How a first research experience had an impact on my scientific journey

As I look back to my scientific trajectory on the occasion of being the recipient of the E. B. Wilson Medal of the American Society for Cell Biology, I realize how much an early scientific experience had an impact on my research many years later. The major influence that the first scientific encounters can have in defining a scientist’s path makes the choice of the training environment so important for a future career.

During my scientific career I have worked on different topics. However, my scientific journey represents a progression of steps that build onto each other. Each experience, no matter how successful, had an impact on how I interpreted subsequent results, on how I assessed their implications, and on how I made subsequent programmatic decisions. My research at the interface of cell biology and neuroscience has spanned from membrane traffic and membrane remodeling, to phosphoinositide signaling, and more recently also to the role of membrane contact sites in the control of membrane lipid homeostasis in physiology and disease. While only partially related, all these topics are interconnected by an intellectual thread. As an example—which shows how defining our training years can be—I will summarize here how an early scientific experience in medical school contributed to shape in unexpected ways my research directions many years later.From the day I enrolled in medical school at the University of Milano I knew I wanted to be a scientist. While a medical student, I explored several research environments that would expose me to leading-edge science. Eventually I encountered a young scientist, Jacopo Meldolesi, who had just returned to Italy after studying the secretory pathway as a postdoc with Jim Jamieson and George Palade at Rockefeller University, and I decided to join his lab to carry out the required short medical school thesis project. Jacopo was interested in the regulation of secretion and proposed that I explore mechanisms underlying the so-called “PI (Phosphatidyl Inositol) effect” described by Hokin and Hokin in the 1950s (Hokin and Hokin, 1953). These investigators had found that stimulation of secretory cells resulted in the rapid cleavage of PI immediately followed by its resynthesis. It remained unclear whether these changes reflected a signaling reaction elicited by secretagogues or metabolic changes associated with membrane traffic reactions triggered by the stimulus. So, the idea was to determine in which membrane compartment this turnover occurred: selectively at the plasma membrane (thus supporting a role in signaling) or in membranes of the secretory pathway. We were puzzled by finding that at least under the rather poor time resolution of our experiments—incubation of pancreatic lobules for tens of minutes with radioactive inositol following stimulation with acetylcholine—newly synthesized PI was found at similar concentrations in all membranes. But then we became aware of the recently described proteins that transport lipids between membranes through the cytosol and independent of membrane traffic (Wirtz and Zilversmit, 1969). We thought that perhaps these proteins could contribute to rapidly equilibrating PI pools in different compartments, questioning the validity of our experimental approach to identify the site of PI resynthesis. I moved on to other projects, and I thought I would never again work with lipids.In the following years the work of several labs established that the PI effect discovered by Hokin and Hokin mainly reflected agonist-stimulated phospholipase C (PLC)-dependent cleavage of the phosphoinositide PI(4,5)P₂ at the plasma membrane to generate the second messengers diacylglycerol (DAG) and IP3 (phosphoinositides are the phosphorylated products of PI) (Lapetina and Michell, 1973; Inoue et al., 1977; Berridge et al., 1983). These findings became textbook knowledge, and the idea that PI(4,5)P₂ had primarily a function in signal transduction at the cell surface became mainstream. The additional discovery of the PI(3,4)P₂ and PI(3,4,5)P₃ as a mediators of growth factor actions added a new twist to the field by showing that not only metabolites generated by PI(4,5)P₂ cleavage, but also phosphoinositides themselves, could have a signaling role (Traynor-Kaplan et al., 1988; Whitman et al., 1988; Auger et al., 1989). However, once again, this signaling appeared to be implicated in classical signal transduction, not in membrane traffic.In the meantime, I had moved to Yale, first as a postdoc with Paul Greengard and then as a faculty in the Section of Cell Biology (which subsequently became the Department of Cell Biology). Following my postdoc with Greengard, I had become interested in the cell biology of synapses and in the molecular machinery underlying the exo-endocytic recycling of synaptic vesicles. In what turned out to be a seminal finding, in the mid-1990s Peter McPherson, a postdoc in my laboratory, identified a nerve terminal–enriched protein that shared some interactions with the GTPase dynamin, another abundant presynaptic protein studied in our lab (McPherson et al., 1994). As dynamin mediates the fission reaction of endocytosis by cutting the neck of endocytic buds to generate free vesicles (Takei et al., 1995; Roux et al., 2006; Ferguson and De Camilli, 2012), we speculated that this new protein would also have a role in endocytic traffic. Peter set out to clone it, and I vividly recall my surprise when, upon my returning from a summer vacation in Italy he reported to me that a domain of this protein, which we called synaptojanin, had homology to an inositol 5-phosphatase that dephosphorylates PI(4,5)P₂ (McPherson et al., 1996). A second domain of this protein was subsequently found to have inositol 4-phosphatase activity (Guo et al., 1999), so that synaptojanin can dephosphorylate PI(4,5)P₂ all the way to PI. Thus, I was back to lipids, specifically to inositol phospholipids, and to a potential role of these lipids not in the transduction of extracellular signals, but in membrane traffic. With a mind primed by my project in medical school, I delved with enthusiasm into this topic. PI(4,5)P₂ had been shown to interact with clathrin adaptors (Beck and Keen, 1991), but the physiological significance of these findings had remained unclear. Our studies led to a model in which the selective concentration of PI(4,5)P₂ in the plasma membrane is required for the recruitment of clathrin adaptors and other endocytic factors to this membrane, while the tight coupling of PI(4,5)P₂ dephosphorylation by synaptojanin to the dynamin-dependent fission reaction of endocytosis is required to allow the shedding of such factors once the vesicle has undergone separation from the plasma membrane (Cremona et al., 1999; Cremona and De Camilli, 2001; Wenk et al., 2001). We also discovered curvature-generating/sensing proteins (BAR domain–containing proteins, primarily endophilin) that are responsible for achieving this coupling by coordinating the formation of the narrow neck of endocytic buds with the recruitment of synaptojanin (Takei et al., 1999; Farsad et al., 2001; Frost et al., 2009; Wu et al., 2010; Milosevic et al., 2011). This model, which is encapsulated in Figure 1 and which is supported by genetic studies in mice and other model organisms, posits that a cycle of PI phosphorylation and dephosphorylation is nested within the synaptic vesicle cycle. The implication of a phosphoinositide phosphatase in membrane transport converged with the identification of a PI kinase (the PI 3-kinase Vps34) involved in “vacuolar protein sorting” in yeast (Schu et al., 1993). This raised the possibility that the phosphorylation and dephosphorylation of inositol phospholipids could have a general significance in membrane traffic (De Camilli et al., 1996).Open in a separate windowFIGURE 1:Schematic cartoon depicting the occurrence of a cycle of phosphatidylinositol phosphorylation–dephosphorylation nested within the exo-endocytic recycling of synaptic vesicles at synapses. PI(4,5)P₂ in the plasma membrane helps define this membrane as the acceptor for the fusion of synaptic vesicles and functions as a coreceptor for endocytic factors responsible for their reinternalization. PI(4,5)P₂ dephosphorylation by synaptojanin allows the shedding of endocytic factors and the reutilization of vesicles for a new cycle of secretion. Recruitment of synaptojanin is coupled to dynamin-dependent fission via its binding to the BAR domain–containing protein endophilin, a curvature-generating/sensing protein that also binds dynamin.The field grew very rapidly. It was found that reversible phosphorylation of phosphatidylinositol at the 3,4 and 5 positions of the inositol ring by a multiplicity of enzymes generates seven phosphoinositide species whose differential protein-binding specificities help control, often via a dual key mechanism (Wenk and De Camilli, 2004), membrane cytosol interfaces (Di Paolo and De Camilli, 2006; Vicinanza et al., 2008; Balla, 2013). Along with small GTPases (Behnia and Munro, 2005), phosphoinositides are key determinants of the identity of membranes or membrane subdomains and thus control the multiplicity of reactions that occur at membrane–cytosol interfaces, such as the recruitment and shedding of trafficking proteins, the function of integral membrane proteins, and the assembly and disassembly of signaling and cytoskeleton complexes. Importantly, it became clear that, as we had found for synaptojanin, interconversion of phosphoinositide species along the secretory and endocytic pathways functions as a switch to release membrane-associated factors that define one compartment and to recruit factors that define the next compartment (Odorizzi et al., 2000; Di Paolo and De Camilli, 2006; Zoncu et al., 2009). I think I would not have immediately grasped the significance of the identification of synaptojanin and then invested much of our work in this field had it not been for my early research experience in medical school.The roles of phosphoinositides in membrane identity imply the existence of mechanisms to tightly control their localizations and also to ensure availability of PI in membranes—primarily the plasma membrane—where the backbone of this lipid is consumed by PLC. Surprisingly, genetic evidence suggested that one mechanism to control PI4P levels in membranes is via their direct contacts with the endoplasmic reticulum (ER), where a main PI4P phosphatase is localized (Foti et al., 2001; Stefan et al., 2011; Mesmin et al., 2013). Concerning availability of PI, DAG generated by PLC and its phosphorylated downstream product phosphatidic acid must be returned to the ER for metabolic recycling by the ER-localized phosphatidylinositol synthase, and newly synthesized PI then needs to be rapidly transported to the plasma membrane to replace its depleted pool. Strong evidence indicates that these reactions are mediated at least in part by lipid transfer proteins, and we have now learned that much of this protein-mediated lipid transport occurs at membrane contact sites (Mesmin et al., 2013; Wong et al., 2017; Cockcroft and Raghu, 2018; Pemberton et al., 2020). This is what motivated my lab to enter the young field of membrane contact sites. Here, once again, my early appreciation of the importance of lipid transfer proteins during medical school had an impact on my decision to invest in this rapidly developing area of cell biology.Not only has it been rewarding to help expand the inventory of proteins known to function both as membrane tethers and as lipid transporters, and to elucidate their function and regulation (Giordano et al., 2013; Schauder et al., 2014; Chung et al., 2015; Dong et al., 2016; Saheki et al., 2016), but this has been an opportunity, in collaboration with my Yale colleague Karin Reinisch, to participate in the discovery of something unexpected and new in the biology of eukaryotic cells. Until recently, it was thought that nonvesicular lipid transport, including the transport occurring at membrane contact sites, occurred only via protein modules that function as shuttles between two bilayers, often acting as countertransporters, delivering different cargoes as they move back and forth between two closely apposed membranes. However, our investigations of VPS13, a protein originally identified in yeast for its function in membrane transport and subsequently linked to lipid dynamics (Lang et al., 2015; Park et al., 2016), raised the possibility that this protein, and thus also the closely related autophagy factor ATG2, may be the founding members of a protein superfamily with bulk lipid transport properties, that is, proteins that could facilitate the net fluxes of lipids between adjacent membranes, and thus in membrane expansion (Kumar et al., 2018). As was subsequently shown by structural studies (Valverde et al., 2019; Li et al., 2020), these rod-like proteins harbor a hydrophobic groove that spans their length, thus supporting a bridge-like model of lipid transport to support bulk lipid flow (Li et al., 2020; Guillen-Samander et al., 2021; Leonzino et al., 2021). Inspection of protein sequence and structure databases suggests the existence of several other proteins with these properties, implying that this mode of lipid transport (a bridge-like mechanism), previously described for the transport of lipids from the inner to the outer membrane of Gram-negative bacteria (Bishop, 2019), may be of broad relevance in cell biology. As mutations in VPS13 family proteins result in neurodegenerative diseases, including a Huntington-like disease and Parkinson’s disease (Ugur et al., 2020), this is a field rich in medical implications. In fact, my curiosity about disease mechanisms stemming from my medical school training contributed to making me specially interested in studying these proteins.It has been a wonderful journey, rich with twists and turns, exploring new territories and continuously discovering that there are untapped frontiers whose existence we still do not know about. During the course of my career, I found especially rewarding bringing together different fields and finding new connections between fundamental cell biology and medicine, more so in recent years, where genetic studies of diseases often provide the first clues to molecular and cellular mechanisms. As I have emphasized in this essay, each of our experiences, no matter how successful, becomes part of the body of knowledge that defines us and has an impact on our research trajectory. I typically encourage students to search for training opportunities in different settings rather than follow a linear path, as this will enable them to bring together in their independent careers the expertise and knowledge of different worlds and thus to carry out original science. Our specific and unique paths are what make our research innovative and special.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

An Improved Algorithm for the Piecewise-Smooth Mumford and Shah Model in Image Segmentation

An improved algorithm for the piecewise-smooth Mumford and Shah functional is presented. Compared to the previous work of Chan and Vese, and Choi et al., extensions of the key functions are replaced by updating the level set function based on an artificial image that is composed of the diffused image and the original image. The low convergence problem of the classical algorithm is efficiently solved in the proposed approach. The resulting algorithm has also been demonstrated by several cases.[1,2,3,4,5,6,7,8,9,10,11]     

Genetic Analysis of the nuo Locus,Which Encodes the Proton-Translocating NADH Dehydrogenase in Escherichia coli

Complex I (EC 1.6.99.3) of the bacterium Escherichia coli is considered to be the minimal form of the type I NADH dehydrogenase, the first enzyme complex in the respiratory chain. Because of its small size and relative simplicity, the E. coli enzyme has become a model used to identify and characterize the mechanism(s) by which cells regulate the synthesis and assembly of this large respiratory complex. To begin dissecting the processes by which E. coli cells regulate the expression of nuo and the assembly of complex I, we undertook a genetic analysis of the nuo locus, which encodes the 14 Nuo subunits comprising E. coli complex I. Here we present the results of studies, performed on an isogenic collection of nuo mutants, that focus on the physiological, biochemical, and molecular consequences caused by the lack of or defects in several Nuo subunits. In particular, we present evidence that NuoG, a peripheral subunit, is essential for complex I function and that it plays a role in the regulation of nuo expression and/or the assembly of complex I.

Complex I (NADH:ubiquinone oxidoreductase; EC 1.6.99.3), a type I NADH dehydrogenase that couples the oxidation of NADH to the generation of a proton motive force, is the first enzyme complex of the respiratory chain (2, 35, 47). The Escherichia coli enzyme, considered to be the minimal form of complex I, consists of 14 subunits instead of the 40 to 50 subunits associated with the homologous eukaryotic mitochondrial enzyme (17, 29, 30, 48–50). E. coli also possesses a second NADH dehydrogenase, NDH-II, which does not generate a proton motive force (31). E. coli complex I resembles eukaryotic complex I in many ways (16, 17, 30, 49): it performs the same enzymatic reaction and is sensitive to a number of the same inhibitors, it consists of subunits homologous to those found in all proton-translocating NADH:ubiquinone oxidoreductases studied thus far, it is comprised of a large number of subunits relative to the number that comprise other respiratory enzymes, and it contains flavin mononucleotide and FeS center prosthetic groups. Additionally, it possesses an L-shaped topology (14, 22) like that of its Neurospora crassa homolog (27), and it consists of distinct fragments or subcomplexes. Whereas eukaryotic complex I can be dissected into a peripheral arm and a membrane arm, the E. coli enzyme consists of three subcomplexes referred to as the peripheral, connecting, and membrane fragments (29) (Fig. ​(Fig.1A).1A). The subunit composition of these three fragments correlates approximately with the organization of the 14 structural genes (nuoA to nuoN) (49) of the nuo (for NADH:ubiquinone oxidoreductase) locus (Fig. ​(Fig.1B),1B), an organization that is conserved in several other bacteria, including Salmonella typhimurium (3), Paracoccus denitrificans (53), Rhodobacter capsulatus (12), and Thermus thermophilus (54). The 5′ half of the locus contains a promoter (nuoP), previously identified and located upstream of nuoA (8, 49), and the majority of genes that encode subunits homologous to the nucleus-encoded subunits of eukaryotic complex I and to subunits of the Alcaligenes eutrophus NAD-reducing hydrogenase (17, 29, 30, 49). In contrast, the 3′ half contains the majority of the genes that encode subunits homologous to the mitochondrion-encoded subunits of eukaryotic complex I and to subunits of the E. coli formate-hydrogen lyase complex (17, 29, 30, 49). Whereas the nuclear homologs NuoE, NuoF, and NuoG constitute the peripheral fragment (also referred to as the NADH dehydrogenase fragment [NDF]), the nuclear homologs NuoB, NuoC, NuoD, and NuoI constitute the connecting fragment. The mitochondrial homologs NuoA, NuoH, NuoJ, NuoK, NuoL, NuoM, and NuoN constitute the membrane fragment (29). E. coli complex I likely evolved by fusion of preexisting protein assemblies constituting modules for electron transfer and proton translocation (17–19, 30). Open in a separate windowFIG. 1Schematic of E. coli complex I and the nuo locus. Adapted with permission of the publisher (17, 29, 30, 49). (A) E. coli complex I is comprised of three distinct fragments: the peripheral (light gray), connecting (white), and membrane (dark gray) fragments (17, 29). The peripheral fragment (NDF) is comprised of the nuclear homologs NuoE, -F, and -G and exhibits NADH dehydrogenase activity that oxidizes NADH to NAD⁺; the connecting fragment is comprised of the nuclear homologs NuoB, -C, -D, and -I; and the membrane fragment is comprised of the mitochondrial homologs NuoA, -H, and -J to -N and catalyzes ubiquinone (Q) to its reduced form (QH₂). FMN, flavin mononucleotide. (B) The E. coli nuo locus encodes the 14 Nuo subunits that constitute complex I. The 5′ half of the locus contains a previously identified promoter (nuoP) and the majority of genes that encode the peripheral and connecting subunits (light gray and white, respectively). The 3′ half of the locus contains the majority of the genes encoding the membrane subunits (dark gray). The 3′ end of nuoG encodes a C-Terminal region (CTR) of the NuoG subunit (hatched).Because of its smaller size and relative simplicity, researchers recently have begun to utilize complex I of E. coli, and that of its close relative S. typhimurium, to identify and characterize the mechanism(s) by which cells regulate the synthesis and assembly of this large respiratory complex (3, 8, 46) and to investigate the diverse physiological consequences caused by defects in this enzyme (4, 6, 10, 40, 59). Such defects affect the ability of cells to perform chemotaxis (40), to grow on certain carbon sources (4, 6, 10, 40, 57), to survive stationary phase (59), to perform energy-dependent proteolysis (4), to regulate the expression of at least one gene (32), and to maintain virulence (5).To begin dissecting the processes by which E. coli cells regulate the expression of nuo and the assembly of complex I, we undertook a genetic analysis of the nuo locus. Here, we present the results of studies, performed on an isogenic collection of nuo mutants, that focus on the physiological, biochemical, and molecular consequences caused by the lack of or defects in several Nuo subunits. In particular, we present evidence that NuoG, a peripheral subunit, is essential for complex I function and that it plays a role in the regulation of nuo expression and/or the assembly of complex I.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Biochemical Characterization and Function of Complexes Formed by Hyaluronan and the Heavy Chains of Inter-��-inhibitor (HC��HA) Purified from Extracts of Human Amniotic Membrane

Clinically, amniotic membrane (AM) suppresses inflammation, scarring, and angiogenesis. AM contains abundant hyaluronan (HA) but its function in exerting these therapeutic actions remains unclear. Herein, AM was extracted sequentially with buffers A, B, and C, or separately by phosphate-buffered saline (PBS) alone. Agarose gel electrophoresis showed that high molecular weight (HMW) HA (an average of ∼3000 kDa) was predominantly extracted in isotonic Extract A (70.1 ± 6.0%) and PBS (37.7 ± 3.2%). Western blot analysis of these extracts with hyaluronidase digestion or NaOH treatment revealed that HMW HA was covalently linked with the heavy chains (HCs) of inter-α-inhibitor (IαI) via a NaOH-sensitive bond, likely transferred by the tumor necrosis factor-α stimulated gene-6 protein (TSG-6). This HC·HA complex (nHC·HA) could be purified from Extract PBS by two rounds of CsCl/guanidine HCl ultracentrifugation as well as in vitro reconstituted (rcHC·HA) by mixing HMW HA, serum IαI, and recombinant TSG-6. Consistent with previous reports, Extract PBS suppressed transforming growth factor-β1 promoter activation in corneal fibroblasts and induced mac ro phage apo pto sis. However, these effects were abolished by hyaluronidase digestion or heat treatment. More importantly, the effects were retained in the nHC·HA or rcHC·HA. These data collectively suggest that the HC·HA complex is the active component in AM responsible in part for clinically observed anti-inflammatory and anti-scarring actions.Hyaluronan (HA)⁴ is widely distributed in extracellular matrices, tissues, body fluids, and even in intracellular compartments (reviewed in Refs. 1 and 2). The molecular weight of HA ranges from 200 to 10,000 kDa depending on the source (3), but can also exist as smaller fragments and oligosaccharides under certain physiological or pathological conditions (1). Investigations over the last 15 years have suggested that low M_r HA can induce the gene expression of proinflammatory mediators and proangiogenesis, whereas high molecular weight (HMW) HA inhibits these processes (4–7).Several proteins have been shown to bind to HA (8) such as aggrecan (9), cartilage link protein (10), versican (11), CD44 (12, 13), inter-α-inhibitor (IαI) (14, 15), and tumor necrosis factor-α stimulated gene-6 protein (TSG-6) (16, 17). IαI consists of two heavy chains (HCs) (HC1 and HC2), both of which are linked through ester bonds to a chondroitin sulfate chain that is attached to the light chain, i.e. bikunin. Among all HA-binding proteins, only the HCs of IαI have been clearly demonstrated to be covalently coupled to HA (14, 18). However, TSG-6 has also been reported to form stable, possibly covalent, complexes with HA, either alone (19, 20) or when associated with HC (21).The formation of covalent bonds between HCs and HA is mediated by TSG-6 (22–24) where its expression is often induced by inflammatory mediators such as tumor necrosis factor-α and interleukin-1 (25, 26). TSG-6 is also expressed in inflammatory-like processes, such as ovulation (21, 27, 28) and cervical ripening (29). TSG-6 interacts with both HA (17) and IαI (21, 24, 30–33), and is essential for covalently transferring HCs on to HA (22–24). The TSG-6-mediated formation of the HC·HA complex has been demonstrated to play a crucial role in female fertility in mice. The HC·HA complex is an integral part of an expanded extracellular “cumulus” matrix around the oocyte, which plays a critical role in successful ovulation and fertilization in vivo (22, 34). HC·HA complexes have also been found at sites of inflammation (35–38) where its pro- or anti-inflammatory role remain arguable (39, 40).Immunostaining reveals abundant HA in the avascular stromal matrix of the AM (41, 42).⁵ In ophthalmology, cryopreserved AM has been widely used as a surgical graft for ocular surface reconstruction and exerts clinically observable actions to promote epithelial wound healing and to suppress inflammation, scarring, and angiogenesis (for reviews see Refs. 43–45). However, it is not clear whether HA in AM forms HC·HA complex, and if so whether such an HC·HA complex exerts any of the above therapeutic actions. To address these questions, we extracted AM with buffers of increasing salt concentration. Because HMW HA was found to form the HC·HA complex and was mainly extractable by isotonic solutions, we further purified it from the isotonic AM extract and reconstituted it in vitro from three defined components, i.e. HMW HA, serum IαI, and recombinant TSG-6. Our results showed that the HC·HA complex is an active component in AM responsible for the suppression of TGF-β1 promoter activity, linkable to the scarring process noted before by AM (46–48) and by the AM soluble extract (49), as well as for the promotion of macrophage death, linkable to the inflammatory process noted by AM (50) and the AM soluble extract (51).     

In-depth Characterization of the Cerebrospinal Fluid (CSF) Proteome Displayed Through the CSF Proteome Resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.Cerebrospinal fluid (CSF)¹ surrounds and supports the central nervous system (CNS), including the ventricles and subarachnoid space (1). About 80% of the total protein amount in CSF derives from size-dependent filtration of blood across the blood-brain barrier (BBB), and the rest originate from drainage of interstitial fluid from the CNS (2–4). Because CSF is in direct contact with the CNS, it should be a promising source for finding biomarkers for diseases in the CNS (5).Mapping studies characterizing the human CSF proteome and peptidome has previously been carried out using various experimental designs, including both healthy and disease-affected individuals (5–16). A total of 2630 proteins were detected in normal CSF by immunoaffinity depletion of high abundant proteins followed by strong cation exchange fractionation and LC-MS (5), whereas proteome and peptidome analyses of human CSF (collected for diagnostic purposes and turned out normal) by gel separation and trypsin digestion followed by LC-MS analysis have shown 798 proteins and 563 peptide products (derived from 91 precursor proteins) (6). In another publication, Pan et al. combined several proteomics studies in CSF from both normal subjects and subjects with neurological diseases and created a dataset of 2594 identified proteins (16). But in general, the availability and usefulness of published data from proteome mapping experiments is scarce, and the format of the data often makes searching and comparison across datasets difficult. Thus, organizing the data in online databases would greatly benefit the scientific community by making the data more accessible and easier to query. Current online databases containing MS data for CSF include the Sys-BodyFluid, with a total of 1286 CSF proteins from six studies (17). The proteome identifications database (PRIDE) (18) includes 19 studies on human CSF, but none reporting more than 103 identified proteins.Glycosylation is one of the most common post-translational modifications (PTMs), and many known clinical biomarkers as well as therapeutic targets are glycoproteins (19–25). Furthermore, glycosylation plays important roles in cell communication, signaling, aging, and cell adhesion (26, 27). Nevertheless, there are few studies on glycoprotein identification in CSF. One study identified 216 glycoproteins in CSF using both lectin affinity and hydrazide chemistry (8), and another reported 36 N-linked and 44 O-linked glycosylation sites, from 23 and 22 glycoproteins respectively, by enriching for sialic-acid containing glycopeptides (28).Considering the sparse information about the CSF proteome available in public repositories, we have combined several proteomics approaches to create a map of the global CSF proteome, the CSF glycoproteome, and the respective plasma proteome from a pool of 21 (20 for the plasma pool) neurologically healthy individuals. The large amount of data generated through these four datasets (with linked and complementary information) would not easily be accessible through existing repositories. We therefore developed the open access CSF Proteome Resource (CSF-PR, www.probe.uib.no/csf-pr), an online database including the detailed data from the four different proteomics experiments described in this study. CSF-PR will be particularly useful in guiding the selection of appropriate signature peptides for the development of targeted CSF protein assays.     

The -Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted -values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramér-von Mises two-sample test, based on a certain -distance between two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramér-von Mises test statistic in finite samples. However, the computation is very time- and space-consuming. An counterpart of the Cramér-von Mises test represents an appealing alternative. In this work, we present an efficient algorithm for computing exact quantiles of the -distance test statistic. The performance and power of the -distance test are compared with those of the Cramér-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The -distance test appears to be nearly as powerful as its counterpart. The lower computational intensity of the -distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramér-von Mises test.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]     

RNA Interference Screen to Identify Kinases That Suppress Rescue of ΔF508-CFTR

Cystic Fibrosis (CF) is an autosomal recessive disorder caused by mutations in the gene encoding the Cystic fibrosis transmembrane conductance regulator (CFTR). ΔF508-CFTR, the most common disease-causing CF mutant, exhibits folding and trafficking defects and is retained in the endoplasmic reticulum, where it is targeted for proteasomal degradation. To identify signaling pathways involved in ΔF508-CFTR rescue, we screened a library of endoribonuclease-prepared short interfering RNAs (esiRNAs) that target ∼750 different kinases and associated signaling proteins. We identified 20 novel suppressors of ΔF508-CFTR maturation, including the FGFR1. These were subsequently validated by measuring channel activity by the YFP halide-sensitive assay following shRNA-mediated knockdown, immunoblotting for the mature (band C) ΔF508-CFTR and measuring the amount of surface ΔF508-CFTR by ELISA. The role of FGFR signaling on ΔF508-CFTR trafficking was further elucidated by knocking down FGFRs and their downstream signaling proteins: Erk1/2, Akt, PLCγ-1, and FRS2. Interestingly, inhibition of FGFR1 with SU5402 administered to intestinal organoids (mini-guts) generated from the ileum of ΔF508-CFTR homozygous mice resulted in a robust ΔF508-CFTR rescue. Moreover, combination of SU5402 and VX-809 treatments in cells led to an additive enhancement of ΔF508-CFTR rescue, suggesting these compounds operate by different mechanisms. Chaperone array analysis on human bronchial epithelial cells harvested from ΔF508/ΔF508-CFTR transplant patients treated with SU5402 identified altered expression of several chaperones, an effect validated by their overexpression or knockdown experiments. We propose that FGFR signaling regulates specific chaperones that control ΔF508-CFTR maturation, and suggest that FGFRs may serve as important targets for therapeutic intervention for the treatment of CF.Cystic fibrosis (CF)¹ is a pleiotropic disease caused by an abnormal ion transport in the secretory epithelia lining the tubular organs of the body such as lungs, intestines, pancreas, liver, and male reproductive tract. In the airways of CF patients, reduced Cl⁻ and bicarbonate secretion caused by lack of functional Cystic fibrosis transmembrane conductance regulator (CFTR) on the apical surface, and hyper-absorption of Na⁺ because of elevated activity of ENaC (1), lead to a dehydration of the airway surface liquid (ASL). This reduces the viscosity of the mucus layer and the deposited layer of thickened mucus creates an environment that promotes bacterial colonization, which eventually leads to chronic infection of the lungs and death (2, 3).CFTR is a transmembrane protein that functions as a cAMP-regulated, ATP-dependent Cl⁻ channel that also allows passage of bicarbonate through its pore (4, 5). It also possesses ATPase activity important for Cl⁻ conductance (6, 7). The CFTR structure is predicted to consist of five domains: two membrane spanning domains (MSD1, MSD2), each composed of six putative transmembrane helices, two nucleotide binding domains (NBD1, NBD2), and a unique regulatory (R) region (8).More than 1900 CFTR mutations have been identified to date (www.genet.sickkids.on.ca/cftr). The most common mutation is a deletion of phenylalanine at position 508 (ΔF508 or ΔF508-CFTR) in NBD1 (9). The ΔF508 mutation causes severe defects in the processing and function of CFTR. The protein exhibits impaired trafficking from the endoplasmic reticulum (ER) to the plasma membrane (PM), impaired intramolecular interactions between NBD1 and the transmembrane domain, and cell surface instability (10–15). Nevertheless, the ΔF508 defect can be corrected, because treating cells expressing ΔF508-CFTR with low temperature or chemical chaperones (e.g. glycerol) can restore some surface expression of the mutant (11, 16).Numerous small molecules that can at least partially correct (or potentiate) the ΔF508-CFTR defect have been identified to date (17–27), and some were already tested in clinical trials (e.g. sildenafil, VX-809/Lumacaftor), or have made it to the clinic (VX-770/Kalydeco/Ivacaftor) (http://www.cff.org/research/DrugDevelopmentPipeline/). However, the need to identify new ΔF508-CFTR correctors remains immense as the most promising corrector, VX-809, has proven ineffective in alleviating lung disease of CF patients when administered alone (27). Thus, our group developed a high-content technology aimed at identifying proteins and small molecules that correct the trafficking and functional defects of ΔF508-CFTR (28). We successfully used this approach to carry out three separate high-content screens: a protein overexpression screen (28), a small-molecule kinase inhibitor screen (29) and a kinome RNA interference (RNAi) screen, described here.     

Sequence Diversity of the oprI Gene,Coding for Major Outer Membrane Lipoprotein I,among rRNA Group I Pseudomonads

The sequence of oprI, the gene coding for the major outer membrane lipoprotein I, was determined by PCR sequencing for representatives of 17 species of rRNA group I pseudomonads, with a special emphasis on Pseudomonas aeruginosa and Pseudomonas fluorescens. Within the P. aeruginosa species, oprI sequences for 25 independent isolates were found to be identical, except for one silent substitution at position 96. The oprI sequences diverged more for the other rRNA group I pseudomonads (85 to 91% similarity with P. aeruginosa oprI). An accumulation of silent and also (but to a much lesser extent) nonsilent substitutions in the different sequences was found. A clustering according to the respective presence and/or positions of the HaeIII, PvuII, and SphI sites could also be obtained. A sequence cluster analysis showed a rather widespread distribution of P. fluorescens isolates. All other rRNA group I pseudomonads clustered in a manner that was in agreement with other studies, showing that the oprI gene can be useful as a complementary phylogenetic marker for classification of rRNA group I pseudomonads.Pseudomonads are increasingly being recognized as important microorganisms in our biosphere, and Pseudomonas aeruginosa and Pseudomonas fluorescens are two important representatives of this genus. As a typical opportunist, P. aeruginosa is more and more involved in a variety of often fatal nosocomial infections, in which it accounts for more than 11% of all isolates recovered (29). In cystic fibrosis, one of the most common autosomal recessive genetic diseases, it is a characteristic pathogen responsible for most of the cases of morbidity and mortality (16, 38). In general, fluorescent pseudomonads, including P. aeruginosa, Pseudomonas putida, P. fluorescens, and other species, are frequently found as rhizosphere microorganisms, in some cases promoting plant growth (11, 19, 20).P. fluorescens and P. aeruginosa are also found as inherent flora of mineral water (14, 39). Identification of fluorescent pseudomonads is often tedious and not reliable. Indeed, the present taxonomy of this group is far from clear at the finer taxonomic level, as polyphasic investigations have demonstrated (4, 13, 18, 26). Ribosomal RNAs have been applied as molecular markers with great success to unravel the rough phylogenetic structure which, at the finer level, is not always in complete agreement with the genotypic and phenotypic similarities deduced from other parts of the genome. Horizontal gene transfer, chromosomal mutation hot spots, and internal genomic rearrangements are probably the bases of these discrepancies at the species and subspecies levels. These arguments, together with the importance of discriminating phenotypic tests in routine identifications, support a polyphasic approach in bacterial taxonomy (2, 8–10, 13, 37, 40). Additional phylogenetic information requires the identification of molecules, like the recA or the gyrB genes, that are widely distributed, large enough to contain a substantial amount of information, and conserved to an appropriate degree (24, 46). In the phylogenetic tree published by Woese (43), species with the same generic name were allocated in phylogenetically distant groups. This was the case for the “genus” Pseudomonas, which is known to be a dump of assemblages of distantly related species (3, 8–10, 17). Taxonomic rearrangements of the genus Pseudomonas sensu stricto resulted in the splitting of the genus and as a logical consequence, the present genus Pseudomonas is restricted to the rRNA group I organisms, with P. aeruginosa as the type species in this group (27, 28, 37, 42, 44, 45).The oprI gene, coding for the outer membrane lipoprotein I of P. aeruginosa (5), was found to be conserved among the fluorescent pseudomonads and was considered to be a possible phylogenetic marker (6, 31). In this study, we tested whether the oprI gene could be a useful detection and identification target molecule as well as a complementary phylogenetic marker for rRNA group I pseudomonads. Also, we examined to what extent the sequence variation of the oprI gene reflects the species diversity in P. aeruginosa and P. fluorescens.