, and the HIF transcription factor complex was detected by addition of a specific primary antibody directed against HIF-1α and incubated for 1 h. Wells were washed five times and exposed with secondary antibody conjugated to HRP for 1 h. Wells were then washed five times, and developing agent was added to provide a sensitive colorimetric readout at 450 nm (Infinite M200; Tecan Group Ltd., Männedorf, Switzerland) to quantify nuclear HIF-1α levels.

## Statistical analyses.

The data are shown as mean ± SD for each group. Statistical analysis was performed by unpaired *t* test or by one-way ANOVA, followed by the Tukey test correction for multiple comparisons. All results were considered statistically significant at *P* \< 0.05.

# RESULTS

## Deletion of PKCδ improved reperfusion and vascular response ischemia on diabetic limbs.

Nondiabetic and diabetic male *Prkcd*^−/−^ mice and control littermates (*Prkcd*^+/+^) underwent unilateral right femoral artery ligation. Body weight and fasting glucose levels were measured when mice were killed ([Supplementary Table 2](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1432/-/DC1)). Blood flow reperfusion was assessed using the PIM 3 laser Doppler imaging system (Fig. 1A). Diabetic *Prkcd*^+/+^ mice exhibited reduced blood reperfusion of the ischemic limb compared with nondiabetic *Prkcd*^+/+^ mice (*P* = 0.0046). In contrast, reperfusion of blood flow of diabetic *Prkcd*^−/−^ mice was significantly improved (*P* = 0.0003) compared with diabetic *Prkcd*^+/+^ mice and similar to nondiabetic *Prkcd*^+/+^ and *Prkcd*^−/−^ mice 28 days after the ligation (Fig. 1B). Because diabetic patients are at high risk of lower limb amputation, we assessed limb necrosis and apoptosis. Impaired reperfusion in ischemic limbs of diabetic *Prkcd*^+/+^ mice was associated with elevated tissue necrosis, amputation (Fig. 1C), and apoptosis (Fig. 2) compared with nondiabetic counterparts.

One main effect of hypoxia is to induce angiogenesis and to promote new capillary formation. To test whether activation of PKCδ is responsible for poor collateral vessel formation in diabetes, we measured capillary density and capillary diameter in the ischemic adductor muscles. Figure 3 demonstrated that the adductor muscles of diabetic *Prkcd*^+/+^ mice displayed a 31% vascular density reduction compared with nondiabetic *Prkcd*^+/+^ mice. The decline of capillary density was accompanied with a 50% reduction in number of vessels with a diameter of 50 µm or less. Interestingly, diabetic *Prkcd*^−/−^ mice showed a significant increase in capillary density and number of vessels with a diameter of less than 50 µm compared with diabetic *Prkcd*^+/+^ mice (Fig. 3D).

## PKCδ is activated in diabetic ischemic limb.

Hyperglycemia is known to activate multiple PKC isoforms, preferably the β and δ isoforms, in vascular cells. Expression of various isoforms of PKC was assessed by quantitative PCR in muscle tissues (Fig. 4). Compared with nondiabetic *Prkcd*^+/+^ mice, mRNA expression of PKCβ and δ was modestly increased in adductor muscles of diabetic *Prkcd*^+/+^ mice and unchanged in *Prkcd*^−/−^ mice (Fig. 4B and D). There was no significant difference in the mRNA expression of PKCα and -ε (Fig. 4A and C). Although diabetic *Prkcd*^+/+^ mice did not exhibit higher levels of protein expression of PKCα, -β2, -ε, or -δ isoforms, adductor muscles of *Prkcd*^+/+^ mice showed a significant and impressive increase of PKCδ phosphorylation (Thr 505; *P* = 0.0040), as a marker of PKCδ activation, 28 days after unilateral femoral artery ligation compared with nondiabetic *Prkcd*^+/+^ mice (Fig. 5).

## Inhibition of PKCδ promotes proangiogenic growth factor expression and activation.

To explain how the absence of PKCδ improved reperfusion in diabetic ischemic limbs, we performed a wide analysis of the gene and protein expression of angiogenic-related factors and their receptors. Quantitative gene expression analyses by real-time PCR indicated that VEGF-A, PDGF-B, and PDGFR-β mRNA expression was significantly decreased in the adductor muscles of diabetic mice by 46, 30, and 63%, respectively, compared with nondiabetic *Prkcd*^+/+^ mice (Fig. 6A, C, and D). The reduction of these genes in diabetic *Prkcd*^+/+^ mice was not observed in diabetic *Prkcd*^−/−^ mice. Moreover, mRNA expression of VEGFR2 (KDR/Flk-1), PDGF-B, and PDGFR-β was significantly upregulated in diabetic *Prkcd*^−/−^ compared with diabetic *Prkcd*^+/+^ mice (Fig. 6B–D). These results suggest that impaired PDGF and VEGF expression by PKCδ activation might be the contributing factor for poor collateral vessel formation in diabetes. Expression of other angiogenic factors, such as SDF-1, FGF-2, and eNOS, as well as transcriptional factor activity of HIF-1α, was unchanged within all groups of mice (Fig. 6E–H and [Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1432/-/DC1)). In contrast to 4 weeks after femoral artery ligation, transcriptional factor activity and mRNA levels of HIF-1α were significantly decreased in diabetic *Prkcd*^+/+^ mice compared with nondiabetic *Prkcd*^+/+^ and diabetic *Prkcd*^−/−^ mice ([Supplementary Figs. 2 and 3](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1432/-/DC1)).

## VEGFR2 and PDGFR-β activation is decreased in diabetic ischemic muscles.

To further investigate the mechanisms of impaired angiogenic response to restore blood flow in diabetes, the expression, activation, and signaling pathway of VEGF-A and PDGF-B and their respective receptors (VEGFR2 and PDGFR-β) were examined. Protein expression of PDGF-B was significantly decreased in diabetic versus nondiabetic adductor muscles of wild-type animals. In contrast, VEGF-A and PDGF-B protein expression was elevated in the ischemic limb of the diabetic PKCδ null mice (Fig. 7A and B). Phosphorylation of VEGFR2 and PDGFR-β was inhibited in ischemic adductor muscles of diabetic mice compared with nondiabetic *Prkcd*^+/+^ mice. However, activation of Src was elevated in adductor muscles of diabetic *Prkcd*^+/+^ mice compared with nondiabetc *Prkcd*^+/+^ and *Prkcd*^−/−^ mice (Fig. 7B). Interestingly, tyrosine phosphorylation of VEGFR2 and PDGFRβ, as well as PLCγ1, Akt, and ERK phosphorylation, was greatly enhanced in *Prkcd*^−/−^ mice compared with diabetic *Prkcd*^+/+^ mice (Fig. 7A and B). We did not observe any changes in the eNOS protein expression among experimental groups (Fig. 7A).

## Expression of SHP-1 caused VEGFR2 and PDGFR-β inactivation.

We have previously shown that activation of PKCδ leads to increased expression of SHP-1, which inhibits the PDGF-signaling pathway and promotes retinal pericyte apoptosis in diabetic animals. To determine whether SHP-1 is implicated in PKCδ-induced VEGFR2 and PDGFR-β dephosphorylation in diabetic ischemic adductor muscles, we measured SHP-1 expression by quantitative PCR and immunoblot analysis. Figure 8A and B indicates that mRNA expression of SHP-1, but not SHP-2 or PTP1B, is elevated in diabetic *Prkcd*^+/+^ mice, whereas SHP-1 is clearly downregulated in *Prkcd*^−/−^ mice. We confirmed through immunoblot analysis that SHP-1 protein expression was elevated by 2.3-fold in ischemic adductor muscles of diabetic *Prkcd*^+/+^ mice compared with nondiabetic *Prkcd*^+/+^ mice. The increase expression of SHP-1 was not observed in diabetic *Prkcd*^−/−^ mice (Fig. 8C). No change was detected in the protein expression of SHP-2 and PTP1B within all groups of mice (Fig. 8D).

# DISCUSSION

Diabetes is associated with the progression of vascular complications, such as peripheral arterial disease, and is a major risk factor for lower limb amputations (4). In the current study, we have demonstrated that activation of PKCδ diminishes the expression of VEGF and PDGF, two critical proangiogenic factors contributing to poor capillary formation and blood flow reperfusion of the ischemic limbs. In addition to reducing expression of VEGF and PDGF, phosphorylation of VEGF and PDGF receptors was abrogated in diabetic ischemic muscles compared with nondiabetic ischemic muscles. The inhibition of growth factor receptor phosphorylation was associated with the upregulation of SHP-1 expression, which has been reported to deactivate tyrosine kinase receptors such as VEGF and PDGF receptors. Overall, deletion of PKCδ prevents the reduction of VEGF and PDGF expression and re-establishes KDR/Flk-1 and PDGFR-β phosphorylation, favoring new capillary formation and blood flow reperfusion.

Wound healing is a complex, well-orchestrated, and dynamic process that involves a coordinated and precise interaction of various cell types and mediators. Given the fundamental contribution of VEGF and PDGF to the angiogenic process, the mechanism by which activation of PKCδ isoform prevents growth factors expression and signaling actions may provide a better understanding of how diabetes reduces collateral vessel formation in the ischemic limb. In this study, we demonstrated that PKCδ is activated in diabetic ischemic muscles and reduced blood flow reperfusion, contributing to tissue necrosis, amputation, and apoptosis. Previous studies have reported that PKCδ is involved in vascular cell apoptosis. PKCδ activates p-38, mitogen-activated protein kinase, p53, and caspase-3 cleavage to favor endothelial (36) and smooth muscle cell apoptosis (37,38). Therefore, deletion of PKCδ may enhance vascular cell migration and proliferation, two significant steps in the formation of new blood vessels.

Total expression of PKC isoform in ischemic muscles was slightly affected by diabetes, probably because mRNA and protein analyses were performed 28 days after femoral artery ligation. However, phosphorylation of PKCδ on threonine 505, a phosphorylation site within the activation loop, clearly suggests that PKCδ is activated in the muscles of diabetic ischemic limbs compared with nondiabetic muscles. Previous data showed that the inhibition of PKCδ, using an isozyme-specific peptide, improved the number of microvessels and cerebral blood flow after acute focal ischemia in normotensive rats (39). Our data demonstrate that deletion of PKCδ restores blood flow perfusion in diabetic ischemic muscles by promoting the number of capillaries and reducing tissue apoptosis.

The reduction of VEGF and PDGF receptor expression and the downstream signaling pathway is associated with impaired angiogenesis process in diabetic foot ulcer and ischemic diseases. Our results indicate that diabetes-induced PKCδ activation decreases VEGF, PDGF, KDR/Flk-1, and PDGFR-β mRNA expression in the ischemic limb, which is completely restored in PKCδ-null mice. Interestingly, impaired angiogenic response in ischemic arterial diseases of type 1 and type 2 diabetes is associated with VEGF inhibition in endothelial cells and monocytes (13,40). It is possible that the ablation of PKCδ may also affect VEGF signaling in monocytes, which may contribute to vessel formation abnormalities. However, this assumption will need further investigation.

HIF-1α is a master regulator of angiogenic factors in response to tissue hypoxia. Previous study showed that HIF-1α gene transfer increased recovery of limb perfusion, increasing eNOS activation and vessel density (41). In our study, however, the increase in the expression of VEGF in muscles of PKCδ-deficient mice may not have been entirely due to upregulation of HIF-1α. Because protein extraction was performed 4 weeks after the femoral artery ligation, it is possible that the expression of HIF-1α could have returned to basal levels. This hypothesis is supported by results obtained 2 weeks after the surgery. Our data demonstrated that HIF-1α transcriptional factor activity and mRNA expression were increased in nondiabetic and diabetic PKCδ-null mice 2 weeks only after surgery ([Supplementary Figs. 2 and 3](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1432/-/DC1)). Besides VEGF and PDGF expression, our data suggest that PKCδ activation disrupts VEGF and PDGF signaling, whereas in PKCδ-deficient mice, the activity of VEGFR2, PDGFR-β, PLCγ1, Akt, and ERK is enhanced. Surprisingly, Src phosphorylation was increased in the ischemic muscles of diabetic wild-type mice even if PDGFR-β activity was reduced. However, a previous study reported that reactive oxygen species (ROS) production induced Src phosphorylation (42). Because ROS are massively produced in ischemic and hyperglycemic conditions, it is probable that ROS production is responsible for the Src phosphorylation seen in diabetic wild-type mice.

There is strong evidence that progenitor cell recruitment and homing participate in angiogenesis and wound repair, which are guided by SDF-1 (43). Although the number of progenitor cells is reduced in diabetic mice, inadequate progenitor cell mobilization has been proposed as one potential mechanism of impaired angiogenesis (44). However, our results did not observe any change in SDF-1 expression in PKCδ-null mice, suggesting that mobilization and local trafficking of progenitor cells to the ischemic site was not affected by the PKCδ isoform.

Despite advances in revascularization techniques, limb salvage and pain relief cannot be achieved in many diabetic patients with diffuse peripheral vascular disease. VEGF-mediated gene therapy has shown promising results as an innovative method in the treatment of severe cardiovascular diseases. However, a randomized study of gene therapy failed to meet the primary objective of significant amputation reduction (45). During the 10-year follow-up period, no significant differences were detected in the number of amputations or causes of death with the use of transient VEGF-A–mediated gene therapy. One reason for this lack of improvement is perhaps because neovascularization requires the interaction of multiple growth factors that can promote, in a synergic manner, new and mature blood vessels. Enhancing the responsiveness of diabetic vascular cells to proangiogenic factors may offer a potential new approach to treat peripheral arterial diseases. Protein tyrosine phosphatase is a group of proteins that is critical in abating cell response to growth factors by inhibiting tyrosine kinase phosphorylation. Our results demonstrated that SHP-1 expression was increased in diabetic ischemic muscles and was responsible for VEGF and PDGR receptor dephosphorylation.

Although not significant, a slight rise in SHP-2 (18%) and PTP1B (37%) expression was observed in diabetic PKCδ-null mice. Previous studies have shown that PDGF activation enhanced SHP-2 and PTP1B activity (46,47), which may explain our results. We have reported that activation of PKCδ induces the expression of SHP-1 in cultured pericytes exposed to high glucose concentrations and inhibits the PDGF signaling pathway contributing to pericyte apoptosis (23). Others studies have also shown that SHP-1 is a negative regulator of VEGF signal transduction and inhibits endothelial cell proliferation (48,49). Interestingly, silencing SHP-1 increased phosphorylation of KDR/Flk-1 and markedly enhanced capillary density in a nondiabetic hind limb ischemia model (50). However, our current study does not provide a direct link between SHP-1 expression and reduced angiogenesis, which will require further investigations. Nevertheless, our findings have identified PKCδ, and potentially SHP-1, as potential therapeutic targets for the treatment of diabetic peripheral arterial diseases and cardiovascular complications.

In summary, we have provided evidence that PKCδ is activated by diabetes in ischemic muscles and induced SHP-1 expression, contributing to VEGF and PDGF unresponsiveness and poor angiogenesis response. Although various therapies are partly successful in restoring blood flow to the affected tissues, there is no effective strategy to specifically produce new functional vessels to dismiss diabetic ischemic stress. Our data enhance our understanding of the mechanisms underlying poor collateral vessel formation induced by PKC activation and may offer potential novel targets to regulate angiogenesis therapeutically in patients with diabetes.

## ACKNOWLEDGMENTS

This study was supported by grants from the Canadian Diabetes Association, Fonds de Recherche du Québec–Santé, and Diabète Québec to P.G. and was performed at the Centre de Recherche Clinique Étienne-Le Bel, a research center funded by the Fonds de Recherche du Québec–Santé. P.G. is currently the recipient of a Scholarship Award from the Canadian Diabetes Association and the Canadian Research Chair in Vascular Complications of Diabetes.

No potential conflicts of interest relevant to this article were reported.

F.L., M.P., B.D., and P.G. performed experiments and analyzed the data. M.L. provided the *Prkcd*-deficient mice. A.G. performed animal care and researched data. F.L. and P.G. wrote the manuscript. P.G. is the guarantor of this work, and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

The authors gratefully acknowledge Marie-Élaine Clavet (Montreal Heart Institute) for her assistance with histochemistry technics.

# REFERENCES


abstract: We present the first ever global account of the production, use, and end-of-life fate of all plastics ever made by humankind.
author: Roland Geyer; Jenna R. Jambeck; Kara Lavender Law\*Corresponding author. Email: <firstname.lastname@example.com>
date: 2017-07
references:
title: Production, use, and fate of all plastics ever made

# INTRODUCTION

A world without plastics, or synthetic organic polymers, seems unimaginable today, yet their large-scale production and use only dates back to \~1950. Although the first synthetic plastics, such as Bakelite, appeared in the early 20th century, widespread use of plastics outside of the military did not occur until after World War II. The ensuing rapid growth in plastics production is extraordinary, surpassing most other man-made materials. Notable exceptions are materials that are used extensively in the construction sector, such as steel and cement (*1*, *2*).

Instead, plastics' largest market is packaging, an application whose growth was accelerated by a global shift from reusable to single-use containers. As a result, the share of plastics in municipal solid waste (by mass) increased from less than 1% in 1960 to more than 10% by 2005 in middle- and high-income countries (*3*). At the same time, global solid waste generation, which is strongly correlated with gross national income per capita, has grown steadily over the past five decades (*4*, *5*).

The vast majority of monomers used to make plastics, such as ethylene and propylene, are derived from fossil hydrocarbons. None of the commonly used plastics are biodegradable. As a result, they accumulate, rather than decompose, in landfills or the natural environment (*6*). The only way to permanently eliminate plastic waste is by destructive thermal treatment, such as combustion or pyrolysis. Thus, near-permanent contamination of the natural environment with plastic waste is a growing concern. Plastic debris has been found in all major ocean basins (*6*), with an estimated 4 to 12 million metric tons (Mt) of plastic waste generated on land entering the marine environment in 2010 alone (*3*). Contamination of freshwater systems and terrestrial habitats is also increasingly reported (*7*–*9*), as is environmental contamination with synthetic fibers (*9*, *10*). Plastic waste is now so ubiquitous in the environment that it has been suggested as a geological indicator of the proposed Anthropocene era (*11*).

We present the first global analysis of all mass-produced plastics ever made by developing and combining global data on production, use, and end-of-life fate of polymer resins, synthetic fibers, and additives into a comprehensive material flow model. The analysis includes thermoplastics, thermosets, polyurethanes (PURs), elastomers, coatings, and sealants but focuses on the most prevalent resins and fibers: high-density polyethylene (PE), low-density and linear low-density PE, polypropylene (PP), polystyrene (PS), polyvinylchloride (PVC), polyethylene terephthalate (PET), and PUR resins; and polyester, polyamide, and acrylic (PP&A) fibers. The pure polymer is mixed with additives to enhance the properties of the material.

# RESULTS AND DISCUSSION

Global production of resins and fibers increased from 2 Mt in 1950 to 380 Mt in 2015, a compound annual growth rate (CAGR) of 8.4% (table S1), roughly 2.5 times the CAGR of the global gross domestic product during that period (*12*, *13*). The total amount of resins and fibers manufactured from 1950 through 2015 is 7800 Mt. Half of this—3900 Mt—was produced in just the past 13 years. Today, China alone accounts for 28% of global resin and 68% of global PP&A fiber production (*13*–*15*). Bio-based or biodegradable plastics currently have a global production capacity of only 4 Mt and are excluded from this analysis (*16*).

We compiled production statistics for resins, fibers, and additives from a variety of industry sources and synthesized them according to type and consuming sector (table S2 and figs. S1 and S2) (*12*–*24*). Data on fiber and additives production are not readily available and have typically been omitted until now. On average, we find that nonfiber plastics contain 93% polymer resin and 7% additives by mass. When including additives in the calculation, the amount of nonfiber plastics (henceforth defined as resins plus additives) manufactured since 1950 increases to 7300 Mt. PP&A fibers add another 1000 Mt. Plasticizers, fillers, and flame retardants account for about three quarters of all additives (table S3). The largest groups in total nonfiber plastics production are PE (36%), PP (21%), and PVC (12%), followed by PET, PUR, and PS (\<10% each). Polyester, most of which is PET, accounts for 70% of all PP&A fiber production. Together, these seven groups account for 92% of all plastics ever made. Approximately 42% of all nonfiber plastics have been used for packaging, which is predominantly composed of PE, PP, and PET. The building and construction sector, which has used 69% of all PVC, is the next largest consuming sector, using 19% of all nonfiber plastics (table S2).

We combined plastic production data with product lifetime distributions for eight different industrial use sectors, or product categories, to model how long plastics are in use before they reach the end of their useful lifetimes and are discarded (*22*, *25*–*29*). We assumed log-normal distributions with means ranging from less than 1 year, for packaging, to decades, for building and construction (Fig. 1). This is a commonly used modeling approach to estimating waste generation for specific materials (*22*, *25*, *26*). A more direct way to measure plastic waste generation is to combine solid waste generation data with waste characterization information, as in the study of Jambeck *et al.* (*3*). However, for many countries, these data are not available in the detail and quality required for the present analysis.

We estimate that in 2015, 407 Mt of primary plastics (plastics manufactured from virgin materials) entered the use phase, whereas 302 Mt left it. Thus, in 2015, 105 Mt were added to the in-use stock. For comparison, we estimate that plastic waste generation in 2010 was 274 Mt, which is equal to the independently derived estimate of 275 Mt by Jambeck *et al.* (*3*). The different product lifetimes lead to a substantial shift in industrial use sector and polymer type between plastics entering and leaving use in any given year (tables S4 and S5 and figs. S1 to S4). Most of the packaging plastics leave use the same year they are produced, whereas construction plastics leaving use were produced decades earlier, when production quantities were much lower. For example, in 2015, 42% of primary nonfiber plastics produced (146 Mt) entered use as packaging and 19% (65 Mt) as construction, whereas nonfiber plastic waste leaving use was 54% packaging (141 Mt) and only 5% construction (12 Mt). Similarly, in 2015, PVC accounted for 11% of nonfiber plastics production (38 Mt) and only 6% of nonfiber plastic waste generation (16 Mt).

By the end of 2015, all plastic waste ever generated from primary plastics had reached 5800 Mt, 700 Mt of which were PP&A fibers. There are essentially three different fates for plastic waste. First, it can be recycled or reprocessed into a secondary material (*22*, *26*). Recycling delays, rather than avoids, final disposal. It reduces future plastic waste generation only if it displaces primary plastic production (*30*); however, because of its counterfactual nature, this displacement is extremely difficult to establish (*31*). Furthermore, contamination and the mixing of polymer types generate secondary plastics of limited or low technical and economic value. Second, plastics can be destroyed thermally. Although there are emerging technologies, such as pyrolysis, which extracts fuel from plastic waste, to date, virtually all thermal destruction has been by incineration, with or without energy recovery. The environmental and health impacts of waste incinerators strongly depend on emission control technology, as well as incinerator design and operation. Finally, plastics can be discarded and either contained in a managed system, such as sanitary landfills, or left uncontained in open dumps or in the natural environment.

We estimate that 2500 Mt of plastics—or 30% of all plastics ever produced—are currently in use. Between 1950 and 2015, cumulative waste generation of primary and secondary (recycled) plastic waste amounted to 6300 Mt. Of this, approximately 800 Mt (12%) of plastics have been incinerated and 600 Mt (9%) have been recycled, only 10% of which have been recycled more than once. Around 4900 Mt—60% of all plastics ever produced—were discarded and are accumulating in landfills or in the natural environment (Fig. 2). Of this, 600 Mt were PP&A fibers. None of the mass-produced plastics biodegrade in any meaningful way; however, sunlight weakens the materials, causing fragmentation into particles known to reach millimeters or micrometers in size (*32*). Research into the environmental impacts of these "microplastics" in marine and freshwater environments has accelerated in recent years (*33*), but little is known about the impacts of plastic waste in land-based ecosystems.

Before 1980, plastic recycling and incineration were negligible. Since then, only nonfiber plastics have been subject to significant recycling efforts. The following results apply to nonfiber plastic only: Global recycling and incineration rates have slowly increased to account for 18 and 24%, respectively, of nonfiber plastic waste generated in 2014 (figs. S5 and S6). On the basis of limited available data, the highest recycling rates in 2014 were in Europe (30%) and China (25%), whereas in the United States, plastic recycling has remained steady at 9% since 2012 (*12*, *13*, *34*–*36*). In Europe and China, incineration rates have increased over time to reach 40 and 30%, respectively, in 2014 (*13*, *35*). However, in the United States, nonfiber plastics incineration peaked at 21% in 1995 before decreasing to 16% in 2014 as recycling rates increased, with discard rates remaining constant at 75% during that time period (*34*). Waste management information for 52 other countries suggests that in 2014, the rest of the world had recycling and incineration rates similar to those of the United States (*37*). To date, end-of-life textiles (fiber products) do not experience significant recycling rates and are thus incinerated or discarded together with other solid waste.

Primary plastics production data describe a robust time trend throughout its entire history. If production were to continue on this curve, humankind will have produced 26,000 Mt of resins, 6000 Mt of PP&A fibers, and 2000 Mt of additives by the end of 2050. Assuming consistent use patterns and projecting current global waste management trends to 2050 (fig. S7), 9000 Mt of plastic waste will have been recycled, 12,000 Mt incinerated, and 12,000 Mt discarded in landfills or the natural environment (Fig. 3).

Any material flow analysis of this kind requires multiple assumptions or simplifications, which are listed in Materials and Methods, and is subject to considerable uncertainty; as such, all cumulative results are rounded to the nearest 100 Mt. The largest sources of uncertainty are the lifetime distributions of the product categories and the plastic incineration and recycling rates outside of Europe and the United States. Increasing/decreasing the mean lifetimes of all product categories by 1 SD changes the cumulative primary plastic waste generation (for 1950 to 2015) from 5900 to 4600/6200 Mt or by −4/+5%. Increasing/decreasing current global incineration and recycling rates by 5%, and adjusting the time trends accordingly, changes the cumulative discarded plastic waste from 4900 (for 1950 to 2015) to 4500/5200 Mt or by −8/+6%.

The growth of plastics production in the past 65 years has substantially outpaced any other manufactured material. The same properties that make plastics so versatile in innumerable applications—durability and resistance to degradation—make these materials difficult or impossible for nature to assimilate. Thus, without a well-designed and tailor-made management strategy for end-of-life plastics, humans are conducting a singular uncontrolled experiment on a global scale, in which billions of metric tons of material will accumulate across all major terrestrial and aquatic ecosystems on the planet. The relative advantages and disadvantages of dematerialization, substitution, reuse, material recycling, waste-to-energy, and conversion technologies must be carefully considered to design the best solutions to the environmental challenges posed by the enormous and sustained global growth in plastics production and use.

# MATERIALS AND METHODS

## Plastic production

The starting point of the plastic production model is global annual pure polymer (resin) production data from 1950 to 2015, published by the Plastics Europe Market Research Group, and global annual fiber production data from 1970 to 2015 published by The Fiber Year and Tecnon OrbiChem (table S1). The resin data closely follow a second-order polynomial time trend, which generated a fit of *R*^2^ = 0.9968. The fiber data closely follow a third-order polynomial time trend, which generated a fit of *R*^2^ = 0.9934. Global breakdowns of total production by polymer type and industrial use sector were derived from annual market and polymer data for North America, Europe, China, and India (table S2) (*12*, *13*, *19*–*24*). U.S. and European data are available for 2002 to 2014. Polymer type and industrial use sector breakdowns of polymer production are similar across countries and regions.

Global additives production data, which are not publicly available, were acquired from market research companies and cross-checked for consistency (table S3) (*17*, *18*). Additives data are available for 2000 to 2014. Polymer type and industrial use sector breakdowns of polymer production and the additives to polymer fraction were both stable over the time period for which data are available and thus assumed constant throughout the modeling period of 1950–2015. Any errors in the early decades were mitigated by the lower production rates in those years. Additives data were organized by additive type and industrial use sector and integrated with the polymer data. *P*~*i*~ (*t*) denotes the amount of primary plastics (that is, polymers plus additives) produced in year *t* and used in sector *i* (fig. S1).

## Plastic waste generation and fate

Plastics use was characterized by discretized log-normal distributions, LTD~i~ (*j*), which denotes the fraction of plastics in industrial use sector *i* used for *j* years (Fig. 1). Mean values and SDs were gathered from published literature (table S4) (*22*, *25*–*29*). Product lifetimes may vary significantly across economies and also across demographic groups, which is why distributions were used and sensitivity analysis was conducted with regard to mean product lifetimes. The total amount of primary plastic waste generated in year *t* was calculated as PW (*t*) = $\sum_{\mathit{i} = 1}^{8}\sum_{\mathit{j} = 1}^{65}\mathit{P}_{\mathit{i}}(\mathit{t} - \mathit{j}) \bullet \text{LTD}_{\mathit{i}}(\mathit{j})$ (figs. S3 and S4). Secondary plastic waste generated in year *t* was calculated as the fraction of total plastic waste that was recycled *k* years ago, SW (*t*) = \[PW (*t* − *k*) + SW (*t* − *k*)\]\[RR (*t* − *k*)\], where *k* is the average use time of secondary plastics and RR (*t* − *k*) is the global recycling rate in year *t* − *k*. Amounts of plastic waste discarded and incinerated are calculated as DW(*t*) = \[PW(*t*) + SW(*t*) ∙ DR(*t*) and IW(*t*) = \[PW(*t*) + SW(*t*)\] ∙ IR(*t*), with DR(*t*) and IR(*t*) being the global discard and incineration rates in year *t* (fig. S5). Cumulative values at time *T* were calculated as the sum over all *T* − 1950 years of plastics mass production. Examples are cumulative primary production $\text{CP}_{\mathit{i}}(\mathit{T}) = \sum_{\mathit{t} = 1950}^{\mathit{T}}\mathit{P}_{\mathit{i}}(\mathit{t})$ and cumulative primary plastic waste generation, $\text{CPW}(\mathit{T}) = \sum_{\mathit{t} = 1950}^{\mathit{T}}PW(\mathit{t})$ (Fig. 3).

## Recycling, incineration, and discard rates

Time series for resin, that is, nonfiber recycling, incineration, and discard rates were collected separately for four world regions: the United States, the EU-28 plus Norway and Switzerland, China, and the rest of the world. Detailed and comprehensive solid waste management data for the United States were published by the U.S. Environmental Protection Agency dating back to 1960 (table S7) (*34*). European data were from several reports by PlasticsEurope, which collectively cover 1996 to 2014 (*12*, *13*, *38*). Chinese data were synthesized and reconciled from the English version of the China Statistical Yearbook, translations of Chinese publications and government reports, and additional waste management literature (*35*, *36*, *39*–*41*). Waste management for the rest of the world was based on World Bank data (*37*). Time series for global recycling, incineration, and discard rates (fig. S5) were derived by adding the rates of the four regions weighted by their relative contribution to global plastic waste generation. In many world regions, waste management data were sparse and of poor quality. For this reason, sensitivity analysis with regard to waste management rates was conducted.

The resulting global nonfiber recycling rate increased at a constant 0.7% per annum (p.a.) between 1990 and 2014. If this linear trend is assumed to continue, the global recycling rate would reach 44% in 2050. The global nonfiber incineration rate has grown more unevenly but, on average, increased 0.7% p.a. between 1980 and 2014. Assuming an annual increase of 0.7% between 2014 and 2050 yielded a global incineration rate of 50% by 2050. With those two assumptions, global discard rate would decrease from 58% in 2014 to 6% in 2050 (fig. S7). The dashed lines in Fig. 3 are based on those assumptions and therefore simply forward projections of historical global trends and should not be mistaken for a prediction or forecast. There is currently no significant recycling of synthetic fibers. It was thus assumed that end-of-life textiles are incinerated and discarded together with all other municipal solid waste.

# Supplementary Material

###### http://advances.sciencemag.org/cgi/content/full/3/7/e1700782/DC1

## Acknowledgments

We thank C.-J. Simon and R. Nayaran for providing polymer production data and G.L. Mahoney for designing Fig. 2. We especially thank I. Creelman and R. Song for data collection and model implementation, E. Smith for assistance in compiling the U.S. Environmental Protection Agency and China solid waste management data, S. Wang for researching China plastic waste management and translation of articles, J. Chamberlain for researching additives, and I. MacAdam-Somer for researching synthetic fibers. This work benefitted from helpful discussions with T. Siegler and C. Rochman and comments from two anonymous reviewers. **Funding:** This work was conducted within the Marine Debris Working Group at the National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, with support from Ocean Conservancy. R.G. was supported by the NSF Chemical, Bioengineering, Environmental and Transport Systems grant \#1335478. **Author contributions:** R.G. led the research design, data collection, model development, calculations, interpretation of results, and writing of the manuscript; J.R.J. contributed to research design, data collection, model development, and interpretation of results; K.L.L. contributed to research design, analysis and interpretation of results, and writing of the manuscript. **Competing interests:** The authors declare that they have no competing interests. **Data and materials availability:** All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

### SUPPLEMENTARY MATERIALS

Supplementary material for this article is available at <http://advances.sciencemag.org/cgi/content/full/3/7/e1700782/DC1>

fig. S1. Global primary plastics production (in million metric tons) according to industrial use sector from 1950 to 2015.

fig. S2. Global primary plastics production (in million metric tons) according to polymer type from 1950 to 2015.

fig. S3. Global primary plastics waste generation (in million metric tons) according to industrial use sector from 1950 to 2015.

fig. S4. Global primary plastics waste generation (in million metric tons) according to polymer type from 1950 to 2015.

fig. S5. Estimated percentage of global (nonfiber) plastic waste recycled, incinerated, and discarded from 1950 to 2014 \[(*12*, *13*, *34*–*42*) and table S7\].

fig. S6. Annual global primary and secondary plastic waste generation TW (*t*), recycling RW (*t*), incineration IW (*t*), and discard DW (*t*) (in million metric tons) from 1950 to 2014.

fig. S7. Projection of global trends in recycling, incineration, and discard of plastic waste from 1980 to 2014 (to the left of vertical black line) to 2050 (to the right of vertical black line).

table S1. Annual global polymer resin and fiber production in million metric tons (*12*–*15*).

table S2. Share of total polymer resin production according to polymer type and industrial use sector calculated from data for Europe, the United States, China, and India covering the period 2002–2014 (*12*, *13*, *19*–*24*).

table S3. Share of additive type in global plastics production from data covering the period 2000–2014 (*17*, *18*).

table S4. Baseline mean values and SDs used to generate log-normal product lifetime distributions for the eight industrial use sectors used in this study (*22*, *25*–*29*).

table S5. Global primary plastics production and primary waste generation (in million metric tons) in 2015 according to industrial use sector.

table S6. Global primary plastics production and primary waste generation (in million metric tons) in 2015 according to polymer type/additive.

table S7. Additional data sources for U.S. plastics recycling and incineration.

table S8. Complete list of data sources.

# REFERENCES AND NOTES


date: 2018-04
title: Individualized, supportive care key to positive childbirth experience, says WHO

**15 FEBRUARY 2018 \| GENEVA -** AWHO has issued new recommendations to establish global care standards for healthy pregnant women and reduce unnecessary medical interventions.

Worldwide, an estimated 140 million births take place every year. Most of these occur without complications for women and their babies. Yet, over the past 20 years, practitioners have increased the use of interventions that were previously only used to avoid risks or treat complications, such as oxytocin infusion to speed up labour or caesarean sections.

"We want women to give birth in a safe environment with skilled birth attendants in well-equipped facilities. However, the increasing medicalization of normal childbirth processes are undermining a woman's own capability to give birth and negatively impacting her birth experience," says Dr Princess Nothemba Simelela, WHO Assistant Director-General for Family, Women, Children and Adolescents.

"If labour is progressing normally, and the woman and her baby are in good condition, they do not need to receive additional interventions to accelerate labour," she says.

Childbirth is a normal physiological process that can be accomplished without complications for the majority of women and babies. However, studies show a substantial proportion of healthy pregnant women undergo at least one clinical intervention during labour and birth. They are also often subjected to needless and potentially harmful routine interventions.

The new WHO guideline includes 56 evidence-based recommendations on what care is needed throughout labour and immediately after for the woman and her baby. These include having a companion of choice during labour and childbirth; ensuring respectful care and good communication between women and health providers; maintaining privacy and confidentiality; and allowing women to make decisions about their pain management, labour and birth positions and natural urge to push, among others.

**Every labour is unique and progresses at different rates**

The new WHO guideline recognizes that every labour and childbirth is unique and that the duration of the active first stage of labour varies from one woman to another. In a first labour, it usually does not extend beyond 12 hours. In subsequent labours it usually does not extend beyond 10 hours.

To reduce unnecessary medical interventions, the WHO guideline states that the previous benchmark for cervical dilation rate at 1 cm/hr during the active first stage of labour (as assessed by a partograph or chart used to document the course of a normal labour) may be unrealistic for some women and is inaccurate in identifying women at risk of adverse birth outcomes. The guideline emphasizes that a slower cervical dilation rate alone should not be a routine indication for intervention to accelerate labour or expedite birth.

"Many women want a natural birth and prefer to rely on their bodies to give birth to their baby without the aid of medical intervention," says Ian Askew, WHO Director, Department of Reproductive Health and Research. "Even when a medical intervention is wanted or needed, the inclusion of women in making decisions about the care they receive is important to ensure that they meet their goal of a positive childbirth experience."

**High quality care for all women**

Unnecessary labour interventions are widespread in low-, middle- and high-income settings, often putting a strain on already scarce resources in some countries, and further widening of the equity gap.

As more women give birth in health facilities with skilled health professionals and timely referrals, they deserve better quality of care. About 830 women die from pregnancy- or childbirth-related complications around the world every day – the majority could be prevented with high-quality care in pregnancy and during childbirth.

Disrespectful and non-dignified care is prevalent in many health facilities, violating human rights and preventing women from accessing care services during childbirth. In many parts of the world, the health provider controls the birthing process, which further exposes healthy pregnant women to unnecessary medical interventions that interfere with the natural childbirth process.

Achieving the best possible physical, emotional, and psychological outcomes for the woman and her baby requires a model of care in which health systems empower all women to access care that focuses on the mother and child.

Health professionals should advise healthy pregnant women that the duration of labour varies greatly from one woman to another. While most women want a natural labour and birth, they also acknowledge that birth can be an unpredictable and risky event and that close monitoring and sometimes medical interventions may be necessary. Even when interventions are needed or wanted, women usually wish to retain a sense of personal achievement and control by being involved in decision making, and by rooming in with their baby after childbirth.

The data also highlight the need for rapid scale-up of research. There have been some encouraging signs in funding available for investment in research for a cure for dementia in recent years, but much more needs to be done. The number of articles in peer-reviewed journals on dementia in 2016 was close to 7000. This compares with more than 15 000 for diabetes, and more than 99 000 for cancer during the same year. Research is needed not only to find a cure for dementia, but also in the areas of prevention, risk reduction, diagnosis, treatment and care.

The Observatory will provide a knowledge bank where health and social care authorities, medical professionals, researchers and civil society organizations will be able to find country and regional dementia profiles, global reports, policy guidance, guidelines and toolkits on dementia prevention and care.

**Dementia**

Dementia is an umbrella term for several diseases that are mostly progressive, affecting memory, other cognitive abilities and behaviour and interfering significantly with a person's ability to maintain the activities of daily living. Women are more often affected than men. Alzheimer's disease is the most common type of dementia and accounts for 60–70% of cases. The other common types are vascular dementia and mixed forms.

Available from: <http://www.who.int/mediacentre/news/releases/2018/positive-childbirth-experience/en/>


abstract: Using a genome-scale approach to study transcription levels in a human CD8^+^ T-cell clone, a recent study has suggested that the repertoire of molecules on the surface of T cells is close to being completely characterized.
author: Joerg Ermann; Chan D Chung; C Garrison Fathman
date: 2004
institute: 1Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
references:
title: Scratching the (T cell) surface

Over the last few years technological advances have made it possible to study, in parallel, the expression of thousands of genes in cells, tissues or organisms. While this genome-scale approach to gene-expression analysis has been touted by some as the new 'golden era' of hypothesis-unlimited discovery-driven research \[1\], it is apparent that our ability to make sense of the vast accumulations of data does not always keep up with our ability to generate them. Many groups have used technologies such as microarrays or serial analysis of gene expression (SAGE) to analyze gene expression in cells of the immune system. A recent article by Evans *et al*. in *Immunity* \[2\] attempting to expand beyond the simple description of differential gene-expression patterns arrives at a bold conclusion. The article's title is intriguing - *The T-cell surface - how well do we know it?* - and the conclusion is that we know it quite well.

Not surprisingly, molecules on the surface of T cells are of great interest to immunologists. Much information has been generated about T-cell-specific surface molecules and their function since the first monoclonal antibodies against leukocyte surface markers were made in the late 1970s. Attempts to compare the specificities of different monoclonal antibodies and to identify their targets led to the development of the cluster of differentiation (CD) nomenclature \[3\]. Most of the CD antigens turned out to be proteins (a few CD antibodies detect carbohydrate modifications) and the respective genes have been cloned in both humans and mice. Over time the CD system thus transformed into a classification for leukocyte surface molecules, rather than antibodies. Currently, there are 247 assigned CDs \[4\]. Almost 200 additional molecules are being considered for CD status during the current Eighth International Workshop on Human Leukocyte Differentiation Antigens that will culminate in the HLDA8 Conference in Adelaide, Australia in December 2004 \[5\].

The molecules on the surface of T cells belong to very diverse structural and functional classes, and include components of the immunoreceptors on T, B and NK cells, adhesion molecules, and cytokine and chemokine receptors. Some CDs have proven to be useful markers of subpopulations of cells with strikingly different functions. Most T cells express CD3 and an αβ T-cell receptor (TCR) paired with either the CD4 or the CD8 molecule as co-receptor. CD4^+^ αβ T cells recognize antigens presented by antigen-presenting cells in the context of major histocompatibility complex (MHC) class II molecules. Their main effector function is the production of cytokines and the facilitation of immune responses. A subclass of CD4^+^ T cells that has recently gained attention comprises regulatory/suppressor T cells, which are characterized by the constitutive expression of CD25 - the α-chain of the interleukin 2 (IL-2) receptor. These cells are thought to negatively regulate immune responses and to prevent uncontrolled autoimmunity. CD8^+^ αβ T cells recognize antigens in the context of MHC class I molecules, which are expressed on most somatic cells. Upon activation, CD8^+^ T cells develop into cytolytic T lymphocytes (CTLs) ready to kill cells infected by intracellular pathogens, such as viruses, or eradicate tumors cells.

In their article on the T-cell surface, Evans *et al*. \[2\] used SAGE to study gene expression in a human CD8^+^ CTL clone. The SAGE library that was generated (referred to as the CTL library) contained 71,174 SAGE tags representing 20,204 distinct sequences. This number was estimated to cover all of the transcripts whose expression level was at or above 0.008% of the transcriptome (that is having at least 22 copies per cell). The library included 111 genes with, or being considered for, CD status. Several pairwise comparisons with unrelated SAGE libraries were then performed.

The central analysis of the paper starts with a comparison of the CTL library with a library derived from cerebellum; 1,098 transcripts were significantly more abundant in the CTL library. Among these, about a quarter of the transcripts with known function coded for proteins involved in protein/mRNA synthesis, a result that was thought to reflect the proliferating versus non-proliferating character of the two cell/tissue types (CTL versus cerebellum). Interestingly, the set of genes that was highly differentially expressed in the CTLs was enriched for surface markers, signaling molecules and soluble mediators. In an attempt to find a core set of CTL-specific genes, additional comparisons were performed between the CTL library and SAGE libraries from ovary epithelium (as a type of proliferating cell) and a panel of tumor libraries. This resulted in a shortened list of 387 CTL-specific transcripts.

Notably, at all stages of comparison 42-45% of the transcripts lacked an assigned function. Of the known genes in the final list of 387 specific transcripts in the CTL library, 27% were cell-surface molecules, including TCR components, CD2, CD5, and CD8. Evans *et al*. \[2\] then asked how many of the unknown CTL-specific transcripts encoded surface molecules. Sequences representing UniGene clusters \[6\] were analyzed for signatures of surface molecules by domain analysis, looking for transmembrane regions or other domains characteristic of leukocyte surface molecules, and by BLAST searches for related genes with known function. Surprisingly, only 2 of the 97 (2%) UniGene clusters analyzed showed some potential for encoding novel surface molecules. The authors therefore concluded that "the cell-type-specific composition of the resting CD8^+^ T-cell surface is now largely defined."

How complete and cell-type-specific is this list of 387 genes? The CTL SAGE library of 20,204 transcripts represents the transcriptome of the CTL clone. A detailed discussion of the limitations of the SAGE method is beyond the scope of this article; suffice it to say that it is possible that functionally important genes may be missing from the library due to low mRNA expression levels, chance or because they lack the target sequence for the tagging enzyme used in the SAGE protocol \[7\]. In order to get to the final set of 378 CTL-specific genes Evans *et al*. \[2\] eliminated all transcripts that were present at comparable levels in unrelated libraries. This powerful approach seems to have validated itself by the fact that TCR components and other principal T-cell markers were present in the shortlist of 387 genes. The method used is not unbiased, however. The choice of libraries (in this case cerebellum, ovary epithelium, and a panel of tumor cell lines that were not specified further), data quality, and the algorithms used for comparison can be expected to have an enormous impact on the results obtained. It is easy to see how functionally important genes might get lost because they are expressed at sufficiently high levels in one of the cell or tissue types used for comparison. A list of cell-type-specific genes derived by this method of successive *in silico* subtraction defines a cell-type-specific gene-expression pattern against the transcriptional background of the cell or tissue types used for comparison. It is not a list of all genes relevant for cell-type-specific function.

The major finding of the Evans *et al*. study \[2\] is that among the 387 CTL-specific transcripts 27% of the known genes encoded cell-surface molecules, whereas only 2% of the unknown genes showed some potential in that regard. The implication is that the catalog of CTL surface molecules is close to being complete. While it is not unreasonable to assume that the concerted efforts over the last two decades to characterize surface molecules on leukocytes have led to a situation where most CTL-specific surface molecules are known \[8\], some questions remain. Is this finding unique for CTLs (or for leukocytes in general)? What would be the result of a similar analysis in, for instance, ovary epithelium? Were there unknown cell-surface molecules in the CTL SAGE library? If so, at what point of the stepwise subtraction process did these transcripts get eliminated? It has been noted that leukocytes share many surface molecules with neuronal cells and epithelial cells \[8\], the very cells used for subtraction by Evans *et al*. \[2\]. An alternative experimental approach to analyzing the incidence of unknown cell-surface molecules might be to generate SAGE libraries from microsomal and free-ribosomal mRNA pools generated through equilibrium density centrifugation. This approach has been demonstrated to discriminate secretory and cell-surface molecules from nonsecretory proteins quite efficiently \[9,10\]. One would expect a significantly lower percentage of unknown transcripts in the secretory/surface molecule fraction.

Only the CTL SAGE library was actually generated by Evans *et al*. \[2\]. The other libraries used for comparison were derived from publicly available databases. Open access to primary gene-expression data is essential, not only for enabling researchers to reproduce published analyses, but also to allow for novel experimental approaches that incorporate relevant data generated by others. Important information can be gained by comparing genome-wide expression data across large numbers of samples. In a recent, extreme example, 3,283 DNA microarrays from human, *Drosophila*, *Caenorhabditis elegans* and yeast were used to define evolutionarily conserved genetic modules of co-expressed genes \[11\]. SAGE data have been publicly available on SAGEMap \[12,13\] for a number of years. Microarray data are far more complex, but a standard for the annotation of microarray data (Minimal Information About a Microarray Experiment; MIAME) \[14\] and a platform-independent data exchange format (Microarray Gene Expression Markup Language; MAGE-ML) \[15\] have been developed. Furthermore, public repositories for microarray data such as ArrayExpress \[16,17\] and GeoBus \[18,19\] are now available.

SAGE has the advantage over current microarray technology of measuring absolute transcript abundance. Nevertheless, there are some limitations as to what can be said about the T-cell surface by studying mRNA levels. First, for a number of surface molecules, such as CD45, a variety of functionally important splice variants have been described \[20\] that cannot be distinguished by the 3' SAGE tag. Second, mRNA levels correlate poorly with protein abundance \[21\]. Third, posttranslational protein modifications can be functionally relevant; for example, glycosylation of CD8 has been demonstrated to affect thymocyte selection by influencing activation thresholds \[22\]. Fourth, T-cell activation involves re-localization of surface molecules leading to the formation of the immunological synapse, a supramolecular cluster at the contact zone between antigen-presenting cell and the T cell \[23\]. These early events precede changes in gene expression.

Finally, it seems important to note that the T-cell surface is an abstraction. T cells comprise quite different subsets of cells at variable activation states. As pointed out by Evans *et al*. \[2\], the finding that most of the molecules on the T-cell surface appear to be known applies strictly only to a resting CD8^+^ T-cell clone *in vitro*. 'The T-cell surface - how well do we know it?' is an important question on our way into the post-genomic era of immunology. But even with complete lists of the genes expressed in certain T-cell subpopulations, much more needs to be learned about the regulation and complex interactions of the proteins they encode. We are just scratching the (T cell) surface.