Vast (but avoidable) underestimation of global biodiversity

The number of species on Earth is highly uncertain. A recent study has suggested that there are less than 2 million prokaryotic species on Earth; this Formal Comment suggests instead that there are more likely hundreds of millions or billions of species, and that the majority of these are bacteria associated with insects and other animals.

The number of species on Earth is a fundamental number in science. Yet, estimates of global biodiversity have been highly uncertain. There are presently approximately 1.9 million described species [1]. Estimates of the actual number (both described and undescribed) have ranged from the low millions into the trillions [2,3]. Furthermore, described species richness [1] is dominated by animals (1.3 million; 68%), not bacteria (approximately 10,000 species; 0.5%). Larsen and colleagues [2] summarized evidence suggesting that the majority of species on Earth may be bacteria associated with insect hosts and that bacterial richness may push global biodiversity into the hundreds of millions of species or even low billions.Louca and colleagues [4] (LEA hereafter) have claimed instead that there are only 40,100 host-associated bacterial species among all animal species and 0.8 to 1.6 million prokaryotic species overall (see their “Author summary”). Strangely, they excluded bacterial species associated with animal hosts from their estimates of total prokaryotic diversity and justified this by claiming that the estimates of Larsen and colleagues [2] were “mathematically flawed.” Here, I examine their claims and present new estimates of global biodiversity.Remarkably, all projections by LEA for host-associated bacterial richness were based on an estimate from one ant genus (Cephalotes), an estimate that is demonstrably incorrect by orders of magnitude (S1 Text). Without examining the underlying data [5], LEA estimated only 40 bacterial species among all 130 ant species in this genus. Yet, simply counting the bacterial species among the 25 sampled ant species in that genus reveals 616 unique bacterial species, of which 539 appear to be unique to the genus and 369 each unique to a single ant species (using the standard 97% cutoff for 16S divergence and data from [5]). Thus, there were >500 bacterial species among 25 ant species, not 40 bacterial species among 130 ant species. This mistake was further exacerbated by inexplicably ignoring data from the other 2 insect genera analyzed by Larsen and colleagues [2], thus maximizing the impact of their incorrect estimate for this genus.Their overall estimate of bacterial richness was also strongly influenced by their questionable assumption that all animal genera can share bacterial species (i.e., reducing their estimate of 3 million host-associated bacterial species to only 40,100). They assumed “a conservative overlap of only 0.1% between any two randomly chosen genera” for the number of bacterial species shared between animal genera. No justification was given for this value of 0.1%, nor were any alternative values explored. Furthermore, they implicitly assumed that any bacterial species can be shared between any pair of animal genera, regardless of their phylogeny, habitat, or geographic range. So, for example, a bacterial species that is a gut endosymbiont of a terrestrial herbivorous insect species endemic to Madagascar could somehow be shared with a deep-sea worm in the northern Pacific Ocean. This is ridiculous: there must be a reason why bacterial species are shared among host species and genera (e.g., shared phylogeny, location, diet). For example, broad-scale studies show that sharing of bacteria among insect hosts is associated with both host phylogeny and diet [6].LEA stated “it is known that substantial overlap exists between the microbiota of different host genera and even of distantly related animal taxa.” However, they provided no numbers to justify this “substantial overlap.” In fact, none of the papers they cited as supporting this assumption actually do (S2 Text). For example, one study [7] found 5 bacterial species shared among 5 insect genera utilizing the same type of host plant (cycads). However, LEA do not mention that this study found 1,789 unique bacterial species among just these 5 insect species (or 177 after filtering). This seems inconsistent with their estimate of only 40,100 bacterial species across all animals. In summary, rather than estimating the overlap of bacterial species among host genera, LEA simply made a number up and combined this with unrealistic, unsupported assumptions about overlap. If LEA had considered Cephalotes (which all their estimates were based on), a survey of this genus and related genera [5] found 1,019 bacterial species, with only 77 of the 616 bacterial species in Cephalotes shared with other sampled genera, and the sharing of bacterial species among hosts strongly related to host phylogeny.Numerous surveys of bacterial diversity in insects strongly suggest that there are far more than 40,100 bacterial species among all animals (8] found roughly twice as many bacterial species as those of approximately 30 insect species [5,9], and the study of 218 insect species [6] found >3.5 times as many as the study of 62 insect species. The simple fact that a study found 9,301 bacterial species among only 218 sampled insect species strongly suggests that there are more than 40,100 bacteria among all animals.Table 1Surveys of bacterial diversity among insect species.LEA incorrectly estimated that a genus of 130 ant species (Cephalotes) hosts only 40 bacterial species and subsequently assumed that all animal genera have the same low number of bacterial species. These broad surveys of bacterial species among insects suggest that many insects (including Cephalotes) host much larger numbers of bacterial species.

Conservation Research Is Not Happening Where It Is Most Needed

Target 19, set by the Convention on Biological Diversity, seeks to improve the knowledge, science base, and technologies relating to biodiversity. We will fail to achieve this target unless prolific biases in the field of conservation science are addressed. We reveal that comparatively less research is undertaken in the world’s most biodiverse countries, the science conducted in these countries is often not led by researchers based in-country, and these scientists are also underrepresented in important international fora. Mitigating these biases requires wide-ranging solutions: reforming open access publishing policies, enhancing science communication strategies, changing author attribution practices, improving representation in international processes, and strengthening infrastructure and human capacity for research in countries where it is most needed.In the environmental sciences, the scientific process generates evidence for policies and practices. Published evidence indicates that the quality standards associated with peer review have been met. Publishing also provides others with access to the evidence being shared, and increasingly, to the data and methodological processes underlying it. There are, however, strong biases in the peer-reviewed literature.Biodiversity and the threats to its persistence are not uniformly distributed across the globe and therefore some areas demand comparatively greater scientific attention. If research is biased away from the most biodiverse areas, then this will accentuate the impacts of the global biodiversity crisis and reduce our capacity to protect and manage the natural ecosystems that underpin human well-being. Target 19 of the Convention on Biodiversity (CBD) states that “By 2020, knowledge, the science base, and technologies relating to biodiversity, its values, functioning, status and trends, and the consequences of its loss, are improved, widely shared and transferred, and applied” [1]. Biases in conservation science will prevent us from achieving this target.We conducted the first comprehensive analysis of publishing trends of the conservation science literature. We identified all publications from 2014 on the topic of “conservation” in the research areas of environmental sciences, ecology, biodiversity conservation, plant sciences, zoology, and geography. We searched both the Thomson Reuters Zoological Records and Web of Science Core Collection databases, which returned 10,036 scientific publications (from 1,061 journals), after the duplicate, unrelated, and incomplete records were removed. For a subset of these publications (n = 7,593, or 81%), we manually identified at least one topic country, and we determined the relative conservation importance of these countries for mammal conservation [2] as well as a broader definition of conservation importance that considers richness of vascular plants, endemic species, and functional species [3].The countries for which knowledge is sparse coincide with where research is most urgently needed. The top five countries, ranked according to relative importance for mammal conservation (i.e, Indonesia, Madagascar, Peru, Mexico, and Australia), were represented in 11.9% of the publications (Fig 1). If we consider the broader definition of conservation importance that reflects the richness of vascular plants, endemic species, and functional species, then the top five countries (i.e., Ecuador, Costa Rica, Panama, the Dominican Republic, and Papua New Guinea) are the focus of only 1.6% of publications (4,5], will continue to be populated with biased data.Open in a separate windowFig 1Global distribution of publications on biodiversity conservation (S1 Data).

Table 1

Publishing trends and representation in the International Union for Conservation of Nature (IUCN) Specialist Groups or the Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES) for (A) the countries ranked highest in terms of importance for mammal conservation [2], (B) the countries ranked highest in terms of biodiversity [3], and (C) the United States and United Kingdom, for the purposes of comparison (S1 Data).

Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.We compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Astronomy has faced the challenges of Big Data for over 20 years and continues with ever-more ambitious studies of the universe. YouTube burst on the scene in 2005 and has sparked extraordinary worldwide interest in creating and sharing huge numbers of videos. Twitter, created in 2006, has become the poster child of the burgeoning movement in computational social science [6], with unprecedented opportunities for new insights by mining the enormous and ever-growing amount of textual data [7]. Particle physics also produces massive quantities of raw data, although the footprint is surprisingly limited since the vast majority of data are discarded soon after acquisition using the processing power that is coupled to the sensors [8]. Consequently, we do not include the domain in full detail here, although that model of rapid filtering and analysis will surely play an increasingly important role in genomics as the field matures.To compare these four disparate domains, we considered the four components that comprise the “life cycle” of a dataset: acquisition, storage, distribution, and analysis (

Immunological Mechanisms Mediating Hantavirus Persistence in Rodent Reservoirs

Hantaviruses, similar to several emerging zoonotic viruses, persistently infect their natural reservoir hosts, without causing overt signs of disease. Spillover to incidental human hosts results in morbidity and mortality mediated by excessive proinflammatory and cellular immune responses. The mechanisms mediating the persistence of hantaviruses and the absence of clinical symptoms in rodent reservoirs are only starting to be uncovered. Recent studies indicate that during hantavirus infection, proinflammatory and antiviral responses are reduced and regulatory responses are elevated at sites of increased virus replication in rodents. The recent discovery of structural and non-structural proteins that suppress type I interferon responses in humans suggests that immune responses in rodent hosts could be mediated directly by the virus. Alternatively, several host factors, including sex steroids, glucocorticoids, and genetic factors, are reported to alter host susceptibility and may contribute to persistence of hantaviruses in rodents. Humans and reservoir hosts differ in infection outcomes and in immune responses to hantavirus infection; thus, understanding the mechanisms mediating viral persistence and the absence of disease in rodents may provide insight into the prevention and treatment of disease in humans. Consideration of the coevolutionary mechanisms mediating hantaviral persistence and rodent host survival is providing insight into the mechanisms by which zoonotic viruses have remained in the environment for millions of years and continue to be transmitted to humans.Hantaviruses are negative sense, enveloped RNA viruses (family: Bunyaviridae) that are comprised of three RNA segments, designated small (S), medium (M), and large (L), which encode the viral nucleocapsid (N), envelope glycoproteins (G_N and G_C), and an RNA polymerase (Pol), respectively. More than 50 hantaviruses have been found worldwide [1]. Each hantavirus appears to have coevolved with a specific rodent or insectivore host as similar phylogenetic trees are produced from virus and host mitochondrial gene sequences [2]. Spillover to humans causes hemorrhagic fever with renal syndrome (HFRS) or hantavirus cardiopulmonary syndrome (HCPS), depending on the virus [3]–[5]. Although symptoms vary, a common feature of both HFRS and HCPS is increased permeability of the vasculature and mononuclear infiltration [4]. Pathogenesis of HRFS and HCPS in humans is hypothesized to be mediated by excessive proinflammatory and CD8+ T cell responses (​).

Table 1

Summary of Immune Responses in Humans during Hantavirus Infection.

Intrinsic Disorder in Pathogen Effectors: Protein Flexibility as an Evolutionary Hallmark in a Molecular Arms Race

Effector proteins represent a refined mechanism of bacterial pathogens to overcome plants’ innate immune systems. These modular proteins often manipulate host physiology by directly interfering with immune signaling of plant cells. Even if host cells have developed efficient strategies to perceive the presence of pathogenic microbes and to recognize intracellular effector activity, it remains an open question why only few effectors are recognized directly by plant resistance proteins. Based on in-silico genome-wide surveys and a reevaluation of published structural data, we estimated that bacterial effectors of phytopathogens are highly enriched in long-disordered regions (>50 residues). These structurally flexible segments have no secondary structure under physiological conditions but can fold in a stimulus-dependent manner (e.g., during protein–protein interactions). The high abundance of intrinsic disorder in effectors strongly suggests positive evolutionary selection of this structural feature and highlights the dynamic nature of these proteins. We postulate that such structural flexibility may be essential for (1) effector translocation, (2) evasion of the innate immune system, and (3) host function mimicry. The study of these dynamical regions will greatly complement current structural approaches to understand the molecular mechanisms of these proteins and may help in the prediction of new effectors.Plants and pathogens are entangled in a continual arms race. While host organisms have developed complex and dynamic immune systems able to recognize a wide range of pathogens and to discriminate them from beneficial microbes (Jones and Dangl, 2006; Medzhitov, 2007), bacterial pathogens have evolved refined adaptation strategies to overcome the plant’s innate immune system. Among these ingenious adaptations are effector proteins. Most of these proteins are secreted via the type III secretion system (TTSS) into the host cytoplasm, where they manipulate the immune signaling and the physiology of plant cells and thereby improve bacterial fitness within the host (Dean, 2011).Plant–pathogen interactions are highly dynamic processes, both from the evolutionary and the physiological point of view. Here, we postulate that they are equally dynamic at the protein-structure level. This is based on our finding that numerous effector proteins are predicted to be intrinsically disordered (ID) and that this feature may be essential for (1) effector translocation, (2) evasion of the innate immune system, and (3) host function mimicry. Intrinsic disorder has so far been postulated to preferentially occur in eukaryotic proteins. While on average ∼20% of the eukaryotic proteome harbors long (>50 residues) ID segments, these regions are only predicted at low abundance (8% on average) in bacterial proteomes (Dunker et al., 2000). The most likely reason for this discrepancy is the lack of efficient mechanisms to protect unfolded proteins from degradation (Ward et al., 2004). However, when surveying genomes of pathogenic bacteria with the widely used PONDR VL-XT program (Romero et al., 2001), we observed that not only the average percentage of sequence disorder, but most strikingly long (>50 residues) stretches of intrinsic disorder are highly overrepresented in secreted effectors, with especially high levels in phytopathogenic bacteria (Pseudomonas syringae, ∼39%; Ralstonia solanacearum, ∼70%; Xanthomonas spp, ∼77%) (Supplemental Table 1 online). This striking enrichment of unstructured regions strongly suggests positive evolutionary selection of intrinsic disorder in effector proteins and highlights their dynamic nature.

Table 1.

Predictions of Intrinsic Disorder in Effectors and Whole Proteomes of Different Bacterial Species

On the Extent of Tyrosine Phosphorylation in Chloroplasts

Reanalysis of published mass spectrometry data on Tyr-phosphorylated chloroplast proteins indicates that the majority of peptide spectrum matches reporting Tyr phosphorylation are ambiguous.Tyr phosphorylation is a controversial issue in plant phosphoproteomics, ever since early analyses reported up to 5% Tyr phosphorylation in Arabidopsis (Arabidopsis thaliana), despite the lack of a classical Tyr kinase in the Arabidopsis genome (Sugiyama et al., 2008; de la Fuente van Bentem and Hirt, 2009). The same controversy extends to the phosphorylation of chloroplast proteins. In the past 20 years, several indications for Tyr phosphorylation in chloroplasts were reported, and Rubisco is annotated as Tyr phosphorylated protein (www.arabidopsis.org). Initially, Tullberg et al. (1998) found the protein Tyr kinase inhibitor genistein to inhibit the phosphorylation of thylakoid membrane proteins. Supported by the observed stability of some thylakoid phosphoproteins against acid and base hydrolysis, a characteristic property of phospho-Tyr, the authors argue that Tyr phosphorylation of thylakoid membrane proteins is vital for short-term acclimation responses. Similar biochemical properties were observed for autophosphorylation of the chloroplast sensor kinase CSK (Puthiyaveetil et al., 2008). Support for Tyr phosphorylation came from the cross-reactivity of thylakoid membrane proteins and Calvin cycle enzymes (e.g. Rubisco) with phospho-Tyr-specific antibodies (Forsberg and Allen, 2001; Fedina et al., 2008; Ghelis et al., 2008). With the same set of methods, no Tyr phosphorylation was observed in mitochondrial proteins (Forsberg and Allen, 2001).The above reported data are indirect hints for Tyr phosphorylation, and none of the applied methods is sufficiently specific to serve as solid evidence. For example, all phospho-Tyr-specific antibodies have significant cross reactivity with phospho-Ser and phospho-Thr when these have an aromatic amino acid in the +1 position (Zerweck et al., 2009). Using phospho-Tyr-specific antibodies, Forsberg and Allen (2001) found genistein inhibition of light-harvesting complex II phosphorylation with a 50% inhibition of initial activity of around 15 µm. Surprisingly, the same inhibition kinetics were observed with phospho-Thr-specific antibodies, suggesting a lack of specificity of either genistein or the phospho-amino acid antibodies, or both. So far, direct proof for Tyr phosphorylation in chloroplasts by phospho-amino acid analyses is missing. However, mass spectrometry-based phosphoproteomics experiments with plant cell extracts reported phospho-Tyr-containing peptides in chloroplasts but surprisingly not in abundant thylakoid membrane proteins or Calvin cycle enzymes. A recent meta-analysis collated data from 27 published studies and several internal data sets, resulting in a cumulative data set with 5% Tyr-phosphorylated peptides in the entire data set and 12% to 19% in the mitochondria (van Wijk et al., 2014). In this data set, almost 30% of the plastid phosphoproteins are flagged as Tyr phosphorylated (90 proteins from around 300; see supplemental table 5B in van Wijk et al., 2014), standing in stark contrast to dedicated plastid phosphoproteome analyses that identified less than 1% Tyr phosphorylation in the cellular phosphoproteome and none in chloroplast proteins (Reiland et al., 2009).Many of the phospho-Tyr-containing peptides were identified in analyses that applied multistage activation to elevate fragment ion intensity in spectra dominated by the neutral loss of phosphoric acid from phospho-Ser and/or phospho-Thr, sometimes in combination with searches for the phospho-Tyr-specific immonium ion at mass-to-charge ratio 216.0426 (see table 1 in van Wijk et al., 2014). In one instance, phospho-Tyr-specific antibodies were used to enrich Tyr phosphorylated proteins from Arabidopsis full cell extracts (Mithoe et al., 2012). Remarkably, there is almost no overlap in phospho-Tyr peptide identification between the different studies (Mithoe et al., 2012; Wu et al., 2013; van Wijk et al., 2014). Although this could be the result of diverse acquisition methods, enrichment strategies, and data interpretation software in different analyses (Bodenmiller et al., 2007), the low reproducibility and the discrepancies in phospho-Tyr detection among different analyses require further attention, because both are characteristic for incorrect peptide spectrum matches. This is a specific problem here, because false discovery rates (FDRs) accumulate in cumulated data sets.Therefore, we decided to assess the quality of matches to Tyr phosphorylated peptides by a dedicated reanalysis of the original data and benchmarked the robustness of peptide identification by using different software tools for spectra interpretation. Different tools use different scoring schemes to calculate identification probabilities from the fragment ion spectrum; however, they all use basic rules for spectrum matching, such as consecutive b- or y-ion series, matching of the highest intensity peaks to peptide fragments, and identification of matches to plausible derivatives of the major fragments such as losses of ammonia or water. Because of the differences in scoring the identified fragment ions, software tools may interpret spectra differently. However, since the basic rules for peptide matching apply to all identifications, it is clear that robust and reliable identifications are made by different tools that agree on the same interpretation for a spectrum. A specific aspect in the interpretation of phosphopeptide spectra is the assignment of the exact modification site. Common database matching software is usually unsuitable to distinguish modifications at closely spaced amino acids, and it is insufficiently explicit when spectra do not allow distinguishing between alternatives. To circumvent this problem, specialized software tools were developed that score spectra for alternative phosphorylation sites within the peptide sequence by searching for specific fragment ions supporting one or another phosphorylation site (MacLean et al., 2008; Martin et al., 2010).We extracted from the different data sets Tyr-phosphorylated chloroplast proteins and extracted the spectrum information in the form of a MASCOT generic file (mgf) from either PhosphAT (van Wijk et al., 2014) or PRIDE (Mithoe et al., 2012). This resulted in 139 spectra identifying 53 unique peptides representing putative Tyr phosphorylation sites in 53 chloroplast proteins (Supplemental Table S1A; Supplemental Data Set S1). This set of spectra was reanalyzed with MASCOT to assess the significance of the identifications and two alternative software tools established for database searches: PEAKS, a database matching software with a de novo sequencing option (Ma et al., 2003); and Proteome Discoverer with the search engine SEQUEST (Thermo Scientific). With the original search parameters of dynamic phosphorylation of Ser, Thr, and Tyr, dynamic oxidation of Met, fixed carbamidomethylation of Cys, and maximum of two missed cleavages at mass tolerances for precursor and fragment ion matching of 20 ppm/0.5 D (Wu et al., 2013), 50 ppm/0.8 D (internal data sets in van Wijk et al., 2014), and 10 ppm/0.9 D (Mithoe et al., 2012), 11 out of 53 unique peptides were identified with the reported amino acid sequence above the MASCOT significance threshold of P < 0.05, while 42 mgf matchings were reported as insignificant or gave rise to an unrelated peptide identification (www.matrixscience.com; Supplemental Table S1B). The lack of significance correlates with the relaxed search parameters and the many degrees of freedom allowed for peptide matching. With a variation of the above mass tolerance settings, PEAKS identified five (9%) and Proteome Discoverer identified 11 (21%) out of 53 peptides from the data set at a fully relaxed FDR, of which three (6%) identifications by PEAKS and one (2%) by Proteome Discoverer were significant (van Wijk et al., 2014)Presented are those peptides that were identified at least once with one of the alternative tools at one of the indicated mass tolerance settings: precursor tolerance/tandem mass spectrometry/ion match tolerance 50 ppm/0.8 D, 20 ppm/0.5 D, or 10 ppm/0.9 D. We reported all identifications irrespective of the score. Identifications considered significant are labeled with asterisks. Provided is the FDR at which the identification was made. Proteome Discoverer has two FDR settings: below 1% (stringent) or below 5% (relaxed). All matches above a 5% FDR threshold are considered insignificant. The PEAKS FDR is calculated individually for every peptide. Dashes indicate that the peptide was not identified.

Focus on Weed Control: Herbicides as Weed Control Agents: State of the Art: I. Weed Control Research and Safener Technology: The Path to Modern Agriculture

The purpose of modern industrial herbicides is to control weeds. The species of weeds that plague crops today are a consequence of the historical past, being related to the history of the evolution of crops and farming practices. Chemical weed control began over a century ago with inorganic compounds and transitioned to the age of organic herbicides. Targeted herbicide research has created a steady stream of successful products. However, safeners have proven to be more difficult to find. Once found, the mode of action of the safener must be determined, partly to help in the discovery of further compounds within the same class. However, mounting regulatory and economic pressure has changed the industry completely, making it harder to find a successful herbicide. Herbicide resistance has also become a major problem, increasing the difficulty of controlling weeds. As a result, the development of new molecules has become a rare event today.Modern industrial herbicide research begins with the analysis and definition of research objectives. A major part of this lies in the definition of economically important weeds in major arable crops (Kraehmer, 2012). Weed associations change slowly over time. It is important, therefore, to foresee such changes. Today’s weed associations result from events in the distant past. They are associated with the history of crops and the evolution of farm management. In Europe and the Americas, some large-acre crops such as winter oilseed rape and spring oilseed rape (canola), both derived from Brassica spp., and soybean (Glycine max) have attained their current importance only within the last 100 years. Other Old World crops, such as cereals, have expanded over a very long time span and were already rather widespread in Neolithic times (Zohary et al., 2012). The dominance of crop species in agricultural habitats only left room for weed species that could adapt to cultivation technologies. Changes in crop management and the global weed infestation have happened in waves. A major early factor in Europe was presumably the grain trade in the Roman period (Erdkamp, 2005). The Romans spread their preferred crops and, unintentionally, associated weed seeds throughout Europe, Asia, and Africa. A second wave of global vegetation change started in the 16th century after the discovery of the Americas. Crops and weeds were distributed globally by agronomists and botanists. Alien species started to spread on all continents. A third phase can be seen in the 19th century with the industrialization of agriculture and the breeding of competitive crop varieties. The analysis of weed spectra in arable fields grew from this historical background. Weeds are plants interfering with the interests of people (Kraehmer and Baur, 2013), which is why they have been controlled by farmers for millennia.Chemical weed control began just about a century ago with a few inorganic compounds, such as sulfuric acid, copper salts, and sodium chlorate (Cremlyn, 1991). The herbicidal activity of 2,4-dichlorophenoxyacetic acid was detected in the 1940s (Troyer, 2001). Büchel et al. (1977) and Cremlyn (1991), Worthington and Hance (1991). Targeted herbicide research began in the 1950s. In the early days, herbicide candidates progressed from screens purely on the basis of their having biology that would satisfy farmers’ requirements. Mode of action (MoA) studies did not play a major role in the chemical industry prior to the 1970s. Analytical tools were developed and the rapid elucidation of plant pathways and in vitro-based screen assays were used from the 1980s onward. However, in the 1990s and beyond, ever-increasing regulatory and economic pressures have changed the situation of the industry completely, and to satisfy the new requirements, selection criteria beyond biological activity have needed to be applied. Herbicide resistance in weeds has developed into a more serious problem that now constrains the application of certain types of herbicides in some markets. Finally, the introduction of crops resistant to cheap herbicides and of glyphosate-resistant soybean, in particular, took value out of the market and resulted in an enormous economic pressure on the herbicide-producing industry. As a result of this changing and more difficult landscape, the development of new molecules is now a rare event.

Table I.

History of chemical weed control innovationsPost, Postemergence application; Pre, preemergence application, based on data from Cremlyn (1991), Worthington and Hance (1991), Büchel et al. (1977), Herbicide Resistance Action Committee (www.hracglobal.com), and others.

Hepatitis C Virus (HCV) Sequence Variation Induces an HCV-Specific T-Cell Phenotype Analogous to Spontaneous Resolution

Hepatitis C virus (HCV)-specific CD8⁺ T cells in persistent HCV infection are low in frequency and paradoxically show a phenotype associated with controlled infections, expressing the memory marker CD127. We addressed to what extent this phenotype is dependent on the presence of cognate antigen. We analyzed virus-specific responses in acute and chronic HCV infections and sequenced autologous virus. We show that CD127 expression is associated with decreased antigenic stimulation after either viral clearance or viral variation. Our data indicate that most CD8 T-cell responses in chronic HCV infection do not target the circulating virus and that the appearance of HCV-specific CD127⁺ T cells is driven by viral variation.Hepatitis C virus (HCV) persists in the majority of acutely infected individuals, potentially leading to chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. The cellular immune response has been shown to play a significant role in viral control and protection from liver disease. Phenotypic and functional studies of virus-specific T cells have attempted to define the determinants of a successful versus an unsuccessful T-cell response in viral infections (10). So far these studies have failed to identify consistent distinguishing features between a T-cell response that results in self-limiting versus chronic HCV infection; similarly, the impact of viral persistence on HCV-specific memory T-cell formation is poorly understood.Interleukin-7 (IL-7) receptor alpha chain (CD127) is a key molecule associated with the maintenance of memory T-cell populations. Expression of CD127 on CD8 T cells is typically only observed when the respective antigen is controlled and in the presence of significant CD4⁺ T-cell help (9). Accordingly, cells specific for persistent viruses (e.g., HIV, cytomegalovirus [CMV], and Epstein-Barr virus [EBV]) have been shown to express low levels of CD127 (6, 12, 14) and to be dependent on antigen restimulation for their maintenance. In contrast, T cells specific for acute resolving virus infections, such as influenza virus, respiratory syncytial virus (RSV), hepatitis B virus (HBV), and vaccinia virus typically acquire expression of CD127 rapidly with the control of viremia (5, 12, 14). Results for HCV have been inconclusive. The expected increase in CD127 levels in acute resolving but not acute persisting infection has been found, while a substantial proportion of cells with high CD127 expression have been observed in long-established chronic infection (2). We tried to reconcile these observations by studying both subjects with acute and chronic HCV infection and identified the presence of antigen as the determinant of CD127 expression.Using HLA-peptide multimers we analyzed CD8⁺ HCV-specific T-cell responses and CD127 expression levels in acute and chronic HCV infection. We assessed a cohort of 18 chronically infected subjects as well as 9 individuals with previously resolved infection. In addition, we longitudinally studied 9 acutely infected subjects (5 individuals who resolved infection spontaneously and 4 individuals who remain chronically infected) (Tables ​(Tables11 and ​and2).2). Informed consent in writing was obtained from each patient, and the study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, as reflected in a priori approval from the local institutional review boards. HLA-multimeric complexes were obtained commercially from Proimmune (Oxford, United Kingdom) and Beckman Coulter (CA). The staining and analysis procedure was as described previously (10). Peripheral blood mononuclear cells (PBMCs) were stained with the following antibodies: CD3 from Caltag; CD8, CD27, CCR7, CD127, and CD38 from BD Pharmingen; and PD-1 (kindly provided by Gordon Freeman). Primer sets were designed for different genotypes based on alignments of all available sequences from the public HCV database (http://hcvpub.ibcp.fr). Sequence analysis was performed as previously described (8).

TABLE 1.

Patient information and autologous sequence analysis for patients with chronic and resolved HCV infection