Individual Gene Products Research Articles

Functional analysis of the Helicobacter pullorum N-linked protein glycosylation system.

N-linked protein glycosylation systems operate in species from all three domains of life. The model bacterial N-linked glycosylation system from Campylobacter jejuni is encoded by pgl genes present at a single chromosomal locus. This gene cluster includes the pglB oligosaccharyltransferase responsible for transfer of glycan from lipid carrier to protein. Although all genomes from species of the Campylobacter genus contain a pgl locus, among the related Helicobacter genus only three evolutionarily related species (H. pullorum, H. canadensis and H. winghamensis) potentially encode N-linked protein glycosylation systems. Helicobacter putative pgl genes are scattered in five chromosomal loci and include two putative oligosaccharyltransferase-encoding pglB genes per genome. We have previously demonstrated the in vitro N-linked glycosylation activity of H. pullorum resulting in transfer of a pentasaccharide to a peptide at asparagine within the sequon (D/E)XNXS/T. In this study, we identified the first H. pullorum N-linked glycoprotein, termed HgpA. Production of histidine-tagged HgpA in the background of insertional knockout mutants of H. pullorum pgl/wbp genes followed by analysis of HgpA glycan structures demonstrated the role of individual gene products in the PglB1-dependent N-linked protein glycosylation pathway. Glycopeptide purification by zwitterionic-hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry identified six glycosites from five H. pullorum proteins, which was consistent with proteins reactive with a polyclonal antiserum generated against glycosylated HgpA. This study demonstrates functioning of a H. pullorum N-linked general protein glycosylation system.

Delivery of gene targeting siRNAs to breast cancer cells using a multifunctional peptide complex that promotes both targeted delivery and endosomal release.

RNA interference has been used to dissect the importance of individual gene products in various human disease processes, including cancer. Small-interfering RNA, or siRNA, is one of the tools utilized in this regard, but specially-designed delivery agents are required to allow the siRNA to gain optimal access to the cell interior. Our laboratory has utilized two different siRNA-binding delivery peptides containing a polyarginine core, and modified by myristoylation and targeting motifs (iRGD or Lyp-1). A third peptide was designed to assist with endosomal release. Various ratios of the peptides and siRNA were combined and assayed for the ability to form stable complexes, and optimized ratios were determined. The complexes were found to form particles, with the majority having a diameter of 100–300 nm, as visualized by electron microscopy. These siRNA complexes have enhanced protection from nucleases present in serum, as compared to “naked” unprotected siRNA. The particles were internalized by the cells and could be detected in the cell cytoplasm by confocal fluorescence microscopy. In functional assays, peptide/siRNA complexes were shown to cause the knock down of corresponding targeted proteins. The peptide with the LyP-1 targeting motif was more effective at knockdown in MDA-MB-231 breast cancer cells than the peptide with the iRGD motif. Inclusion of the endosomal release peptide in the complexes greatly enhanced the peptide/siRNA effects. Peptide/siRNA complexes simultaneously targeting Stat3 and c-Myc caused a marked reduction in anchorage-independent growth, a property correlated with tumorigenicity. This study demonstrates the ability of a peptide-based siRNA-delivery system to deliver siRNA into breast cancer cells and cause both protein knockdown and suppression of the malignant phenotype. Such peptide complexes are likely to become highly useful siRNA-delivery vehicles for the characterization, and potentially for the treatment, of human cancer.

Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data.

We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.

I've got algorithm: predicting tumor and autoimmune peptide targets for CD8+ T cells.

CD8+ T cells play a central role in eradicating intracellular pathogens, but also are important for noninfectious diseases, including cancer and autoimmunity. The ability to clinically manipulate CD8+ T cells to target cancer and autoimmune disease is limited by our ignorance of relevant self-peptide target antigens. In this issue of the JCI, Pearson et al. describe 25,270 MHC class I-associated peptides presented by a wide range of HLA A and B allomorphs expressed by 18 different B cell lines. Via extensive bioinformatic analysis, the authors make surprising conclusions regarding the selective nature of peptide generation at the level of individual gene products and create a predictive algorithm for disease-relevant self-peptides that will be of immediate use for clinical and basic immunological research.

Highlighting the Need for Systems-Level Experimental Characterization of Plant Metabolic Enzymes.

The biology of living organisms is determined by the action and interaction of a large number of individual gene products, each with specific functions. Discovering and annotating the function of gene products is key to our understanding of these organisms. Controlled experiments and bioinformatic predictions both contribute to functional gene annotation. For most species it is difficult to gain an overview of what portion of gene annotations are based on experiments and what portion represent predictions. Here, I survey the current state of experimental knowledge of enzymes and metabolism in Arabidopsis thaliana as well as eleven economically important crops and forestry trees – with a particular focus on reactions involving organic acids in central metabolism. I illustrate the limited availability of experimental data for functional annotation of enzymes in most of these species. Many enzymes involved in metabolism of citrate, malate, fumarate, lactate, and glycolate in crops and forestry trees have not been characterized. Furthermore, enzymes involved in key biosynthetic pathways which shape important traits in crops and forestry trees have not been characterized. I argue for the development of novel high-throughput platforms with which limited functional characterization of gene products can be performed quickly and relatively cheaply. I refer to this approach as systems-level experimental characterization. The data collected from such platforms would form a layer intermediate between bioinformatic gene function predictions and in-depth experimental studies of these functions. Such a data layer would greatly aid in the pursuit of understanding a multiplicity of biological processes in living organisms.

Functional Dissection of an Alternatively Spliced Herpesvirus Gene by Splice Site Mutagenesis.

Herpesviruses have large and complex DNA genomes. The largest among the herpesviruses, those of the cytomegaloviruses, include over 170 genes. Although most herpesvirus gene products are expressed from unspliced transcripts, a substantial number of viral transcripts are spliced. Some viral transcripts are subject to alternative splicing, which leads to the expression of several proteins from a single gene. Functional analysis of individual proteins derived from an alternatively spliced gene is difficult, as deletion and nonsense mutagenesis, both common methods used in the generation of viral gene knockout mutants, affect several or all gene products at the same time. Here, we show that individual gene products of an alternatively spliced herpesvirus gene can be inactivated selectively by mutagenesis of the splice donor or acceptor site and by intron deletion or substitution mutagenesis. We used this strategy to dissect the essential M112/113 gene of murine cytomegalovirus (MCMV), which encodes the MCMV Early 1 (E1) proteins. The expression of each of the four E1 protein isoforms was inactivated individually, and the requirement for each isoform in MCMV replication was analyzed in fibroblasts, endothelial cells, and macrophages. We show that the E1 p87 isoform, but not the p33, p36, and p38 isoforms, is essential for viral replication in cell culture. Moreover, the presence of one of the two medium-size isoforms (p36 or p38) and the presence of intron 1, but not its specific sequence, are required for viral replication. This study demonstrates the usefulness of splice site mutagenesis for the functional analysis of alternatively spliced herpesvirus genes. Herpesviruses include up to 170 genes in their DNA genomes. The functions of most viral gene products remain poorly defined. The construction of viral gene knockout mutants has thus been an important tool for functional analysis of viral proteins. However, this strategy is of limited use when viral gene transcripts are alternatively spliced, leading to the expression of several proteins from a single gene. In this study, we showed, as a proof of principle, that each protein product of an alternatively spliced gene can be eliminated individually by splice site mutagenesis. Mutant viruses lacking individual protein products displayed different phenotypes, demonstrating that the products of alternatively spliced genes have nonredundant functions.

Whole Genome Expression Microarray Analysis of Highly Versus Poorly Tumorigenic Murine Melanoma Cell Lines Provides Insights into Factors That Regulate Tumor Growth, Metastasis, and Immunogenicity.

Melanoma, a cancer derived from pigment-producing melanocytes of the skin, is a disease of major public health significance. Although it is the least common form of skin cancer, melanoma is by far the most lethal due to its propensity to metastasize to a number of vital organs, including the brain, lungs, liver, and other visceral organs (1). According to the American Cancer Society’s 2015 Cancer Facts & Figures statistics, melanoma accounts for <2% of all skin cancer cases but is responsible for nearly 75% of skin cancer-related deaths, and it is predicted that melanoma will cause 9,940 deaths in the U.S. this year (2). Worldwide estimates of mortality for melanoma are that this disease is responsible for over 65,000 deaths annually (3). Moreover, recent data collected by the Surveillance, Epidemiology, and End Results (SEER) Program show that melanoma incidence rates have continued to increase over the previous 40 years (4). In the U.S. alone, the current annual costs for treatment and productivity losses associated with melanoma are near $3.3 billion (5). These numbers are even more staggering when considering a recent study by the International Agency for Research on Cancer, which ranked North America only fourth in melanoma incidence worldwide (6), thus highlighting the need to address melanoma as a global public health concern. With these statistics in mind, it is vital that we increase our understanding of the progression of melanoma so that we may improve current – and develop new – therapies for the treatment of this cancer. The B16 murine melanoma cell line is one of the most widely used models for studying the progression and treatment of melanoma in pre-clinical settings (7, 8). Characterized by its rapid outgrowth as both primary tumors and metastatic lesions, the B16-F1 subline has also been shown to elicit dysfunctional CD8+ T cell responses (9) that mimic those frequently observed in many melanoma patients (10–12), and soluble factors derived from this tumor have been shown to alter the function of dendritic cells (DC) as well (13–15). We have recently described a chemically mutated variant of B16 melanoma, D5.1G4, that is significantly less tumorigenic and immunosuppressive than its wild-type counterpart (13, 15), though the basis for these differences between B16-F1 and D5.1G4 are poorly understood. Therefore, we sought to gain insights into factors that regulate melanoma tumorigenicity by comparing whole genome expression profiles of these murine melanomas. The microarray dataset obtained from these experiments and described herein offers a great resource for investigators wishing to study melanoma growth and progression and will likely drive the design of future experiments aimed at understanding the role of both individual gene products and entire pathways in melanoma tumorigenesis.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association.Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

New cofactor supports α,β-unsaturated acid decarboxylation via 1,3-dipolar cycloaddition.

The ubiD/ubiX or the homologous fdc/pad genes have been implicated in the non-oxidative reversible decarboxylation of aromatic substrates, and play a pivotal role in bacterial ubiquinone biosynthesis1–3 or microbial biodegradation of aromatic compounds4–6 respectively. Despite biochemical studies on individual gene products, the composition and co-factor requirement of the enzyme responsible for in vivo decarboxylase activity remained unclear7–9. We show Fdc is solely responsible for (de)carboxylase activity, and that it requires a new type of cofactor: a prenylated flavin synthesised by the associated UbiX/Pad10. Atomic resolution crystal structures reveal two distinct isomers of the oxidized cofactor can be observed: an isoalloxazine N5-iminium adduct and a N5 secondary ketimine species with drastically altered ring structure, both having azomethine ylide character. Substrate binding positions the dipolarophile enoic acid group directly above the azomethine ylide group. The structure of a covalent inhibitor-cofactor adduct suggests 1,3-dipolar cycloaddition chemistry supports reversible decarboxylation in these enzymes. While 1,3-dipolar cycloaddition is commonly used in organic chemistry11–12, we propose this presents the first example of an enzymatic 1,3-dipolar cycloaddition reaction. Our model for Fdc/UbiD catalysis offers new routes in alkene hydrocarbon production or aryl (de)carboxylation.

The fungal secondary metabolite alternariol and Alternaria induced immunity (MPF6P.659)

Abstract Sensitivity to the airborne fungus Alternaria alternata (common mold) is believed to be a common cause of allergic asthma. Epidemiological studies worldwide indicate that Alternaria sensitivity is closely linked with the development and exacerbation of allergic airway disorders such as asthma, allergic rhinitis, and chronic rhinosinusitis. Therapies are limited by the lack of knowledge about the role of individual fungal gene products in airway responses. The A. alternata mycotoxin alternariol is a genotoxic and cytotoxic molecule and a major contaminant of most food, grains and feed products. We hereby present a study where we explored the hypothesis of alternariol having immune-modulatory properties. It led us to the discovery that alternariol is strongly anti-inflammatory. We have investigated alternariol’s dynamic interplay between innate immunity and human bronchoalveolar epithelial cells. Immune assays have provided the key doses needed to suppress LPS-induced inflammation. We have also profiled the response of mutant fungal spores that no longer produce alternariol with normal wildtype spores on lung epithelium. This model has also been used to investigate alternariol’s mechanism of action in relation to aryl hydrocarbon receptor. Greater understanding of the role of alternariol in allergic asthma may lead to improved treatment strategies as well as a potential drug target for inflammatory syndromes.

Characterization of the major light-harvesting complexes (LHCBM) of the green alga Chlamydomonas reinhardtii.

Nine genes (LHCBM1-9) encode the major light-harvesting system of Chlamydomonas reinhardtii. Transcriptomic and proteomic analyses have shown that those genes are all expressed albeit in different amounts and some of them only in certain conditions. However, little is known about the properties and specific functions of the individual gene products because they have never been isolated. Here we have purified several complexes from native membranes and/or we have reconstituted them in vitro with pigments extracted from C. reinhardtii. It is shown that LHCBM1 and -M2/7 represent more than half of the LHCBM population in the membrane. LHCBM2/7 forms homotrimers while LHCBM1 seems to be present in heterotrimers. Trimers containing only type I LHCBM (M3/4/6/8/9) were also observed. Despite their different roles, all complexes have very similar properties in terms of pigment content, organization, stability, absorption, fluorescence and excited-state lifetimes. Thus the involvement of LHCBM1 in non-photochemical quenching is suggested to be due to specific interactions with other components of the membrane and not to the inherent quenching properties of the complex. Similarly, the overexpression of LHCBM9 during sulfur deprivation can be explained by its low sulfur content as compared with the other LHCBMs. Considering the highly conserved biochemical and spectroscopic properties, the major difference between the complexes may be in their capacity to interact with other components of the thylakoid membrane.

American Heart Association Cardiovascular Genome-Phenome Study: foundational basis and program.

The American Heart Association has established the Cardiovascular Genome-Phenome Study (CV-GPS) as a means by which to achieve the goal of using modern genomics and phenotyping optimally to combat cardiovascular disease (CVD). CVD remains the leading cause of death in the United States and has become a major cause of morbidity and mortality worldwide. The course of CVD begins at an early age and evolves throughout life. Typically, risk factor precursors (eg, prehypertension, borderline abnormal lipids) are first observed in adolescence or young adulthood. By middle age, they develop into full-fledged cardiometabolic risk factors and subclinical disease, which usually become clinically apparent in older age. Importantly, risk factors for CVD reflect the complex interplay between genetic and environmental factors and demonstrate the complexity of the multiple determinants of disease. Editorial see p 17 Many of the genetic and environmental determinants of CVD have been identified over the past 50 years, and successful preventive and therapeutic strategies have been developed as a result. However, many potential genetic or acquired disease drivers remain unaccounted for, as illustrated by the observation that the major CVD risk factors combined account for only a fraction of the population-attributable risk.1 In addition, variations in CVD risk exist among individuals with similar risk profiles. Part of these differences may reflect differences in profiles of known risk factors that have not yet been fully refined but also individual differences in underlying genetic modifiers of such risk factors. Modern genomics, with its ability to provide ideally unbiased analysis of the entire genome, offers an approach to the ascertainment of all genetic determinants of CVD. Furthermore, the increasingly broad range of “omics” methods, including RNA-Seq, modern proteomics, metabolomics, and metabonomics, provide deeper and more refined molecular detail by which to define an individual’s genome and its relationship to (patho)phenotype. …

TFClass: a classification of human transcription factors and their rodent orthologs.

TFClass aims at classifying eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs). For this, a classification schema comprising four generic levels (superclass, class, family and subfamily) was defined that could accommodate all known DNA-binding human TFs. They were assigned to their (sub-)families as instances at two different levels, the corresponding TF genes and individual gene products (protein isoforms). In the present version, all mouse and rat orthologs have been linked to the human TFs, and the mouse orthologs have been arranged in an independent ontology. Many TFs were assigned with typical DNA-binding patterns and positional weight matrices derived from high-throughput in-vitro binding studies. Predicted TF binding sites from human gene upstream sequences are now also attached to each human TF whenever a PWM was available for this factor or one of his paralogs. TFClass is freely available at http://tfclass.bioinf.med.uni-goettingen.de/ through a web interface and for download in OBO format.

Transcriptome-based analysis of kidney gene expression changes associated with diabetes in OVE26 mice, in the presence and absence of losartan treatment.

Diabetes is among the most common causes of end-stage renal disease, although its pathophysiology is incompletely understood. We performed next-generation sequencing-based transcriptome analysis of renal gene expression changes in the OVE26 murine model of diabetes (age 15 weeks), relative to non-diabetic control, in the presence and absence of short-term (seven-day) treatment with the angiotensin receptor blocker, losartan (n = 3–6 biological replicates per condition). We detected 1438 statistically significant changes in gene expression across conditions. Of the 638 genes dysregulated in diabetes relative to the non-diabetic state, >70% were downregulation events. Unbiased functional annotation of genes up- and down-regulated by diabetes strongly associated (p<1×10−8) with terms for oxidative stress and for endoplasmic reticulum stress/protein folding. Most of the individual gene products up- or down-regulated with diabetes were unaffected by losartan treatment; however, of the gene products dysregulated in diabetes and influenced by losartan treatment, the vast majority of changes were in the direction of amelioration rather than exacerbation of the diabetic dysregulation. This group of losartan-protected genes associated strongly with annotation terms for endoplasmic reticulum stress, heat shock proteins, and chaperone function, but not oxidative stress; therefore, the losartan-unaffected genes suggest avenues for additional therapeutic opportunity in diabetes. Interestingly, the gene product most highly upregulated by diabetes (>52-fold), encoded by the cationic amino acid transporter Slc7a12, and the gene product most highly downregulated by diabetes (>99%) – encoded by the “pseudogene” Gm6300 – are adjacent in the murine genome, are members of the SLC7 gene family, and are likely paralogous. Therefore, diabetes activates a near-total genetic switch between these two paralogs. Other individual-level changes in gene expression are potentially relevant to diabetic pathophysiology, and novel pathways are suggested. Genes unaffected by diabetes alone but exhibiting increased renal expression with losartan produced a signature consistent with malignant potential.

Heterologous Expression of Thuricin CD Immunity Genes in Listeria monocytogenes

Bacteriocins are ribosomally synthesized peptides that can have a narrow or broad spectrum of antimicrobial activity. Bacteriocin producers typically possess dedicated immunity systems that often consist of an ATP-binding cassette (ABC) transporter system and/or a dedicated immunity protein. Here we investigated the genes responsible for immunity to thuricin CD, a narrow-spectrum two-peptide sactibiotic produced by Bacillus thuringiensis DPC6431. Heterologous expression of putative thuricin CD immunity determinants allowed us to identify and investigate the relative importance of the individual genes and gene products that contribute to thuricin CD immunity. We established that TrnF and TrnG are the individual components of an ABC transporter system that provides immunity to thuricin CD. We also identified a hitherto overlooked open reading frame located upstream of trnF predicted to encode a 79-amino-acid transmembrane protein. We designated this newly discovered gene trnI and established that TrnI alone can provide protection against thuricin CD.

Antibodies against CKI1RD, a receiver domain of the sensor histidine kinase in Arabidopsis thaliana: From antigen preparation to in planta immunolocalization

Immunodetection is a powerful tool in functional studies of all organisms. In plants, the gene redundancy and presence of gene families composed of highly homologous members often impedes the unambiguous identification of individual gene products. A family of eight sensor histidine kinases (HKs) mediates the transduction of diverse signals into Arabidopsis thaliana cells, thereby ensuring the initiation of appropriate adaptive responses. Antibodies recognizing specific members of the HK family would be valuable for studying their functions in Arabidopsis and other plant species including important crops. We have focused on developing and applying antibodies against CYTOKININ-INDEPENDENT 1 (CKI1), which encodes a constitutively active membrane-bound sensor HK that regulates the development of female gametophytes and vascular tissue in Arabidopsis. A coding sequence delimiting the C-terminal receiver domain of CKI1 (CKI1RD) was expressed in Escherichia coli using the IPTG-inducible expression system and purified to give a highly pure target protein. The purified CKI1RD protein was then used as an antigen for anti-CKI1RD antibody production. The resulting polyclonal antibodies had a detection limit of 10ng of target protein at 1:20,000 dilution and were able to specifically distinguish CKI1, both in vitro and in situ, even in a direct comparison with highly homologous members of the same HK family AHK4, CKI2 and ETR1. Finally, anti-CKI1RD antibodies were able to selectively bind CKI1–GFP fusion protein in a pull-down assay using crude lysate from an Arabidopsis cell suspension culture. Our results suggest that the receiver domain is a useful target for the functional characterization of sensor HKs in immunological and biochemical studies.

Acute Versus Chronic Loss of Mammalian Azi1/Cep131 Results in Distinct Ciliary Phenotypes

Defects in cilium and centrosome function result in a spectrum of clinically-related disorders, known as ciliopathies. However, the complex molecular composition of these structures confounds functional dissection of what any individual gene product is doing under normal and disease conditions. As part of an siRNA screen for genes involved in mammalian ciliogenesis, we and others have identified the conserved centrosomal protein Azi1/Cep131 as required for cilia formation, supporting previous Danio rerio and Drosophila melanogaster mutant studies. Acute loss of Azi1 by knock-down in mouse fibroblasts leads to a robust reduction in ciliogenesis, which we rescue by expressing siRNA-resistant Azi1-GFP. Localisation studies show Azi1 localises to centriolar satellites, and traffics along microtubules becoming enriched around the basal body. Azi1 also localises to the transition zone, a structure important for regulating traffic into the ciliary compartment. To study the requirement of Azi1 during development and tissue homeostasis, Azi1 null mice were generated (Azi1Gt/Gt). Surprisingly, Azi1Gt/Gt MEFs have no discernible ciliary phenotype and moreover are resistant to Azi1 siRNA knock-down, demonstrating that a compensation mechanism exists to allow ciliogenesis to proceed despite the lack of Azi1. Cilia throughout Azi1 null mice are functionally normal, as embryonic patterning and adult homeostasis are grossly unaffected. However, in the highly specialised sperm flagella, the loss of Azi1 is not compensated, leading to striking microtubule-based trafficking defects in both the manchette and the flagella, resulting in male infertility. Our analysis of Azi1 knock-down (acute loss) versus gene deletion (chronic loss) suggests that Azi1 plays a conserved, but non-essential trafficking role in ciliogenesis. Importantly, our in vivo analysis reveals Azi1 mediates novel trafficking functions necessary for flagellogenesis. Our study highlights the importance of both acute removal of a protein, in addition to mouse knock-out studies, when functionally characterising candidates for human disease.

Predicting overlapping protein complexes in weighted interactome networks

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions. The increasing mount of protein-protein interaction (PPI) data has enabled us to predict protein complexes. In spite of the advances in these computational approaches and experimental techniques, it is impossible to construct an absolutely reliable PPI network. Taking into account the reliability of interactions in the PPI network, we have constructed a weighted protein-protein interaction (WPPI) network, in which the reliability of each interaction is represented as a weight using the topology of the PPI network. As overlaps are likely to have biological importance, we proposed a novel method named WN-PC (weighted network-based method for predicting protein complexes) to predict overlapping protein complexes on the WPPI network. The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes, and binds attachment proteins to candidate complexes. Finally, we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size. A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure, coverage rate, and P-value. We have applied WN-PC to two different yeast PPI data sets, one of which is a huge PPI network consisting of over 6000 proteins and 200 000 interactions. Experimental results show that WN-PC outperforms the state-of-the-art methods. We think that our research may be helpful for other applications in PPI networks.

H5N1 pathogenesis studies in mammalian models

H5N1 influenza viruses are capable of causing severe disease and death in humans, and represent a potential pandemic subtype should they acquire a transmissible phenotype. Due to the expanding host and geographic range of this virus subtype, there is an urgent need to better understand the contribution of both virus and host responses following H5N1 virus infection to prevent and control human disease. The use of mammalian models, notably the mouse and ferret, has enabled the detailed study of both complex virus–host interactions as well as the contribution of individual viral proteins and point mutations which influence virulence. In this review, we describe the behavior of H5N1 viruses which exhibit high and low virulence in numerous mammalian species, and highlight the contribution of inoculation route to virus pathogenicity. The involvement of host responses as studied in both inbred and outbred mammalian models is discussed. The roles of individual viral gene products and molecular determinants which modulate the severity of H5N1 disease in vivo are presented. This research contributes not only to our understanding of influenza virus pathogenesis, but also identifies novel preventative and therapeutic targets to mitigate the disease burden caused by avian influenza viruses.

TFClass: an expandable hierarchical classification of human transcription factors.

TFClass (http://tfclass.bioinf.med.uni-goettingen.de/) provides a comprehensive classification of human transcription factors based on their DNA-binding domains. Transcription factors constitute a large functional family of proteins directly regulating the activity of genes. Most of them are sequence-specific DNA-binding proteins, thus reading out the information encoded in cis-regulatory DNA elements of promoters, enhancers and other regulatory regions of a genome. TFClass is a database that classifies human transcription factors by a six-level classification schema, four of which are abstractions according to different criteria, while the fifth level represents TF genes and the sixth individual gene products. Altogether, nine superclasses have been identified, comprising 40 classes and 111 families. Counted by genes, 1558 human TFs have been classified so far or >2900 different TFs when including their isoforms generated by alternative splicing or protein processing events. With this classification, we hope to provide a basis for deciphering protein–DNA recognition codes; moreover, it can be used for constructing expanded transcriptional networks by inferring additional TF-target gene relations.