Marketed therapeutic antibodies compendium

Therapeutic monoclonal antibodies (mAbs) are currently being approved for marketing in Europe and the United States, as well as other countries, on a regular basis. As more mAbs become available to physicians and patients, keeping track of the number, types, production cell lines, antigenic targets, and dates and locations of approvals has become challenging. Data are presented here for 34 mAbs that were approved in either Europe or the United States (US) as of March 2012, and nimotuzumab, which is marketed outside Europe and the US. Of the 34 mAbs, 28 (abciximab, rituximab, basiliximab, palivizumab, infliximab, trastuzumab, alemtuzumab, adalimumab, tositumomab-I131, cetuximab, ibrituximab tiuxetan, omalizumab, bevacizumab, natalizumab, ranibizumab, panitumumab, eculizumab, certolizumab pegol, golimumab, canakinumab, catumaxomab, ustekinumab, tocilizumab, ofatumumab, denosumab, belimumab, ipilimumab, brentuximab) are currently marketed in Europe or the US. Data for six therapeutic mAbs (muromonab-CD3, nebacumab, edrecolomab, daclizumab, gemtuzumab ozogamicin, efalizumab) that were approved but have been withdrawn or discontinued from marketing in Europe or the US are also included.Of the 28 mAbs currently marketed in the European Union or the US, 26 are marketed in Europe and 27 are marketed in the US, with 25 marketed in both regions (1 Of the 28 mAbs that are marketed in one or the other region, 43% (12/28) are produced in Chinese hamster ovary (CHO) cells, 25% (7/28) are produced in SP2/0 cells,² 18% (5/28) are produced in NS0 cells,³ and 7% (2/28) are produced in hybridomas. The remaining two products (ranibizumab, certolizumab pegol) are antigen-binding fragments (Fab) that are produced in E. coli. Humanized and human mAbs comprise 36% (10/28) and 32% (9/28) of the total, respectively, while 21% (6/28) are chimeric and 11% (3/28) are murine. Most (75%; 21/28) are canonical full-length mAbs. Of the 7 non-canonical mAbs, three (abciximab, ranibizumab, certolizumab pegol) are Fab, with one of these (certolizumab pegol) pegylated; two (tositumomab-I131, ibrituximab tiuxetan) are radiolabeled when administered to patients; one (brentuximab vedotin) is an antibody-drug conjugate (ADC); and one is bispecific (catumaxomab). Although 16 marketed mAbs target unique antigens, CD20 and tumor necrosis factor are each targeted by 4 mAbs, and epidermal growth factor receptor (EGFR) and vascular endothelial growth factor are each targeted by 2 mAbs. If approved, pertuzumab, which is undergoing regulatory review in Europe and the US as a treatment for breast cancer, would be one of 2 mAbs that target human epidermal growth factor receptor 2 on the market.Table 1. Therapeutic monoclonal antibodies marketed or in review in the European Union or United States

Genome-wide analysis of lipoxygenase gene family in Arabidopsis and rice

The enzymes called lipoxygenases (LOXs) can dioxygenate unsaturated fatty acids, which leads to lipoperoxidation of biological membranes. This process causes synthesis of signaling molecules and also leads to changes in cellular metabolism. LOXs are known to be involved in apoptotic (programmed cell death) pathway, and biotic and abiotic stress responses in plants. Here, the members of LOX gene family in Arabidopsis and rice are identified. The Arabidopsis and rice genomes encode 6 and 14 LOX proteins, respectively, and interestingly, with more LOX genes in rice. The rice LOXs are validated based on protein alignment studies. This is the first report wherein LOXs are identified in rice which may allow better understanding the initiation, progression and effects of apoptosis, and responses to bitoic and abiotic stresses and signaling cascades in plants.Key words: apoptosis, biotic and abiotic stresses, genomics, jasmonic acid, lipidsLipoxygenases (linoleate:oxygen oxidoreductase, EC 1.13.11.-; LOXs) catalyze the conversion of polyunsaturated fatty acids (lipids) into conjugated hydroperoxides. This process is called hydroperoxidation of lipids. LOXs are monomeric, non-heme and non-sulfur, but iron-containing dioxygenases widely expressed in fungi, animal and plant cells, and are known to be absent in prokaryotes. However, a recent finding suggests the existence of LOX-related genomic sequences in bacteria but not in archaea.¹ The inflammatory conditions in mammals like bronchial asthama, psoriasis and arthritis are a result of LOXs reactions.² Further, several clinical conditions like HIV-1 infection,³ disease of kidneys due to the activation of 5-lipoxygenase,^4,5 aging of the brain due to neuronal 5-lipoxygenase⁶ and atherosclerosis⁷ are mediated by LOXs. In plants, LOXs are involved in response to biotic and abiotic stresses.⁸ They are involved in germination⁹ and also in traumatin and jasmonic acid biochemical pathways.^10,11 Studies on LOX in rice are conducted to develop novel strategies against insect pests¹² in response to wounding and insect attack,¹³ and on rice bran extracts as functional foods and dietary supplements for control of inflammation and joint health.¹⁴ In Arabidopsis, LOXs are studied in response to natural and stress-induced senescence,¹⁵ transition to flowering,¹⁶ regulation of lateral root development and defense response.¹⁷The arachidonic, linoleic and linolenic acids can act as substrates for different LOX isozymes. A hydroperoxy group is added at carbons 5, 12 or 15, when arachidonic acid is the substrate, and so the LOXs are designated as 5-, 12- or 15-lipoxygenases. Sequences are available in the database for plant lipoxygenases (EC:1.13.11.12), mammalian arachidonate 5-lipoxygenase (EC:1.13.11.34), mammalian arachidonate 12-lipoxygenase (EC:1.13.11.31) and mammalian erythroid cell-specific 15-lipoxygenase (EC:1.13.11.33). The prototype member for LOX family, LOX-1 of Glycine max L. (soybean) is a 15-lipoxygenase. The LOX isoforms of soybean (LOX-1, LOX-2, LOX-3a and LOX-3b) are the most characterized of plant LOXs.¹⁸ In addition, five vegetative LOXs (VLX-A, -B, -C, -D, -E) are detected in soybean leaves.¹⁹ The 3-dimensional structure of soybean LOX-1 has been determined.^20,21 LOX-1 was shown to be made of two domains, the N-terminal domain-I which forms a β-barrel of 146 residues, and a C-terminal domain-II of bundle of helices of 693 residues²¹ (Fig. 1). The iron atom was shown to be at the centre of domain-II bound by four coordinating ligands, of which three are histidine residues.²²Open in a separate windowFigure 1Three-dimensional structure of soybean lipoxygenase L-1. The domain I (N-terminal) and domain II (C-terminal) are indicated. The catalytic iron atom is embedded in domain II (PDB ID-1YGE).²¹This article describes identification of LOX genes in Arabidopsis and rice. The Arabidopsis genome encodes for six LOX proteins²³ (www.arabidopsis.org) (

Human Genetic Disorders of Axon Guidance

This article reviews symptoms and signs of aberrant axon connectivity in humans, and summarizes major human genetic disorders that result, or have been proposed to result, from defective axon guidance. These include corpus callosum agenesis, L1 syndrome, Joubert syndrome and related disorders, horizontal gaze palsy with progressive scoliosis, Kallmann syndrome, albinism, congenital fibrosis of the extraocular muscles type 1, Duane retraction syndrome, and pontine tegmental cap dysplasia. Genes mutated in these disorders can encode axon growth cone ligands and receptors, downstream signaling molecules, and axon transport motors, as well as proteins without currently recognized roles in axon guidance. Advances in neuroimaging and genetic techniques have the potential to rapidly expand this field, and it is feasible that axon guidance disorders will soon be recognized as a new and significant category of human neurodevelopmental disorders.The human brain is highly organized and contains a myriad of axon tracts that follow precise pathways and make predictable connections. Model organism research has provided tremendous advances in our understanding of the principles and molecules governing axon growth and guidance. Remarkably, however, only a handful of human disorders resulting from primary errors in these processes have been identified.Traditional tools of the physician have limited sensitivity and specificity to detect human disorders of axon guidance. In particular, congenital synkinesis may be the only physical examination finding that has been attributed to such disorders. Synkinesis is the involuntary and pathological contraction of a muscle simultaneously with contraction of the intended muscle, and is typically reported with hand/finger or eye/eyelid movements and confirmed by electrophysiological studies. Mirror movement synkinesis refers to the contraction of homologous hand/finger muscles bilaterally when one attempts to move only one hand (Schott and Wyke 1981). In humans, 75%–90% of corticospinal tract (CST) fibers normally decussate in the lower medulla. Mirror movement synkinesis occurs in several human disorders with pathological, neuroimaging, and/or electrophysiological evidence of reduced CST decussation, including Joubert, Kallmann, and Klippel-Feil syndromes (Vulliemoz et al. 2005; Cincotta and Ziemann 2008). In some individuals with mirror movements, electrophysiological data are also consistent with bilateral engagement of the motor corticies (Leinsinger et al. 1997). Ocular synkinesis refers to aberrant patterns of eye movement and accompanies various congenital cranial dysinnervation disorders (CCDDs) (Gutowski et al. 2003; Engle 2007), including CFEOM, Duane syndrome, and Marcus Gunn jaw-winking phenomenon (Fig. 1). Finger and ocular movements require precise motor control, and errors in innervation of these muscles may be more easily detected than errors in the wiring of larger muscle groups. If true, this suggests that the clinical exam could fail to recognize many guidance errors in both the peripheral and central nervous system.Open in a separate windowFigure 1.Ocular synkinesis. (A) Child with CFEOM1 and Marcus Gunn jaw-winking phenomenon harboring a KIF21A mutation. His superior branch of the oculomotor nerve is hypoplastic/absent, resulting in bilateral ptosis from lack of appropriate innervation of the levator palpebrae superioris (LPS) muscle, and a downward position of each eye from absent innervation of the superior rectus muscle (left). Marcus Gunn phenomenon (right) is seen as the synkinetic elevation of the left eyelid with a subtle change in jaw position associated with a volitional increase in pterygoid muscle tension. This results from aberrant innervation of the LPS by axons from the motor branch of the trigeminal nerve that also innervates the intended ipsilateral pterygoid muscle. (B) Adult with Duane retraction syndrome harboring a CHN1 mutation. Central gaze reveals mild exotropia (middle). On attempted right gaze (left) and left gaze (right), there is limited horizontal excursion with globe retraction and secondary palpebral fissure narrowing of the adducting eye. Globe retraction results from synkinesis of the medial and lateral recti muscles. (A) Modified with permission from Yamada et al. 2005. Copyright © (2005) American Medial Association. All rights reserved. (B) Modified from Demer et al. 2007. Copyright © (2007) Association for Research in Vision and Ophthalmology. All rights reserved.The physician’s ability to detect disorders of axon guidance has been augmented by classical pathological, radiological, and electrophysiological techniques. Diagnostic radiologic and postmortem neuropathological studies detect overall changes in white matter volume and major abnormalities of axon tracts demarcated from the background such as the corpus callosum, anterior and posterior commissures, optic chiasm, and cerebellar peduncles. Neuropathological studies can also detect absence of axons that normally cross the midline at many points in the brain stem and spinal cord, which are more difficult to visualize by standard magnetic resonance imaging (MRI). Electrophysiological studies such as evoked potentials can reveal aberrant central connections of peripheral sensory or motor nerves.The genetic disorders with aberrant axon connectivity presented in this article have been defined primarily using traditional approaches described above. Exciting advances in neuroimaging and genetics, however, are revolutionizing the ability to define axon guidance disorders, and it is likely that these syndromes are only the first of an important new category of such human neurodevelopmental disorders. Detailed fiber tract anatomy can now be visualized using noninvasive tractography such as diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI). These techniques provide tract orientation by determining the anisotropic properties of water diffusion, and can be used to reconstruct the trajectories of fiber systems in three-dimensional space (Tovar-Moll et al. 2007; Wahl et al. 2009). Tractography has successfully confirmed aberrant projections in several of the disorders discussed below (Fig. 2). At the same time, human genetics now provides an unbiased approach to identify the etiologies of disorders with aberrant axon tracts. For some syndromes, animal and in vitro studies have confirmed that the encoded protein has a primary role in axon guidance. For others, such studies reveal a primary role in neuronal specification and/or migration rather than, or in addition to, a role in axon guidance. Finally, some neurodevelopmental disorders without clinical, pathologic, or radiologic evidence of aberrant axon tracts have been found to result from mutations in genes that contribute to axon guidance in animal models.Open in a separate windowFigure 2.Tractography studies in patients with partial agenesis of the corpus callosum (pACC). T1-weighted anatomic images and DTI tractography of six subjects with pACC (top panels) and two representative controls (bottom panel). Axial (left) and midline sagittal (middle) T1 sections are shown for each subject. Callosal fragments are identified with yellow arrows, and heterotopic fibers visible on T1-weighted images are denoted by red arrows. Midline sagittal DTI color maps are shown with segmented callosal fibers (right). For subjects with pACC, connectivity ranged from anterior frontal connections (subject 3) to only posterior frontal and occipitotemporal connections (subject 4). One individual (subject 5) displayed a discontinuous set of homotopic callosal connections, with anterior frontal and occipitotemporal connectivity without any posterior frontal or parietal connections. Control subjects (bottom panel) display normal callosal morphology and tractography results. Tracts are segmented and colored according to their cortical projections: homotopic anterior frontal, blue; homotopic posterior frontal, orange; homotopic parietal, pink; homotopic occipitotemporal, green; heterotopic left anterior-right posterior, yellow; heterotopic right anterior-left posterior, red. (Reprinted, with permission, from Wahl et al. 2009 [© AJNR].)The major human genetic disorders that result, or are proposed to result, from defective axon guidance are ordered below from rostral to caudal based on the location of the aberrant axons tracts. These include genetic mutations that alter axon growth cone ligands and receptors, downstream signaling molecules, and axon transport, as well as proteins without currently recognized roles in axon guidance (Fig. 3) (Open in a separate windowFigure 3.Schematic representation of gene products implicated in human disorders of axon guidance. KAL1 (anosmin) and PROK2 are shown as secreted ligands. ROBO3, L1, and PROKR2 are shown as transmembrane receptors on the growth cone. CHN1 is depicted with 3 green domains (SH2, C1, RacGAP), responding to an unknown activated receptor and altering a microtubule, which is depicted as a brown line. KIF21A dimers are depicted walking down MTs. The OCA/OA and JSRD gene products are not depicted. Note: these gene products are not necessarily expressed in the same neurons or function in the same pathways.

Table 1

Summary of major human genetic disorders resulting, or hypothesized to result, from errors in axon growth and guidance

Pulsed-Field Gel Electrophoresis Profile Changes Resulting from Spontaneous Chromosomal Deletions in Enterohemorrhagic Escherichia coli O157:H7 during Passage in Cattle

A total of 905 enterohemorrhagic Escherichia coli (EHEC) O157:H7 isolates that were recovered from experimentally infected cattle, in addition to the inoculated strain, were analyzed by pulsed-field gel electrophoresis (PFGE). Twelve PFGE profiles other than that of the inoculated strain were observed. We successfully identified five distinct chromosomal deletions that affected the PFGE profiles using whole-genome PCR scanning and DNA sequencing analysis. The changes in PFGE profiles of EHEC O157:H7 isolates after passage through the intestinal tract of cattle were partially generated by deletion of chromosomal regions.Enterohemorrhagic Escherichia coli (EHEC) O157:H7 causes hemorrhagic colitis and hemolytic-uremic syndrome in humans worldwide (18). Cattle are considered the primary reservoir for this pathogen and play a central role in transmission to humans (6). Healthy cattle transiently carry EHEC O157:H7 and shed the bacteria in their feces (5, 7). Human infections have been associated with the consumption of contaminated meat and milk, direct contact with cattle, and the consumption of vegetables, fruits, and water contaminated with cattle manure (6).Because of its high discriminatory power, pulsed-field gel electrophoresis (PFGE) has been widely employed as a molecular typing method in many epidemiological investigations to identify various outbreaks and routes of transmission of EHEC O157:H7 (1, 12, 15, 17). Simpson''s index of diversity (9) was reported to be >0.985 in previous studies (1, 15), supporting the identification of richness (the number of types among isolates) and evenness (the relative distribution of individual strains among the different types) of molecular typing using PFGE.Instability of the PFGE patterns of EHEC O157:H7 isolates has been reported. Changes in PFGE patterns were observed among strains after repeated subculturing and prolonged storage at room temperature (11). Loss of Shiga toxin genes and a large-scale inversion within the genome were identified as genetic events generating changes in PFGE patterns in vitro (10, 13). Shifts in the genotypes of EHEC O157:H7 clinical isolates from patients and cattle have been reported (3, 14). This phenomenon was also observed in EHEC O157:H7 experimental infections of cattle. Spontaneous curing of a 90-kb plasmid resulted in the loss of two restricted fragments from the PFGE profiles of EHEC O157:H7 isolates obtained from experimentally infected cattle (2). The purpose of the present study was to identify the genetic events affecting the PFGE patterns of EHEC O157:H7 after passage through the intestinal tract of cattle, especially for restriction fragments that are >90 kb long.Four 5-month-old Holstein steers were housed individually in climate-controlled biosafety level 2 containment barns in accordance with the guidelines for animal experimentation defined by the National Institute of Animal Health of Japan. The pens had individual floor drains and were cleaned twice daily with water and disinfectant. All animals were healthy and culture negative for EHEC O157:H7 strains, as determined by a previously described technique (2), prior to inoculation.EHEC O157:H7 strain Sakai-215 (12, 23), which was isolated from an outbreak in Sakai, Osaka Prefecture, in 1996 was used for inoculation. This strain harbors the genes encoding Stx1 and Stx2. A spontaneous resistant strain was selected with nalidixic acid in order to facilitate the recovery of this strain from fecal samples. All calves were inoculated using a stomach tube with an exponential-phase culture (10⁹ CFU) of the nalidixic acid-resistant Sakai-215 strain. Fecal samples were collected from the four calves daily for 45 days. Fecal culturing was performed as described previously (2). Eight non-sorbitol-fermenting colonies were selected daily from each animal and identified as EHEC O157:H7 colonies by routine diagnostic methods (25).All animals were clinically normal throughout the experimental period. The EHEC O157:H7-inoculated calves (calves 1 to 4) were culture positive for the organism 24 h after inoculation. Intermittent fecal shedding by the calves was observed until 27, 32, 26, and 39 days postinoculation for calves 1, 2, 3, and 4, respectively (Fig. ​(Fig.1).1). The numbers of EHEC O157:H7 isolates recovered from calves 1, 2, 3, and 4 were 200, 224, 200, and 281, respectively.Open in a separate windowFIG. 1.Changes in PFGE profiles of EHEC O157:H7 isolates recovered from calves 1 (A), 2 (B), 3 (C), and 4 (D). The absence of a bar indicates that no EHEC O157:H7 was detected. The open horizontal bars under the vertical bars indicate that the eight isolates obtained on a day were obtained from the enrichment culture.A total of 905 recovered isolates in addition to the inoculated strain were used for PFGE analysis. Genomic DNA from each EHEC O157:H7 isolate was prepared using the method of Persing (Mayo Clinic, Rochester, MN) described by Rice et al. (20). Agarose-embedded chromosomal DNA was cleaved with XbaI by following the manufacturer''s instructions. PFGE was performed in a 0.85% megabase agarose gel, using a CHEF DR III apparatus (Bio-Rad Laboratories). The pulse time was increased from 12 to 35 s for 18 h. The PFGE profiles of all of the EHEC O157:H7 isolates recovered from the four calves were compared with that of the inoculated strain. The number of band differences was determined by enumerating the loss and addition of fragments (22).Two hundred eighty-nine isolates had PFGE profiles different from that of the inoculated strain, and 12 distinct PFGE profiles were identified for these isolates (Table ​(Table1).1). The fact that only one to three band differences were observed for the 12 profiles suggested that these isolates were closely related (22) and were variants of the inoculated strain. In addition, the pens had individual floor drains and were cleaned twice daily with water and disinfectant, which reduced the likelihood of introduction of novel EHEC O157:H7 strains. We designated the PFGE profiles A to L. PFGE profiles A, C, and H were obtained for all four calves and accounted for 30.4% of the 905 isolates recovered. Different PFGE profiles were obtained for all animals at least 2 days postinoculation (Fig. ​(Fig.1).1). All eight isolates from calf 2 collected on day 15 postinoculation and from calf 3 collected on days 22 and 23 postinoculation had PFGE profiles different from that of the original isolate (Fig. ​(Fig.1).1). The isolates that had the same PFGE profile as the inoculated strain were detected again later.

TABLE 1.

Temporal distribution of PFGE profiles of EHEC O157:H7 isolates recovered from experimentally infected cattle

Genome-wide analysis of thioredoxin fold superfamily peroxiredoxins in Arabidopsis and rice

A broad range of peroxides generated in subcellular compartments, including chloroplasts, are detoxified with peroxidases called peroxiredoxins (Prx). The Prx are ubiquitously distributed in all organisms including bacteria, fungi, animals and also in cyanobacteria and plants. Recently, the Prx have emerged as new molecules in antioxidant defense in plants. Here, the members which belong to Prx gene family in Arabidopsis and rice are been identified. Overall, the Prx members constitute a small family with 10 and 11 genes in Arabidopsis and rice respectively. The prx genes from rice are assigned to their functional groups based on homology search against Arabidopsis protein database. Deciphering the Prx functions in rice will add novel information to the mechanism of antioxidant defense in plants. Further, the Prx also forms the part of redox signaling cascade. Here, the Prx gene family has been described for rice.Key words: antioxidant defense, chloroplast, gene family, oxidative stress, reactive oxygen speciesThe formation of free radicals and reactive oxygen species (ROS) occur in several enzymatic and non-enzymatic reactions during cellular metabolism. The accumulation of these reactive and deleterious intermediates is suppressed by antioxidant defense mechanism comprised of low molecular weight antioxidants and enzymes. In photosynthetic organisms, the defense against the damage from free radicals and oxidative stress is crucial. For instance, the ROS production occurs in photosystem II with generation of singlet oxygen (¹O₂) and hydrogen peroxide (H₂O₂),^1,2 photosystem I from superoxide anion radicals (O₂⁻),³ and during photorespiration with generation of H₂O₂.⁴ ROS production may exceed under environmental stress conditions like excess light, low temperature and drought.⁵The antioxidant defense mechanism is activated by antioxidant metabolities and enzymes which detoxify ROS and lipid peroxides. The detoxification of ROS can occur in various cellular compartments such as chloroplasts, mitochondria, peroxisomes and cytosol.⁶ The enzymes like ascorbate peroxidase, catalase, glutathione peroxidase and superoxide dismutase are prominent antioxidant enzymes.⁶ The peroxiredoxins (Prx) emerged as new components in the antioxidant defense network of barley.^7,8 Later, Prx were studied in other plants.^9–14Prx can be classified into four different functional groups, PrxQ, 1-Cys Prx, 2-Cys Prx and Type-2 Prx.^15,16 They are members of the thioredoxin fold superfamily.^17,18 In this study, the prx genes found in Arabidopsis and rice genomes are been identified. The Arabidopsis genome encodes 10 prx genes classified into four functional categories, 1-Cys Prx, 2-Cys Prx, PrxQ and Type-2 Prx.¹³ Of these, one each of 1-Cys Prx and PrxQ, two of 2-Cys Prx (2-Cys PrxA and 2-Cys PrxB) and six Type-2 Prx (PrxA–F) are identified¹³ (LocusAnnotationSynonymA^*B^*C^*AT1G481301-Cysteine peroxiredoxin 1 (ATPER1)1-Cys Prx21624081.36.603AT1G60740Peroxiredoxin type 2Type-2 PrxD16217471.95.2297AT1G65970Thioredoxin-dependent peroxidase 2 (TPX2)Type-2 PrxC16217413.95.2297AT1G65980Thioredoxin-dependent peroxidase 1 (TPX1)Type-2 PrxB16217427.84.9977AT1G65990Type 2 peroxiredoxin-relatedType-2 PrxA55362653.66.4368AT3G06050Peroxiredoxin IIF (PRXIIF)Type-2 PrxF20121445.29.3905AT3G116302-Cys Peroxiredoxin A (2CPA, 2-Cys PrxA)2-Cys PrxA26629091.77.5686AT3G26060ATPRX Q, periredoxin QPrxQ21623677.810.0565AT3G52960Peroxiredoxin type 2Type-2 PrxE23424684.09.572AT5G062902-Cysteine Peroxiredoxin B (2CPB, 2-Cys PrxB)2-Cys PrxB27329779.55.414Open in a separate window^*A, amino acids; B, molecular weight; C, isoelectric point.In rice (rice.plantbiology.msu.edu/), there are 11 genomic loci which encode for Prx proteins (​and33). Interestingly, a new prx gene (LOC_Os07g15670) annotated as “peroxiredoxin, putative, expressed” is identified making the tally of prx genes to eleven in rice as compared to ten in Arabidopsis (​and22). The BLAST search has identified its counterpart in Arabidopsis which has been annotated as “antioxidant/oxidoreductase” (AT1G21350) in the TAIR database (www.arabidopsis.org). The rice LOC_Os07g15670 and Arabidopsis AT1G21350 share protein homology %68/78 for 236 amino acids (ChromosomeLocus IdPutative function/AnnotationA^*B^*C^*1LOC_Os01g16152peroxiredoxin, putative, expressed19920873.68.22091LOC_Os01g24740peroxiredoxin-2E-1, chloroplast precursor, putative10711591.56.79061LOC_Os01g48420peroxiredoxin, putative, expressed16317290.85.68282LOC_Os02g09940peroxiredoxin, putative, expressed22623179.56.5352LOC_Os02g33450peroxiredoxin, putative, expressed26228096.95.77094LOC_Os04g339702-Cys peroxiredoxin BAS1, chloroplast precursor, putative, expressed12213410.24.37056LOC_Os06g09610peroxiredoxin, putative, expressed2662892610.50976LOC_Os06g42000peroxiredoxin, putative, expressed23323688.39.20597LOC_Os07g15670peroxiredoxin, putative, expressed25327684.69.85457LOC_Os07g44440peroxiredoxin, putative, expressed22124232.65.36187LOC_Os07g44430peroxiredoxin, putative25627785.36.8544Open in a separate window^*A, amino acids; B, molecular weight; C, isoelectric point.

Table 3

Identification of rice homologs of peroxiredoxins in A. thaliana

Mouse Models of Osteoarthritis: A Summary of Models and Outcomes Assessment

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease. It has a substantial impact on patient quality of life and is a common cause of pain and mobility issues in older adults. The functional limitations, lack of curative treatments, and cost to society all demonstrate the need for translational and clinical research. The use of OA models in mice is important for achieving a better understanding of the disease. Models with clinical relevance are needed to achieve 2 main goals: to assess the impact of the OA disease (pain and function) and to study the efficacy of potential treatments. However, few OA models include practical strategies for functional assessment of the mice. OA signs in mice incorporate complex interrelations between pain and dysfunction. The current review provides a comprehensive compilation of mouse models of OA and animal evaluations that include static and dynamic clinical assessment of the mice, merging evaluation of pain and function by using automatic and noninvasive techniques. These new techniques allow simultaneous recording of spontaneous activity from thousands of home cages and also monitor environment conditions. Technologies such as videography and computational approaches can also be used to improve pain assessment in rodents but these new tools must first be validated experimentally. An example of a new tool is the digital ventilated cage, which is an automated home-cage monitor that records spontaneous activity in the cages.

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease.³⁶ Functional limitations, the absence of curative treatments, and the considerable cost to society result in a substantial impact on quality of life.⁷⁶ Historically, OA has been described as whole joint and whole peri-articular diseases and as a systemic comorbidity.^9,111 OA consists of a disruption of articular joint cartilage homeostasis leading to a catabolic pathway characterized by chondrocyte degeneration and destruction of the extracellular matrix (ECM). Low-grade chronic systemic inflammation is also actively involved in the process.^42,92 In clinical practice, mechanical pain, often accompanied by a functional decline, is the main reason for consultations. Recommendations to patients provide guidance for OA management.^{22, 33,49,86} Evidence-based consensus has led to a variety of pharmacologic and nonpharmacologic modalities that are intended to guide health care providers in managing symptomatic patients. Animal-based research is of tremendous importance for the study of early diagnosis and treatment, which are crucial to prevent the disease progression and provide better care to patients.The purpose of animal-based OA research is 2-fold: to assess the impact of the OA disease (pain and function) and to study the efficacy of a potential treatment.^18,67 OA model species include large animals such as the horse, goat, sheep, and dog, whose size and anatomy are expected to better reflect human joint conditions. However, small animals such as guinea pig, rabbit, mouse, and rat represent 77% of the species used.^1,87 In recent years, mice have become the most commonly used model for studying OA. Mice have several advantageous characteristics: a short development and life span, easy and low-cost breeding and maintenance, easy handling, small joints that allow histologic analysis of the whole joint,³² and the availability of genetically modified lines.¹⁰⁸ Standardized housing, genetically defined strains and SPF animals reduce the genetic and interindividual acquired variability. Mice are considered the best vertebrate model in terms of monitoring and controlling environmental conditions.^7,14,15,87 Mouse skeletal maturation is reached at 10 wk, which theoretically constitutes the minimal age at which mice should be entered into an OA study.^64,87,102 However, many studies violate this limit by testing mice at 8 wk of age.Available models for OA include the following (32,111 physical activity and exercise induced OA; noninvasive mechanical loading (repetitive mild loading and single-impact injury); and surgically induced (meniscectomy models or anterior cruciate ligament transection). The specific model used would be based on the goal of the study.⁷ For example, OA pathophysiology, OA progression, and OA therapies studies could use spontaneous, genetic, surgical, or noninvasive models. In addition, pain studies could use chemical models. Lastly, post-traumatic studies would use surgical or noninvasive models; the most frequently used method is currently destabilization of the medial meniscus,³² which involves transection of the medial meniscotibial ligament, thereby destabilizing the joint and causing instability-driven OA. An important caveat for mouse models is that the mouse and human knee differ in terms of joint size, joint biomechanics, and histologic characteristics (layers, cellularity),^32,64 and joint differences could confound clinical translation.¹⁰ Table 1. Mouse models of osteoarthritis.

Cross-Subtype Neutralization Sensitivity despite Monoclonal Antibody Resistance among Early Subtype A,C, and D Envelope Variants of Human Immunodeficiency Virus Type 1

The human immunodeficiency virus type 1 (HIV-1) variants that are transmitted to newly infected individuals are the primary targets of interventions, such as vaccines and microbicides, aimed at preventing new infections. Newly acquired subtype A, B, and C variants have been the focus of neutralization studies, although many of these viruses, particularly of subtypes A and B, represent viruses circulating more than a decade ago. In order to better represent the global diversity of transmitted HIV-1 variants, an additional 31 sexually transmitted Kenyan HIV-1 env genes, representing several recent infections with subtype A, as well as subtypes A/D, C, and D, were cloned, and their neutralization profiles were characterized. Most env variants were resistant to neutralization by the monoclonal antibodies (MAbs) b12, 4E10, 2F5, and 2G12, suggesting that targeting the epitopes of these MAbs may not be effective against variants that are spreading in areas of endemicity. However, significant cross-subtype neutralization by plasma was observed, indicating that there may be other epitopes, not yet defined by the limited available MAbs, which could be recognized more broadly.Most effective viral vaccines are thought to provide protection primarily by stimulating neutralizing antibodies (NAbs) to clear cell-free virus (25, 27). Because protection by NAbs requires recognition of common viral epitopes, the extreme genetic diversity of human immunodeficiency virus type 1 (HIV-1) presents a particular challenge to NAb-based vaccine approaches. Therefore, a critical starting point for studies of immune-mediated protection against HIV-1 is a collection of newly transmitted HIV-1 variants, particularly from areas of endemicity, such as sub-Saharan Africa, in order to determine whether vaccines are appropriately targeted to common epitopes from these relevant transmitted strains.During HIV-1 transmission, a bottleneck allows only one or a few variants to be transmitted to a newly infected individual (6, 9, 16, 29, 34, 37, 39), and the sensitivity of these early transmitted strains to antibody-mediated neutralization is therefore of particular interest. Newly transmitted HIV-1 variants have demonstrated significant heterogeneity in their neutralization phenotypes both within and between subtypes (2, 3, 6-8, 11, 13-15, 22, 30, 32, 36). Panels of sexually transmitted HIV-1 envelope variants (based on the envelope gene, env) have been characterized, including subtype B variants from North America, Trinidad, and Europe, subtype C variants from South Africa and Zambia, and subtype A variants from Kenya collected between 1994 and 1996 (2, 14, 15). Here, we characterize an additional 31 envelope variants from 14 subjects with sexually transmitted HIV-1 who were infected in Kenya, where subtypes A, C, and D circulate, between 1993 and 2005 (24, 31).The env genes were cloned from samples drawn 14 to 391 (median, 65) days postinfection from individuals enrolled in a prospective cohort of high-risk women in Mombasa, Kenya (19-21). Demographic characteristics of the subjects are summarized in Table ​Table1;1; the timing of first infection was determined by both HIV-1 serology and HIV RNA testing as described previously (12). All of the subjects were presumably infected by male-to-female transmission and displayed a range of plasma viral loads at the time of env gene cloning (Table ​(Table1).1). For most individuals, full-length env genes were cloned from uncultured peripheral blood mononuclear cell (PBMC) DNA, though for two individuals, clones were obtained from DNA following short-term coculture with donor PBMCs (Table ​(Table1).1). env genes were cloned by single-copy nested PCR with primers and PCR conditions as described previously (4, 17). We tested env genes for their ability to mediate infection by transfecting env plasmid DNA into 293T cells along with an env-deficient HIV-1 subtype A proviral plasmid, Q23Δenv, to make pseudoviral particles (17). More than 80 env clones were obtained from 16 subjects; less than one-half were functional on the basis of the infectivity of pseudoviral particles in a single-round infection of TZM-bl cells (AIDS Research and Reference Reagent Program, National Institutes of Health), as observed previously for env genes cloned from proviral sequences (17); a lower fraction of functional env genes have been reported from plasma (18). We focused on the proviral sequences here because they presumably best represent the sequence closest to that of the transmitted strains. The 31 functional env variants are described in Table ​Table11.

TABLE 1.

Demographic characteristics, diversities, gp120 variable-region lengths, numbers of PNGS, and accession numbers of cloned env variants

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 ( Data Phase Astronomy Twitter YouTube Genomics Acquisition 25 zetta-bytes/year0.5–15 billion tweets/year500–900 million hours/year1 zetta-bases/year Storage 1 EB/year1–17 PB/year1–2 EB/year2–40 EB/year Analysis In situ data reductionTopic and sentiment miningLimited requirementsHeterogeneous data and analysisReal-time processingMetadata analysisVariant calling, ~2 trillion central processing unit (CPU) hoursMassive volumesAll-pairs genome alignments, ~10,000 trillion CPU hours Distribution Dedicated lines from antennae to server (600 TB/s)Small units of distributionMajor component of modern user’s bandwidth (10 MB/s)Many small (10 MB/s) and fewer massive (10 TB/s) data movementOpen in a separate window     

Aluminum induced proteome changes in tomato cotyledons

Cotyledons of tomato seedlings that germinated in a 20 µM AlK(SO₄)₂ solution remained chlorotic while those germinated in an aluminum free medium were normal (green) in color. Previously, we have reported the effect of aluminum toxicity on root proteome in tomato seedlings (Zhou et al.¹). Two dimensional DIGE protein analysis demonstrated that Al stress affected three major processes in the chlorotic cotyledons: antioxidant and detoxification metabolism (induced), glyoxylate and glycolytic processes (enhanced), and the photosynthetic and carbon fixation machinery (suppressed).Key words: aluminum, cotyledons, proteome, tomatoDifferent biochemical processes occur depending on the developmental stages of cotyledons. During early seed germination, before the greening of the cotyledons, glyoxysomes enzymes are very active. Fatty acids are converted to glucose via the gluconeogenesis pathway.^2,3 In greening cotyledons, chloroplast proteins for photosynthesis and leaf peroxisomal enzymes in the glycolate pathway for photorespiration are metabolized.^2–4 Enzymes involved in regulatory mechanisms such as protein kinases, protein phosphatases, and mitochondrial enzymes are highly expressed.^3,5,6The chlorotic cotyledons are similar to other chlorotic counterparts in that both contains lower levels of chlorophyll, thus the photosynthetic activities are not as active. In order to understand the impact of Al on tomato cotyledon development, a comparative proteome analysis was performed using 2D-DIGE following the as previously described procedure.¹ Some proteins accumulated differentially in Al-treated (chlorotic) and untreated cotyledons (Fig. 1). Mass spectrometry of tryptic digestion fragments of the proteins followed by database search has identified some of the differentially expressed proteins (Open in a separate windowFigure 1Image of protein spots generated by Samspot analysis of Al treated and untreated tomato cotyledons proteomes separated on 2D-DIGE.

Table 1

Proteins identified from tomato cotyledons of seeds germinating in Al-solution

Peptidoglycan Fine Structure of the Radiotolerant Bacterium Deinococcus radiodurans Sark

Peptidoglycan from Deinococcus radiodurans was analyzed by high-performance liquid chromatography and mass spectrometry. The monomeric subunit was: N-acetylglucosamine–N-acetylmuramic acid–l-Ala–d-Glu-(γ)–l-Orn-[(δ)Gly-Gly]–d-Ala–d-Ala. Cross-linkage was mediated by (Gly)₂ bridges, and glycan strands were terminated in (1→6)anhydro-muramic acid residues. Structural relations with the phylogenetically close Thermus thermophilus are discussed.The gram-positive bacterium Deinococcus radiodurans is remarkable because of its extreme resistance to ionizing radiation (14). Phylogenetically the closest relatives of Deinococcus are the extreme thermophiles of the genus Thermus (4, 11). In 16S rRNA phylogenetic trees, the genera Thermus and Deinococcus group together as one of the older branches in bacterial evolution (11). Both microorganisms have complex cell envelopes with outer membranes, S-layers, and ornithine-Gly-containing mureins (7, 12, 19, 20, 22, 23). However, Deinococcus and Thermus differ in their response to the Gram reaction, having positive and negative reactions, respectively (4, 14). The murein structure for Thermus thermophilus HB8 has been recently elucidated (19). Here we report the murein structure of Deinococcus radiodurans with similar detail.D. radiodurans Sark (23) was used in the present study. Cultures were grown in Luria-Bertani medium (13) at 30°C with aeration. Murein was purified and subjected to amino acid and high-performance liquid chromatography (HPLC) analyses as previously described (6, 9, 10, 19). For further analysis muropeptides were purified, lyophilized, and desalted as reported elsewhere (6, 19). Purified muropeptides were subjected to plasma desorption linear time-of-flight mass spectrometry (PDMS) as described previously (1, 5, 16, 19). Positive and negative ion mass spectra were obtained on a short linear ²⁵²californium time-of-flight instrument (BioIon AB, Uppsala, Sweden). The acceleration voltage was between 17 and 19 kV, and spectra were accumulated for 1 to 10 million fission events. Calibration of the mass spectra was done in the positive ion mode with H⁺ and Na⁺ ions and in the negative ion mode with H⁻ and CN⁻ ions. Calculated m/z values are based on average masses.Amino acid analysis of muramidase (Cellosyl; Hoechst, Frankfurt am Main, Germany)-digested sacculi (50 μg) revealed Glu, Orn, Ala, and Gly as the only amino acids in the muramidase-solubilized material. Less than 3% of the total Orn remained in the muramidase-insoluble fraction, indicating an essentially complete solubilization of murein.Muramidase-digested murein samples (200 μg) were analyzed by HPLC as described in reference 19. The muropeptide pattern (Fig. ​(Fig.1)1) was relatively simple, with five dominating components (DR5 and DR10 to DR13 [Fig. 1]). The muropeptides resolved by HPLC were collected, desalted, and subjected to PDMS. The results are presented in Table ​Table11 compared with the m/z values calculated for best-matching muropeptides made up of N-acetylglucosamine (GlucNAc), N-acetylmuramic acid (MurNAc), and the amino acids detected in the murein. The more likely structures are shown in Fig. ​Fig.1.1. According to the m/z values, muropeptides DR1 to DR7 and DR9 were monomers; DR8, DR10, and DR11 were dimers; and DR12 and DR13 were trimers. The best-fitting structures for DR3 to DR8, DR11, and DR13 coincided with muropeptides previously characterized in T. thermophilus HB8 (19) and had identical retention times in comparative HPLC runs. The minor muropeptide DR7 (Fig. ​(Fig.1)1) was the only one detected with a d-Ala–d-Ala dipeptide and most likely represents the basic monomeric subunit. The composition of the major cross-linked species DR11 and DR13 confirmed that cross-linking is mediated by (Gly)₂ bridges, as proposed previously (20). Open in a separate windowFIG. 1HPLC muropeptide elution patterns of murein purified from D. radiodurans. Muramidase-digested murein samples were subjected to HPLC analysis, and the A₂₀₄ of the eluate was recorded. The most likely structures for each muroeptide as deduced by PDMS are shown. The position of residues in brackets is the most likely one as deduced from the structures of other muropeptides but could not be formally demonstrated. R = GlucNac–MurNac–l-Ala–d-Glu-(γ)→.

TABLE 1

Calculated and measured m/z values for the molecular ions of the major muropeptides from D. radiodurans

Multi-element fingerprinting and high throughput sequencing identify multiple elements that affect fungal communities in Quercus macrocarpa foliage

Diverse fungal mutualists, pathogens and saprobes colonize plant leaves. These fungi face a complex environment, in which stochastic dispersal interplays with abiotic and biotic filters. However, identification of the specific factors that drive the community assembly seems unattainable. We mined two broad data sets and identified chemical elements, to which dominant molecular operational taxonomic units (OTUs) in the foliage of a native tree respond most extremely. While many associations could be identified, potential complicating issues emerged. Those were related to unevenly distributed OTU frequency data, a large number of potentially explanatory variables and the disproportionate effects of outlier observations.Key words: community assembly, environmental filter, fungi, heavy metal enrichment, nutrient enrichment, oak, Quercus macrocarpaHyperdiverse fungal communities inhabit the foliage of most plants^1,2 and these fungal communities have been reported for virtually every plant that has been examined.³ Baas-Becking hypothesis states that environment selects microbial communities from the abundant and possibly globally distributed propagule pools.⁴ Although the foliage-associated communities—like other microbial communities—are suspected to be sensitive to environmental drivers, determination of the mechanisms that control the assembly of these foliar communities has remained difficult and elusive. Some of the proposed mechanisms include distance limitations to propagule dispersal,^5–7 volume limitations to propagule loads,⁷ or limitations set by the environmental conditions either on the scale of the site of fungal colonization⁸ or more broadly on a landscape level.^6,9 The forces that may control the fungal community assembly are overlaid by additional biotic controls that include compatibilities between the fungi and host species^10,11 or genotypes^6,12 and the competitive or facilitative interactions among the component fungal genotypes.^6,10–13 Although a variety of potential controls for the foliage-associated fungal communities have been speculated, very little consensus exists on the relative importance of the different drivers. For example, while macronutrient and heavy metal enrichment may have an influence on the composition fungal communities¹⁴ and populations,¹⁵ relative importance of various chemical elements in the foliage remains yet to be investigated.To evaluate the use of multi-element fingerprinting data produced by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in combination with high throughput 454-pyrosequencing for determining influential chemical elements in structuring of the leaf-associated fungal communities, we mined a recent dataset¹⁶ that explored the effects of urbanization on the diversity and composition of the fungal communities associated with a native tree Quercus macrocarpa. From a total list of more than 700 non-singleton fungal OTUs, we selected fifty with highest overall frequency to provide an observationrich dataset for elemental effect assessment; these OTUs accounted for 84.5% of all sequences. Even so, many of these OTUs had a number of zero frequencies (Fig. 1), highlighting one of the difficulties in the use of environmental sequencing data. We omitted one OTU (OTU630 with a likely affinity to Trimmatostroma cordae [Mycosphaerellaceae]) that was strongly affected by the original land use design (urbanization; Wilcoxon rank sum test with a Bonferroni adjustment) and therefore unlikely to be representative for the present analyses of elemental drivers. This OTU was replaced with one with the next highest frequency. The frequencies of these 50 OTUs were investigated in the context of concentrations for 29 elements after the omission of five (Ag, Au, C, δ¹³C, δ¹⁵N) in the final analyses because of their strong association with the land use or the difficulty of finding a biological relevance. Of the remaining elements three (Fe, Cr and Ni) had pairwise correlations exceeding 0.98 between the three pairings; others showed no similar high correlations. To allow comparable evaluation across the broad array of elements, all concentrations were standardized to have a mean equal to zero and a standard deviation equal to one.Open in a separate windowFigure 1Rank-ordered distribution of observed frequencies for those OTU s whose frequency had an extreme slope when associated with the concentrations of one or more chemical elements in the mixed effects model. The asterisk denotes one extreme frequency for OTU 313 with a value 0.8636. Numbers in parentheses indicate the number of observations with a frequency equal to zero. The OTU s were assigned to approximate taxa using BLAST:²⁰ 425: Alternaria alternata (Pleosporaceae); 46: Phoma glomerata (Pleosporaceae); 686: Aureobasidium pullulans (Dothioraceae); 520: Davidiella tassiana (Davidiellaceae); 567: Cladosporioum tenuissimum (Davidiellaceae); 313 Oidium heveae (mitosporic Erysiphaceae); 586: Erysiphe hypogena (Erysiphaceae); 671: Mycosphaerella microsora (Mycosphaerellaceae); 555: Pestalotiopsis sp. (Amphisphaeriaceae); 607: Pleiochaeta setosa (incertae sedis).To rank elements according to their magnitude of association with the abundance of each OTU, a total of 1,450 models (50 OTUs times 29 elements) relating element concentration to OTU abundance were fit to the data. For each model, OTU frequency was the dependent variable, element concentration and time (a factor with three levels) were fixed effects, and—to account for the spatial arrangement of the experimental units—random effects associated with tree nested within site were included in the error structure. Time by element interactions were also investigated and tested using a likelihood ratio test. These mixed effect models were fit using R and the package lme4 (www.rproject.org).Statistical “tests of significance” that produce p-values can be sensitive to assumptions or outliers. Because of this and the fact that our analyses evaluated a total of 1,450 models, p-values themselves were not considered a reliable measure of importance when associating elements with OTU frequency. Instead, we emphasized metrics that highlight extraordinary findings rather than rely on tests of statistical significance. This approach facilitates finding few elements that have the strongest effect on OTU frequency. Note that the use of standardized element concentrations (above) provided slope coefficients that are comparable across all models. “Extreme slopes”, i.e., models where the OTU response to element concentration was strongest, were identified as those with estimated slope coefficients in the lower or upper 2.5 percentile, i.e., those farther than 1.8 standard deviations from the mean across all estimated slopes (Fig. 2). Using this approach, we identified a total of 69 models with extreme slopes (Open in a separate windowFigure 2Distribution of estimated slopes (i.e., the slope for element concentration) for a model relating OTU frequency to element concentration, time and a concentration by time interaction, including a tree-nested-within-site random effect. The mean across all 1,450 OTU s is approximately zero; the two vertical lines identify upper and lower 2.5 percentiles, beyond which the slopes were considered extreme (large black symbols). The horizontal line identifies the cut off maximum leverage (0.24), above which the slopes were considered to have observations with high leverage. Models with observations with a high leverage were tested for extreme slopes by refitting without those observations. Models are ranked from bottom to top in order of increasing leverage and the element for which the high-leverage observations and extreme slopes were recorded are identified on the right y-axis.

Table 1

Slopes identified as extreme in our analyses

Endoplasmic Reticulum Targeting and Insertion of Tail-Anchored Membrane Proteins by the GET Pathway

Hundreds of eukaryotic membrane proteins are anchored to membranes by a single transmembrane domain at their carboxyl terminus. Many of these tail-anchored (TA) proteins are posttranslationally targeted to the endoplasmic reticulum (ER) membrane for insertion by the guided-entry of TA protein insertion (GET) pathway. In recent years, most of the components of this conserved pathway have been biochemically and structurally characterized. Get3 is the pathway-targeting factor that uses nucleotide-linked conformational changes to mediate the delivery of TA proteins between the GET pretargeting machinery in the cytosol and the transmembrane pathway components in the ER. Here we focus on the mechanism of the yeast GET pathway and make a speculative analogy between its membrane insertion step and the ATPase-driven cycle of ABC transporters.The mechanism of membrane protein insertion into the endoplasmic reticulum (ER) has been extensively studied for many years (Shao and Hegde 2011). From this work, the signal recognition particle (SRP)/Sec61 pathway has emerged as a textbook example of a cotranslational membrane insertion mechanism (Grudnik et al. 2009). The SRP binds a hydrophobic segment (either a cleavable amino-terminal signal sequence or a transmembrane domain) immediately after it emerges from the ribosomal exit tunnel. This results in a translational pause that persists until SRP engages its receptor in the ER and delivers the ribosome-nascent chain complex to the Sec61 channel. Last, the Sec61 channel enables protein translocation into the ER lumen along with partitioning of hydrophobic transmembrane domains into the lipid bilayer through the Sec61 lateral gate (Rapoport 2007).Approximately 5% of all eukaryotic membrane proteins have an ER targeting signal in a single carboxy-terminal transmembrane domain that emerges from the ribosome exit tunnel following completion of protein synthesis and is not recognized by the SRP (Stefanovic and Hegde 2007). Nonetheless, because hydrophobic peptides in the cytoplasm are prone to aggregation and subject to degradation by quality control systems (Hessa et al. 2011), these tail-anchored (TA) proteins still have to be specifically recognized, shielded from the aqueous environment, and guided to the ER membrane for insertion. In the past five years, the guided-entry of TA proteins (GET) pathway has come to prominence as the major machinery for performing these tasks and the enabler of many key cellular processes mediated by TA proteins including vesicle fusion, membrane protein insertion, and apoptosis. This research has rapidly yielded biochemical and structural insights (​and2)2) into many of the GET pathway components (Hegde and Keenan 2011; Chartron et al. 2012a; Denic 2012). In particular, Get3 is an ATPase that uses metabolic energy to bridge recognition of TA proteins by upstream pathway components with TA protein recruitment to the ER for membrane insertion. However, the precise mechanisms of nucleotide-dependent TA protein binding to Get3 and how the GET pathway inserts tail anchors into the membrane are still poorly understood. Here, we provide an overview of the budding yeast GET pathway with emphasis on mechanistic insights that have come from structural studies of its membrane-associated steps and make a speculative juxtaposition with the ABC transporter mechanism.

Table 1.

A catalog of GET pathway component structures

Cognitive Manic Symptoms in Bipolar Disorder Associated with Polymorphisms in the DAOA and COMT Genes

Introduction

Bipolar disorder is characterized by severe mood symptoms including major depressive and manic episodes. During manic episodes, many patients show cognitive dysfunction. Dopamine and glutamate are important for cognitive processing, thus the COMT and DAOA genes that modulate the expression of these neurotransmitters are of interest for studies of cognitive function.

Methodology

Focusing on the most severe episode of mania, a factor was found with the combined symptoms of talkativeness, distractibility, and thought disorder, considered a cognitive manic symptoms (CMS) factor. 488 patients were genotyped, out of which 373 (76%) had talkativeness, 269 (55%) distractibility, and 372 (76%) thought disorder. 215 (44%) patients were positive for all three symptoms, thus showing CMS (Bipolar disorder type 1 [n]488Men [n (%)]209 (43)Talkativeness [n (%)]373 (76)Distracibility [n (%)]269 (55)Thought disorder [n (%)]372 (76)Cognitive manic symptoms^* [n (%)]215 (44)Men [n (%)]81 (39)Non-Cognitive manic symptoms [n (%)]248 (51)Men [n (%)]117 (56)Unknown [n (%)]25 (5)Men [n (%)]11 (44)Anonymous blood donors (ABD)1044Men [n (%)]616 (59)Open in a separate window^*having all three symptoms: talkativeness, distractibility, and tought disorder.

Results

The finding of this study was that cognitive manic symptoms in patients with bipolar 1 disorder was associated with genetic variants in the DAOA and COMT genes. Nominal association for DAOA SNPs and COMT SNPs to cognitive symptoms factor in bipolar 1 disorder was found in both allelic (BP1 CMSBP1 non-CMSABDBP1 CMS vs. non-CMS^b BP1 CMS vs. ABD controls^b GeneSNP^a aa/ab/bbaa/ab/bbaa/ab/bbpEMP1^c EMP2^d OR [95% CI] ^e pEMP1^c EMP2^d OR [95% CI] ^e DAOA rs3916967 (C/T)32/88/8950/118/77177/494/3610.0180.0180.210.72 [0.55–0.93]0.0290.0260.280.78 [0.66–1.0] DAOA rs2391191 (A/C)28/75/7939/111/70179/487/3570.0550.0390.500.75 [0.57–1.0]0.0200.0190.210.75 [0.63–1.0] DAOA rs1935062 (C/A)26/67/8935/102/86146/460/4050.120.120.780.80 [0.58–1.0]0.0690.0660.520.80 [0.65–1.0] COMT rs5993883 (T/G)33/120/5371/112/57269/510/2230.0250.0300.270.73 [0.56–0.95]0.0017^* 1.0E^{−4 *} 0.021^* 0.68 [0.91–1.4] COMT rs165599 (G/A)29/94/8725/93/12687/443/5010.0930.0940.691.27 [1.0–1.8]0.0140.0170.161.34 [1.1–1.7]Open in a separate window^aSNP (minor allele(a)/major allele(b)).^bgender and rs1718119 as covariate.^cpoint-wise p-value from 10,000 pemutations with no covarite (EMP1).^dcorrected empirical p-value by max (T) permutation.^eodds ratio (OR), the proportion of minor versus major allele affected (cognitive manic symptoms factor)/proportion of minor versus major allele unaffected (non-cognitive manic symptoms factor or ABD controls).^*significant after correction for multiple testing by max (T) permutation.

Table 3

Haplotype association of haplotype group 1 in bipolar 1 patients with cognitive manic symptoms (CMS) compared with non-CMS patients or ABD controls in the DAOA gene.

Temporal and Spatial Diversity of the Tap Water Microbiota in a Norwegian Hospital

We analyzed the temporal and spatial diversity of the microbiota in a low-usage and a high-usage hospital tap. We identified a tap-specific colonization pattern, with potential human pathogens being overrepresented in the low-usage tap. We propose that founder effects and local adaptation caused the tap-specific colonization patterns. Our conclusion is that tap-specific colonization represents a potential challenge for water safety.Humans are exposed to and consume large amounts of tap water in their everyday life, with the tap water microbiota representing a potent reservoir for pathogens (8). Despite the potential impact, our knowledge about the ecological diversification processes of the tap water microbiota is limited (4, 11).The aim of the present work was to determine the temporal and spatial distribution patterns of the planktonic tap water microbiota. We compared the summer and winter microbiota from two hospital taps supplied from the same water source. We analyzed 16S rRNA gene clone libraries by using a novel alignment-independent approach for operational taxonomic unit (OTU) designation (6), while established OTU diversity and richness estimators were used for the ecological interpretations.Tap water samples (1 liter) from a high-usage kitchen and a low-usage toilet cold-water tap in Akershus University Hospital, Lørenskog, Norway, were collected in January and July 2006. The total DNA was isolated and the 16S rRNA gene PCR amplified and sequenced. Based on the sequences, we estimated the species richness and diversity, we calculated the distances between the communities, and trees were constructed to reflect the relatedness of the microbiota in the samples analyzed. Details about these analytical approaches are given in the materials and methods section in the supplemental material.Our initial analysis of species composition was done using the RDPII hierarchical classifier. We found that the majority of pathogen-related bacteria in our data set belonged to the class Gammaproteobacteria. The genera encompassed Legionella, Pseudomonas, and Vibrio (Table ​(Table1).1). We found a significant overrepresentation of pathogen-related bacteria in the toilet tap (P = 0.04), while there were no significant differences between summer and winter samples. Legionella showed the highest relative abundance for the pathogen-related bacteria. With respect to the total diversity, we found that Proteobacteria dominated the tap water microbiota (representing 86% of the taxa) (see Table S1 in the supplemental material). There was, however, a large portion (56%) of the taxa that could not be assigned to the genus level using this classifier.

TABLE 1.

Cloned sequences related to human pathogens^a

α-and β-Glycosidases in maize roots

Four glycosidases were analyzed in 10 mm apical segments prepared from growing roots (15 mm) of Zea mays L. The pH optima were found to be 5.8 for -glucosidase, 4.4 for -galactosidase, 6.4 for -glucosidase and 6.0 for -galactosidase. The -glucosidase showed 4-fold higher activity than the -galactosidase. The distribution of the -glucosidase activity was signifcantly different from that of the -galactosidase, -glucosidase and -galactosidase.Abbreviations -Glu -glucosidase - -Gal -galactosidase - -Glu -glucosidase - -Gal -galactosidase