As there are a number of ways to divide up the anatomical structure of an organism, each may be represented by more than one valid partonomic (part-of) hierarchy. This raises the issue of how to represent and integrate multiple such hierarchies.Anatomy ontologies play an increasingly important role in developing integrated bioinformatics applications. One of the primary relationships between anatomical tissues represented in such ontologies is In this paper we describe a solution that is based on our work on an anatomy ontology for mouse embryo development, which is part of the Edinburgh Mouse Atlas Project (EMAP). The paper describes the basic conceptual aspects of our approach and discusses strengths and limitations of the proposed solution. A prototype was implemented in Prolog for evaluation purposes.With the proposed name set approach, rather than having to standardise hierarchies, it is sufficient to agree on a suitable set of basic tissue terms and their meaning in order to facilitate the integration of multiple partonomic hierarchies. Drosophila, zebrafish and C elegans, can be found on the Open Biological Ontologies web site [As the bioinformatics emphasis has shifted from gene sequence analysis to functional genomics and proteomics, the need to describe gene function in the context of specific tissues of an organism has increased. Hence, in addition to anatomy ontologies built for medical purposes, e.g. GALEN , descripweb site .part-of. It is possible to distinguish between different kinds of part-of, e.g. structural part-of and functional part-of. Each anatomy ontology may define one or more such part-of relationships.An ontology model typically consists of concepts and relationships between these concepts. One of the key relationships in anatomy is equivalent to a tissue in another hierarchy, and what are the part-of relationships across these hierarchies?Even for a single type of part-of, there may be more than one correct way to devide the anatomy of an organism into parts and subparts. Hence, multiple valid partonomic (part-of) hierarchies may exist for any organism. This raises the issue of interoperability across such hierarchies: when is a tissue in one hierarchy In general, biologists refer to tissues by their names, unlike computers which can easily work with ID numbers. For example the name "/embryo/limb/forelimb bud/ectoderm" is used to describe the ectoderm of the forelimb bud of the limb of the mouse embryo. Although this name uniquely identifies the tissue, it does so by encoding the particular partonomic hierarchy in the name. This causes problems when trying to work with more than one single hierarchy. This paper discusses a naming scheme that preserves the unique identification property of tissue names, without having to restrict it to a particular hierarchy, thus making it easier to integrate multiple partonomic hierarchies.There is a large body of work discussing mereology (part-of relationships) in the biomedical literature. For example, Rogers and Rector  describeWe are not aware of any previous work dealing specifically with the integration of multiple part-of anatomy hierarchies. However, ontology alignment and integration in general is an active reserach area and has produced tools that aim at helping with the manual alignment of ontologies as well as with the automation of ontology integration. Examples of such tools include OntoMorph , OntoMerThe remainder of the paper is organised as follows. The next section introduces the Edinburgh Mouse Atlas, which forms the basis of the work presented here. Thereafter, the issue of multiple part-of hierarchies is discussed. The next section introduces the developed name set representation, followed by a discussion that covers the implementation of a Prolog prototype system. The proposed approach is then evaluated in the discussion section, followed by our conclusions.in situ gene expression database is spatially mapped onto the atlas allowing the users to query gene expression patterns using the 3D embryo model and/or the ontology as a reference.The Edinburgh Mouse Atlas (EMAP) and Gene Expression (EMAGE) Database project -16 has dTheiler stages (TS1-TS26). Each stage is primarily organised as a structural part-of tree, or partonomic hierarchy. Figure Following the description of mouse embryo development by Theiler , the anastages TS-TS26. Eanon-overlapping (exclusive) and complete, i.e. they describe all distinct parts of the parent tissue. For example, in Figure distinct from each other and are the only parts of the trophectoderm at that stage. Although this holds for EMAP, it is not a requirement for the proposed approach. The tissues represented by subnodes of a node in the tree are intended to be full name. A full name is an n-tuple: (t0, t1,...,tn). The path name of the tissue is (t0, t1,..., tn-1). The component name is tn. For example, given the tissue name (using a file directory style notation):Each tissue can be uniquely identified by its /embryo/branchial arch/3rd arch/branchial pouch/endoderm/dorsalits full name is:its path name is:and its component name is:dorsalgroup, with the appropriate subparts identified. Figure Although the ontology covers all parts of the mouse embryo, there may not be a single node representing a particular tissue of interest. For example, there is no single node named . However, there is a tissue named , which has somite 05 to somite 20 as subparts (somite 05 to somite 20 are part of that tissue), and each of those has a subpart with component name sclerotome. The approach taken in EMAP is to introduce a new tissue node, called a groups to EMAP is addressing the need for alternative arrangements of the part-of hierarchy, it does also raise a number of new questions. For example, it requires a suitable algorithm to determine appropriate tissues of which the new group should be part of. Also, some restrictions should be put in place to constrain what new links can be added; for example, if a new group contains all parts of some other tissue, then that tissue itself, rather than all of its parts, should be linked to the group. In other words, we require a mechanism that prevents biologists from adding too many part-of links unnecessarily. Let us assume that a new group needs to be introduced that contains leg as one of its parts. In this case the biologist should introduce a single part-of link between the new group and leg, and not multiple part-of links between the new group and hip, knee, lower leg and upper leg (which are the parts a leg consists of). The fact that these are parts of the group should be deduced from the transitivity property of the part-of relationship. These and other considerations seem representative of the more general problem of trying to integrate multiple part-of hierarchies over the same anatomical space. The remainder of this paper describes a possible solution to this problem.Although adding the notion of As previously mentioned, there is more than one way to structure the anatomical part-of hierarchy of an organism. The intersection of these hierarchies may occur at any level; they may share some or all of the their leaf nodes, but may also share intermediate nodes. A particular hierarchy may only deal with part of the organism, e.g. brain or heart, while others, such as EMAP, cover the entire organism.The central example we use in this paper is that of somites. The somites are a repeating anatomical structure down the back of the animal. They give rise to the vertebrae, muscles of the backbone, skin and other structures. Each somite is divided into 3 parts: dermomyotome, myocoele and sclerotome. The dermomyotome is a group of cells which form the dermal layer of the skin and muscle tissue. The myocoele is a fluid-filled cavity of the somite, and the sclerotome gives rise to the bone of the vertebrae.Most ontologies require each of their concepts to be uniquely identified by a name. In the context of an anatomical ontology, such as EMAP, it is clearly not enough to simply use the name sclerotome when wanting to refer to the sclerotome of somite 18. In general, the full name of the tissue is required, though in some cases a part of it may be sufficient, e.g. there is only one tissue at Theiler stage 14 that has component name somite 18.Focusing on the somite part of the anatomy given in Figure T, equivalence between two tissues is identified by the equivalence of their name sets:Each tissue in a part-of hierarchy is identified by the set of component names along the path from the root to the tissue (including the component name of the tissue itself). For example, in H1 the dermomyotome of somites 5 and 20 are represented as {dermomyotome, somite, somite 05} and {dermomyotome, somite, somite 20}, respectively; and in H2 somite 20's dermomyotome is represented as {dermomyotome, somite, somite 20}. Using NS(T) to denote the name set of tissue NS(Ti) = NS(Tj) → Ti = TjTi Tj denote that Ti has Tj as a direct subpart, and let Ti Tj denote that Ti has Tj as a subpart (direct or indirect)1, i.e. Ti  Tj ... Tk implies Ti Tk, then the part-of relationships can be derived from the name sets as follows:Let NS(Ti) ⊂ NS(Tj) → Ti Tj andTi Tj ∧ (¬ ∃k·Ti Tk ∧ Tk Tj) → Ti TjTi has Tj as a subpart, if the name set of the first is a proper subset of the name set of the second. The second line states that Ti has Tj as a direct subpart (or child tissue) if Ti has Tj as one of its subparts, and there are no other subparts of Ti which themselves have Tj as one of their own subparts. In the graph representing the ontology, an arrow is drawn from Ti to Tj if, and only if, Ti Tj.The first line simply states that classes and instances of tissues. For example, while in general it holds that a leg has a lower leg part, this may not be true in specific instances. The proposed representation does not deal with such instance issues; many of the existing model organism anatomy ontologies used in bioinformatics today similarly do not represent information at the instance level.The name set representation does not explicitly deal with temporal relationships. For example, changes in the anatomy of the developing embryo must be captured explicitly, i.e. if a particular subpart disappears from one developmental stage to the next, this should be reflected in the lack of that subpart in the ontological representation for the latter stage. Furthermore, the given representation does not explicitly distinguish between T1 in Figure T3) or any of the other parts of the embryo (not shown in the figure).A "rest-of" tissue is a tissue that represents all parts of that tissue other than those which are explicitly represented in a "sibling" of the rest-of tissue. For example, the embryo mesenchyme marked as T1 and T3  ⊂ NS(T3). Based on the definition from above, T1 T3 follows. This, however, is not true. In other words, the basic name set representation introduced earlier is not sufficient to cope with rest-of tissues.Looking at the name set representation of negative name sets (NSn) in addition to the name sets we already have  from now on). A tissue Tr includes in its negative name set the component name of any "sibling" tissue Ts, if Tshas a subpart with the same component name as Tr. For example, branchial arch is added to the negative name set of T1 because of T3  ⊂ NSp(Tj) ∧ NSn(Ti) ∩ NSp(Tj) = ∅ → Ti Tj andTi Tj ∧ (¬ ∃ k·Ti Tk ∧ Tk Tj) → Ti TjTi has Tj as a subpart, if the positive name set of Ti is a proper subset of the positive name set of Tj, and the intersection of the positive name set of Tj and the negative name set of Ti is empty. The intersection part has been added to enforce the exclusions needed to deal with rest-of cases. The second line's meaning is identical to what it was before.The first line states that T1 is now represented as NSp(T1) = {embryo, mysenchyme} and and NSn(T1) = {branchial arch}, T3 is represented as NSp(T3) = {1st arch, branchial arch, embryo, mesenchyme} and NSn(T3) = {}. Since NSn(T1) ∩ NSp(T3) = {branchial arch}, i.e. non-empty, T3 is not a subpart of T1, as required.Returning to the example in Figure T2  between tissues from different hierarchies follow directly from the rules described above. For example, applying these rules to the hierarchies in Figure view set to each tissue. For example, the view set for somite would be {H1, H2}, as it would be for all leaf node tissues in Figure Given the integrated name set representation of two or more hierarchies, it is not possible to determine which tissue belongs to which original hierarchy. For example, if asked for the immediate subparts of somite, based on the rules governing the part-of relationship, all of the tissues at the second level of the diagram in Figure Formalisation of Mouse Embryo Anatomy [A prototype of the name set representation for the Mouse Atlas anatomy ontology has been implemented in Prolog; an extension of the prototype we developed for our work on the  Anatomy . This ortissue(S, T, FN).• S: stage ID, e.g. 14 for Theiler stage 14;• T: tissue ID number (accession number);• FN: full name of tissue represented by the list [N1, N2, N3, ...]; hasPart(TID1, TID2).TID1 TID2;• TID2 is an immediate part of TID1, i.e. For the evaluation of the name set representation, we use an extended version of the tissue predicate (view handling is ommitted from the protoype description to keep our examples simple) :ext_tissue(S, T, FN, NSp, NSpL, NSn, NSnL).• S, T, FN: as above;• NSp: positive name set of tissue represented by a list [N1, N2, N3, ...];• NSpL: length of NSp;• NSn: negative name set of tissue represented by a list [N1, N2, N3, ...];• NSnL: length of NSn;For example, the embryo mesenchyme tissue of Figure ext_tissue.Tp Tc is true:The following Prolog clause is used to determine whether subPart(Tp, Tc) :-ext_tissue(Sp, Tp,_, NSpp, NSpLp, NSnp,_),ext_tissue(Sp, Tc,_, NSpc, NSpLc,_,_),NSpLc > NSpLp,ord_subset(NSpp, NSpc),ord_disjoint(NSpc, NSnp).proper subset relationships.Predicates ord_subset and ord_disjoint from the Prolog library were used to implement the set theoretic aspects of the representation. Although these predicates support ordered sets, this is not required for our representation (but there were no unordered set predicates in the library). NSpLc > NSpLp is required to enforce Tp Tc is true:The following two Prolog clauses are used to determine whether not_immediate_subPart(Tp, Tc) :-subPart(Tp, Tm),subPart(Tm, Tc).immediate_subPart(Tp, Tc) :-subPart(Tp, Tc),not not_immediate_subPart(Tp, Tc).The Prolog implementation given is not particularly efficient and there are a number of optimisations that could be put in place. However, as the purpose of the prototype was not to deliver a robust application for end-users, but a reference implementation of the proposed approach for evaluation purposes, it proved entirely sufficient.The paper makes no claims over the relative merits of different implementation strategies for the proposed approach. Alternatives to Prolog include using a relational database system or an ontology language, such as OWL (more details of OWL available from W3C ). The laFor evaluation purposes, a number of tests were carried out on the name set representation of the Mouse Atlas anatomy. These are discussed here, together with some general observations about the proposed approach.The first assumption that must hold is that no two tissues (at any given stage) have the same name set representation. This was tested usingtest1 :-ext_tissue(S, T1,_, NSp,_, NSn,_),ext_tissue(S, T2,_, NSp,_, NSn,_),T1 not = T2.test1 returns no, i.e. no two different tissues with the same name sets were found, as required.To test whether all part-of relationships can be reconstructed from the name set representation, we usedtest2 :- immediate_subpart(T1, T2), not hasPart(T1, T2).test3 :- hasPart(T1, T2), not immediate_subPart(T1, T2).Both, test2 and test3 return no, i.e. the name set representation does not lead to any part-of relationships that are not intended (test2), and all existing part-of relationships are found through the name sets (test3), as required.group in EMAP. A recently identified need for a group has been for all  tissues at Theiler stage 17. Using predicateThe smallest form of part-of hierarchy integration is the addition of a new tissue node, which is equivalent to adding a immediate_subPart_ns(S, NSp, NSn, T).• S: stage ID;• NSp: positive name set of new tissue node (group);• NSn: negative name set of new tissue node (group);• T: tissue ID of immediate sub-part of tissue identified by name sets and stage;we can write a "query" in the form:immediate_subPart_ns, tissue(_, T, FN),writeName(FN), nl, fail.and obtain the following result:  ...Similarly, using predicate immediate_superPart_ns, we obtain:immediate_superPart_ns is analogous, and its Prolog implementation very similar, to immediate_subPart_ns. Details are, therefore, omitted.The correctness of these results was confirmed by one of the biologists who created EMAP. Other, similar tests, worked equally well. A constraint put on all of these cases, however, is that the name set of the new group tissue must only contain names that are already used in the existing hierarchy.This raises the question of how to deal with the introduction of new component names. For example, the addition of a group (embryo, head) cannot automatically be carried out, since the existing hierarchy does not use head in its name sets. For the integration to work, it is first necessary to add head to the appropriate name sets in the existing hierarchy. This can be done at the highest appropriate levels, since sub-parts inherit all name set elements from their super-parts, and may therefore not require as much effort as one initially expects.For the head example, however, we did identify two additional problems which are likely to be typical in this context. Firstly, some agreement needs to be reached as to what in fact is considered to be part of the newly introduced tissue. In our example: how much of the neck is anatomically considered to be part of the head? The second problem deals with the fact that an existing tissue may need to be divided further in order to obtain the appropriate subparts for the newly introduced tissue. For example, the carotid artery runs from the head into the body of the mouse embryo, i.e. only a part of carotid artery is actually part of the head. Hence, the carotid artery needed to be divided into two subparts, one for the head section of it, one for the rest. In our name set approach, the former contains head in its positive name set, while the latter contains head in its negative name set. Of course, only the head section part becomes part of the head. Neither of these two problems presents any direct consequences for our approach.O1 includes midbrain as one of the parts of the brain, but no further detail, and O2 is a brain anatomy ontology that divides the midbrain into cerebral aqueduct, floor plate, lateral wall, etc., then we would find {brain, central nervous system, embryo, midbrain, mouse, nervous system, organ system} as the positive name set for midbrain in O1, and {brain, cerebral aqueduct, midbrain} as the positive name set in O2, resulting in {brain, central nervous system, cerebral aqueduct, embryo, midbrain, mouse, nervous system, organ system} – the union of these previous two name sets – as the representation of midbrain in the merged ontology. The meaning of the component names in the intersection of the two original names sets, {brain, midbrain} must have been used in a consistent manner for the merger to work, though many of the component names will differ across the ontologies, because of the different levels of granularity, e.g. the terms nervous system and organ system are unlikely to be found in the brain specific ontology. When merging ontologies of different granularity, the same principle as before applies: shared component names must be used in a consistent manner. Assuming ontology component names thus far, shows that some of them have additional structural complexity and if one wishes to take advantage of the semantics of these complexities, the proposed name set representation would need to be extended. For example, at Theiler stage 18 the tissue  has two subparts, called (..., mesenchyme derived from head mesoderm) and . The naming, hence, reflects lineage relationships between tissues, and the identity of a tissue is partially established by that relationship. Although extensions to the name set representation could be developed to allow the inclusion and subsequent reasoning over such information, it would lead to a semantic overloading of the name sets and for simplicity are, therefore, not considered further – the (component) name is treated as an atomic string describing a tissue, while the lineage relationship is modelled externally to the name sets.Taking a closer look at these "basic tissue terms", called Theoretically, merging two part-of hierarchies can be accomplished by systematically (top-down) adding each tissue from one hierarchy into another, i.e. conceptually the problem can be reduced to iteratively adding "group nodes" as discussed above.part-of relationships specified. Instead, a much more flexible solution is offered without having to sacrifice the interoperability across multiple data sets annotated with these anatomical concepts.The approach discussed in this paper will not work where there has been no agreement on the basic component terms, and as such is different from already existing work on merging autonomous ontologies. This raises two questions: what is the basis on which these terms should be agreed and what benefits are to be obtained from the proposed solution if such agreement has to be reached before these partonomic hierarchies can be merged. With respect to the first question, if a basic term, for example skin, exists, then it must be possible to dissect the mouse to a level that separates all the corresponding tissue from the rest of the mouse tissues, e.g. separate all skin tissue from the rest of the mouse. Other examples of basic terms are, therefore, head, skeleton, limb and forelimb. At this point scientists are then free to use combinations of these terms (for the positive and negative name sets) to describe the anatomical concepts they are interested in, e.g. {head, skin} to refer to the skin of the head. The different anatomy hierarchies created by different scientists can then be automatically merged using the approach proposed in this paper. Hence, to answer the second question from above, the benefit of our solution lies in the removal of the need for multiple scientists to agree on a single anatomy partonomy where all tissue concepts are defined and their Essentially, the solution is based on the transitivity property of the structural part-of relationship. As such, one could imagine implementations other than the one based on name sets to achieve the same result. The basic idea, however, would be the same. Using the name set concept makes the solution more directly accessible to biologists, who are more familiar with naming anatomical concepts than using computer generated IDs. We believe that the same approach may be applicable in other ontology areas, which have similarly transitive relationships, but since we have not tested this idea, we shall not elaborate on it in this paper.part-of relationships. Some preliminary studies suggest that where there are different types and these types are organised in an is-a hierarchy, the proposed integration mechanism will still work at the level of the common part-of type. For example, let H1 be a part-of hierarchy based on part-of-type-1, and let H2 be a part-of hierarchy based on part-of-type-2. If both, part-of-type-1 and part-of-type-2, are specialised versions of the more general part-of-type-0, i.e. part-of-type-1 is-a part-of-type-0 and part-of-type-2 is-a part-of-type-0, then we can use the proposed approach to integrate H1 and H2. The integrated hierarchy, however, would only support part-of-type-0 semantics. Our work in this area is still in its early phase and beyond the scope of this paper. Further details will be reported elsewhere.Also, the work described here only deals with the integration of hierarchies that are based on the same type of The work presented in this paper has focused on the issue of integrating different partonomic hierarchies in one species, mouse. We note that a similar approach may be useful when trying to integrate partonomic hierarchies across different organisms. This is subject of current research work, however, and will be reported on separately.part-of. For any given organism, however, there is more than one way to divide it into parts and subparts, thus leading to more than one valid partonomic hierarchy. To be able to interoperate between bioinformatics resources that make use of these anatomy ontologies, the corresponding hierarchies must be reconciled in some way. The paper addresses the problem that unique identifying names for tissues often reflect the partonomic hierarchies in which they are used. Although these names are in fact ordered sets (the order implying a particular hierarchy) of "component names", the order in these sets is not necessary to uniquely identify any tissue. Also, the sets of components in names can be used to derive all part-of relationships in the hierarchy. Based on these observations, we have developed a name set representation which facilitates integration of different partonomic hierarchies. Although this does not eliminate the requirement to agree on a set of suitable basic tissue terms and their meaning, it does remove the need to standardise the partonomic hierarchies. The proposed approach has been tested for the anatomy ontology of the Edinburgh Mouse Atlas. A Prolog prototype was implemented for evaluation purposes.Anatomy ontologies play an important role in bio-medical informatics. One of the key relationships modelled in such ontologies is that of 1Tj is a direct subpart of Ti, if Tj is part of Ti and there is no other tissue Tk such that Tj is part of Tk and Tk is part of Ti. If such a tissue Tk exists, Tj is an indirect subpart of Ti.AB developed the name set representation, implemented the prototype and carried out parts of the evaluation. YY provided input with respect to the current implementation of EMAP and EMAGE in relation to the proposed name set representation. DD carried out part of the evaluation process. RB contributed to the development of the name set representation. DD and RB are overall project leaders of EMAP and EMAGE. All authors have contributed to the writing and/or revision of the paper.


For decades, scientists accepted that the nucleic acids, DNA and RNA, packed with thousands of protein-coding genes, were the sole purveyors of genetic information; all inherited traits, from eye color to shoe size, must be stored and expressed through nucleic acid mechanisms. But prions are an exception. These misshapen proteins are capable of growing, replicating, and infecting other cells—that is, they are heritable. And all without a scrap of DNA. Most famous as the culprit behind bovine spongiform encephalopathy, or mad cow disease, prions also occur naturally in some organisms and may play important roles in their growth and development.Prion-forming proteins normally exist as benign cellular components, such as enzymes or receptors. But they possess the innate ability to alter their three-dimensional structure, or fold, which changes their function and makes them almost impossible to destroy. Like other misfolded proteins, such as those responsible for Alzheimer's and Huntington's diseases, prions pack together and form aggregates. But what distinguishes prions from simple protein aggregates is their exponential growth and amplification, which allows them to infect new host cells. Prions grow by inducing normal proteins to alter their shape and adhere to an initial aggregate “seed.” These growing masses are then thought to divide with the help of “chaperones,” cellular proteins that aid in protein folding and transport, resulting in smaller prion particles called propagons. The propagons are then distributed to both mother and daughter cells during division, thereby infecting the next generation of cells. Though this theory of the prion life cycle was proposed a few years ago, scientists are still working out the underlying molecular mechanismsAs they report in this issue, Lev Osherovich and colleagues dissected yeast prions and found that growth and heritability are controlled by two independent and “portable” sequences. Furthermore, the heritability element seems to be the only thing that keeps slow growing protein aggregates from becoming infectious prions. Previous research showed that one end of the yeast protein, Sup35p, is critical for turning this normal housekeeping enzyme into a prion. The “prion-forming domain” of Sup35p consists of two segments: one stretch rich in the amino acids glutamine and asparagine and another made up of several, small series of amino acids, called oligopeptides. Osherovich and colleagues had earlier found another yeast protein, New1p, which had similar segments, though in reverse order.To study the function of these sequences, the team constructed several strains of yeast, each with a small part of the prion-forming domain missing. By watching the behavior of these modified proteins, each fused to a green fluorescent protein for easy observation, the authors could infer the roles of the deleted segment.For both Sup35p and New1p, the authors found that the area rich in glutamine and asparagine was responsible for the aggregation and growth of prions—acting like a patch of Velcro that locks the misshapen proteins together. While this had been suggested by previous research, the authors also found that this sticky sequence only adheres to proteins that mirror its own pattern of amino acids, thereby explaining why prions from different species don't often interact, a phenomenon called the species barrier. The stretch of oligopeptide repeats in Sup35p and New1p, however, was required for the inheritance of prions—the proper division of prion masses and subsequent distribution of propagons during cell division. The authors suggest that oligopeptide repeats function as a secure binding location for the chaperone proteins, which are necessary for heritability, and thus infectiousness, of prions. Their results also help to explain why stable inheritance of prions is rare; while many proteins have stretches of amino acids similar to the described aggregation sequence, few also contain sequences like oligopeptide repeats that permit inheritance.Though both the aggregation sequence and the oligopeptide repeats are required for prion growth and infection, the segments seemed to function completely separately, allowing the authors to create a synthetic prion-forming domain by combining the aggregation element of New1p with the Sup35p replication/heritability element. This artificial prion acted like New1p, again showing that it is the sticky, aggregation element that specifies which proteins will be added to the growing prion mass. Osherovich and colleagues then went on to create another artificial prion by fusing the oligopeptide repeats to an expanded polyglutamine tract, the type of aggregation sequence responsible for the toxic buildup of brain proteins in Huntington's disease. With this simple addition, the slow growing aggregate was transformed into a heritable, infectious prion.By creating artificial hybrid prions, Osherovich and colleagues showed that the two discrete elements of prion-forming domains are portable and work together regardless of their origins. The authors suggest that other artificial prions could be used as a model system to study different types of aggregation sequences, such as those found in the human prion protein responsible for Creutzfeldt-Jakob's disease or the misshapen plaques of proteins that contribute to Alzheimer's disease.


Although various hematologic abnormalities are seen in tuberculosis, immune thrombocytopenic purpura is a rare event.We report a case of a 29 year-old male who was presented with immune thrombocytopenia-induced hemoptysis, macroscopic hematuria and generalized petechiae. The patient was found to have clinical, microbiological and radiological evidence of active pulmonary tuberculosis. The immune thrombocytopenic purpura was successfully treated with anti-tuberculous drugs combined with corticosteroids and high dose immune globulin therapy.Immune thrombocytopenic purpura can be one of the hematological manifestations of tuberculosis which has a global prevalence with increasing incidence secondary to HIV infection. During the past 2 decades, tuberculosis -both pulmonary and extrapulmonary- has re-emerged as a major health problem worldwide. Hematologic abnormalities have been described in association with mycobacterial infections for almost 100 years. Patients with both pulmonary and extrapulmonary tuberculosis (TB) may demonstrate peripheral blood abnormalities and findings may be minimal or profound [9/l (58% neutrophils, 29% bants, 9% lymphocytes and 4% monocytes), hemoglobin 11.2 gr/dl, hematocrit 36%, MCV 84 fl, reticulocytes 1% and platelet count 7.6 × 109/l. Erythrocyte sedimentation rate was 110 mm/h. A peripheral smear was remarkable for a paucity of platelets. Coagulation profile [prothrombin time (PT), activated-partial thromboplastin time (aPTT), fibrin degradation products (FDP)] were normal. A bone marrow aspiration demonstrated hypercellularity of all cell lines with normal maturation of myeloid and erythroid precursors. Megakaryocytes were increased in number with normal morphology. On bone marrow aspiration hemophagocytosis was not observed. A chest X-ray  units throughout hospitalization. During his hospitalization findings of hemolysis or gastrointestinal bleeding and massive bleeding in another site except hematuria and hemoptysis were not established. A complete blood count at discharge demonstrated a WBC 17 × 109/l, Hb 9.6 g/dl, and platelet count 310 × 109/l -hemolytic uremic syndrome (HUS), hemophagocytic syndrome and disseminated intravascular coagulation (DIC) associated with TB. Idiopathic thrombocytopenic purpura  is an acquired disease of children and adults defined as isolated thrombocytopenia with no clinically apparent associated conditions or other causes of thrombocytopenia. Adult ITP typically has an insidious onset with long-lasting histories of purpura (thrombocytopenia for >6 months) and spontaneous remission is uncommon and is likely to be incomplete -13. Ster9/l and he had no side effect thought to be secondary to anti-tuberculous drugs [Several factors are known to cause bleeding in association with infections, of which thrombocytopenia is the most common. The etiology of thrombocytopenia in most cases appears to be increased destruction of platelets such as due to DIC or septicemia without evidence of DIC or platelet adherence to damaged vascular surfaces or direct platelet toxicity caused by the microorganism or involvement of bone marrow. Adult acute immune thrombocytopenic purpura is defined as a bleeding disorder in otherwise healthy person caused by transient destruction of platelets. Although the most important therapy for infection-related thrombocytopenia is that directed at the underlying infection, treatment decisions for immune thrombocytopenic purpura remain controversial and may include single or combination therapy with corticosteroids, intravenous immunoglobulin (IVIg) according to degree of thrombocytopenia or hemorrhage ,14. The us drugs ,16. The In conclusion, since the incidence of tuberculosis is currently increasing in worldwide countries and it may present with different hematologic manifestations, in case of immune thrombocytopenic purpura tuberculosis should also be recalled. Finally, further studies are needed in order to fully characterize the pathophysiology and immunological abnormalities in tuberculosis-related immune thrombocytopenic purpura.


Streptococcus pneumoniae is a common bacterium that is present in the nasopharynx of many children and some adults, where it causes no harm to its carrier but can be transmitted to others. If it moves beyond the nasopharynx, however, it can cause ear infections or invasive disease, such as pneumonia or meningitis. Invasive disease from this organism occurs especially in children, the elderly, and individuals with weakened immune systems.The protective effect of antibodies against bacterial pneumonia has been appreciated since the 1930s, when it was shown that serum therapy—the transfer of serum from an immunized animal to a patient with acute disease caused by the same bacterial strain—could reduce mortality from pneumococcal pneumonia by half. Subsequent development of vaccines based on the bacterium's polysaccharide capsule, which could protect against infection, confirmed that an endogenous antibody response can provide protection against invasive disease.One challenge for vaccine development has been the existence of many different serotypes (the same species of bacteria but with different composition of the polysaccharide capsule). As protection usually doesn't extend to different serotypes, vaccination with capsule components from different serotypes is necessary to ensure broad protection. Such vaccines have been shown to be efficient and safe. They are now recommended in many countries for infants and toddlers, and for people over 65—the two age groups in which invasive disease is most common—and for others who are at increased risk of pneumococcal disease (e.g., patients with heart, kidney, liver, or lung disease, or who have had a splenectomy).Even without vaccination, however, most exposed individuals will never get invasive disease. Instead, they develop natural immunity against the different serotypes, though this immunity gradually declines with old age. Marc Lipsitch and colleagues wanted to understand the immunological basis of this natural immunity, and specifically whether it was due to anticapsular antibodies.If protection from invasive disease is due to acquiring anticapsular antibodies against each of the pneumococcal serotypes, they argued, this would lead to two predictions about the age distribution of disease caused by the different serotypes in the non-vaccinated population. First, for serotypes that are more common and therefore encountered earlier in life, children should develop immunity more quickly, causing disease from these types to drop off earlier in life than disease from the less common types. Second, protection against invasive disease from a particular serotype should coincide with the acquisition of antibodies against that serotype, on both the individual and population level.Neither prediction was borne out by the actual data the researchers analyzed, suggesting that there is more to natural immunity against pneumococcal disease than just anticapsular antibodies. The study doesn't demonstrate what the additional components are, but additional research might not just teach us about our immune system but also provide clues for further vaccine development. As the authors say, “A better understanding of the mechanisms that underlie natural immunity to pneumococcus could pave the way for the development of more effective, species-specific pneumococcal vaccines.”


Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed.The atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures.This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis. After the development of serological tests for hepatitis A and B viruses in the 1970s it became clear that an additional agent accounted for approximately 90% of transfusion-associated hepatitis (non-A non-B hepatitis, NANBH) [Flavivirideae family. Diagnostic tests for anti-HCV antibodies developed thereafter proved that HCV was indeed the predominant cause of NANBH [The novel agent, hence termed hepatitis C virus (HCV), currently infects approximately 3% of the world's population and it was classified within the of NANBH . In viewRational drug design has not been the primary way for discovering major therapeutics. However, recent successes in the area give reason to expect that drug discovery projects will increasingly be structure based. One of the possible targets for drug development against HCV is the NS3 protease variants. HCV RNA is translated into a polyprotein that during maturation is cleaved into functional components. One component, nonstructural protein 3 (NS3), is a 631-residue bifunctional enzyme with protease and helicase activities.The N-terminal portion of the NS3 protein was predicted to contain a serine protease domain as judged from conserved sequence patterns and by homology to Flavi- and Pestiviruses -6. The NGenome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms, including humans. We are now faced with assigning, understanding and modifying the functions of proteins encoded by these genomes. This task is generally facilitated by 3D structures , which aComparative modeling uses experimentally determined protein structures to predict conformation of other proteins with similar amino acid sequences. For modeling of proteins was used restrained-based modeling implemented in the program MODELLER . The modWe modeled the structure of variants of NS3 protease variants available in the National Center for Biotchnology Information (Genbank), using structural bioinformatics tools. Knowledge of the three-dimensional structure variants will undoubtedly aid the design of useful inhibitors that may be used as a drug against hepatitis C virus. In order to speed up the modeling process all NS3 models were generated in a Beowulf cluster (BioComp, S.J. Rio Preto, Brazil). The potential of the structural bioinformatics for development of new antiviral drugs is discussed.The identity between the sequences of a bifunctional protease structure (PDB access codes:1CU1, 1DY9) ,38  was used to generate homology models for docking studies. Binding of an inhibitor to the active site of an enzyme is typically connected with local and possibly also global structural rearrangement of the enzyme (induced-fit mechanism). Therefore structure-based drug design preferentially relies on the crystal structures of enzyme-inhibitor complexes containing bound inhibitors of similar chemical structures to the compounds being designed. Such complexes offer more detailed and accurate picture of the inhibitor-enzyme interactions and structural complementarity between the inhibitor and the active site. The homology models of the variants of NS3 protease which used the NS3 complexed with an inhibitor are more adequate to docking simulations. The atomic coordinates of all water molecules were removed from the templates.The analysis of the Ramachandran diagram φ-ψ plots of the 1CU1 structure (template) were used to compare the overall stereochemical quality of the NS3 protease variants structures against template solved by biocrystallography  was developed to archive protein structure identified in infectious agents such as NS3 protease variants from hepatitis C virus. All supporting data related to the 3D structures modeling, such as protein codes, atomic coordinates in PDB format from modeled proteins, fasta sequence, links to others databases and various information about the protein were arranged in the MySQL  databaseLarge scale protein homology modeling, in which whole sequence databases or whole genomes are used as input into automated modeling algorithms, have been reported by several groups . By utilInhibition studies have shown that NS3 is only modestly inactivated by classic serine protease inhibitors such as chloromethylketones or phenylmethyl sylfonylfluoride . The strMolecular modeling is usually the method of choice when there is a clear relationship of homology between the sequence of a target protein and at least one known structure. This computational technique is based on the assumption that tertiary structures of two proteins will be similar if their sequences are related, and it is the approach most likely to give accurate results . There aNext, the spatial restraints and CHARMM energy terms enforcing proper stereochemistry  were com1-χ2, χ-1, χ-3, χ-4 and ω values for each of the 20 amino acid types) and covalent geometry (for the main-chain, bond lengths and bond angles) these average values were calculated using PROCHECK [The overall stereochemical quality of the final models for each NS3 protease variants from hepatitis C virus was acessed by the program PROCHECK . The rooPROCHECK . The VerPROCHECK -30.NJFS carried out the molecular modeling and participated in the building and design of the database tables, HAA and CEB carried of analysis of the models and created database interface, FPS selected the primary sequences of NS3 protease variants of the hepatitis C virus, IMVGCM participated of the study, PR conceived the study and suggestions in this manuscript, JRRP participated in the study and suggestions in this manuscript, WFA conceived the study, and participated in its analysis and coordination. All authors read and approved the final manuscript.


Several tumors can exist as multiple lesions within a tissue. The lesions may either arise independently, or they may be monoclonal. The importance of multiple lesions for tumor staging, progression, and treatment is subject to debate. Here we use mathematical models to analyze the emergence of multiple, clonally related lesions within a single tissue. We refer to them as multi-focal cancers. We find that multifocal cancers can arise through a dynamical interplay between tumor promoting and inhibiting factors. This requires that tumor promoters act locally, while tumor inhibitors act over a longer range. An example of such factors may be angiogenesis promoters and inhibitors. The model further suggests that multifocal cancers represent an intermediate stage in cancer progression as the tumor evolves away from inhibition and towards promotion. Different patterns of progression can be distinguished: (i) If tumor inhibition is strong, the initial growth occurs as a unifocal and self contained lesion; progression occurs through bifurcation of the lesion and this gives rise to multiple lesions. As the tumor continues to evolve and pushes the balance between inhibition and promotion further towards promotion, the multiple lesions eventually give rise to a single large mass which can invade the entire tissue. (ii) If tumor inhibition is weaker upon initiation, growth can occur as a single lesion without the occurrence of multiple lesions, until the entire tissue is invaded. The model suggests that the sum of the tumor sizes across all lesions is the best characteristic which correlates with the stage and metastatic potential of the tumor. The occurrence of multiple lesions is observed in a variety of cancers. That is, not one, but several lesions are observed within a given tissue. Multiple lesions can occur by two basic mechanisms -5. EitheIn this paper we focus on multiple lesions with a monoclonal origin. We will refer to them as "multifocal" cancers. The mechanism by which such multifocal cancers are generated, and their relation to the stage and metastatic potential of the cancer, are not fully understood . Yet, thWe start with a simple model which describes tumor growth in relation to the production of promoters and inhibitors. We then extend this model to describe the local spread of cancer cells across space (tissue), and examine somatic evolution of cells away from tumor inhibition and towards promotion.C; promoters, P; and inhibitors, I. It is assumed that both promoters and inhibitors can be produced by cancer cells. In addition, inhibitors may be produced by healthy tissue. The model is given by the following set of differential equations which describe cancer growth as a function of time,We consider a basic mathematical model which describes the growth of a cancer cell population, assuming that the amount of blood supply influences the rate of cell division. The model includes three variables: the population of cancer cells, r. Growth is assumed to be density dependent and saturates if the population of cancer cells becomes large (expressed in the parameter ε). In addition, the growth rate of the cancer cells depends on the balance between promoters and inhibitors, expressed as P/(I + 1). The higher the level of promoters relative to inhibitors, the faster the growth rate of the cancer cell population. If the level of promoters is zero, or the balance between promoters and inhibitors in heavily in favor of inhibitors, the cancer cells cannot grow and remain dormant [δ. Promoters are produced by cancer cells at a rate ap and decay at a rate bp. Inhibitors are produced by cancer cells at a rate aI and decay at a rate bI. In addition, the model allows for production of inhibitors by normal tissue at a rate ξ.The equations are based on a previous study . The pop dormant -26. Canci) The cancer cells cannot grow and consequently go extinct.That is, C = 0, P = 0 and I = 0. The cancer goes extinct in the model because we only consider cells which require the presence of promoters for division. If the level of promoters is not sufficient, the rate of cell death is larger than the rate of cell division. In reality, however, it is possible that a small population of non-angiogenic tumor cells survives. Here, we omit this for simplicity. (ii) The population of cancer cells grows to significant levels, that is, C = .The analysis of the model above is presented in detail in the Materials and Methods section. It suggests the following patterns. There are two outcomes. (r, lies above a threshold relative to the death rate of the cells, δ, and degree of tumor cell inhibition (ap and bp relative to aI and bI, i.e. the production and decay rates of promoters and inhibitors, respectively). The exact condition is given by (9). In this case, the outcome is either failure of cancer growth, or successful growth to large numbers. Which outcome is achieved depends on the initial conditions. Successful growth is only observed if the initial number of cancer cells lies above a threshold. Then, enough promoters are initially produced to overcome inhibition. This provides an important barrier to the successful growth of cancers. It could explain why it is difficult for cancers to escape angiogenesis inhibition, and why autopsies often reveal the existence of multiple small, non-pathogenic tumors which have failed to progress [How do the parameter values influence the outcome of cancer growth? The cancer extinction outcome is always stable. The reason is as follows. The cancer cells require promoters to grow. The promoters, however, are produced by the cancer cells themselves. If we start with a relatively low initial number of cancer cells, this small population cannot produce enough promoters to overcome the presence of inhibitors. Consequently, the cancer fails to grow and goes extinct. This outcome is always a possibility, regardless of the parameter values. Significant cancer growth can be observed if the intrinsic growth rate, progress .In this section, we introduce space into the above described model. We consider a one-dimensional space along which tumor cells can migrate. The model is formulated as a set of partial differential equations and is written as follows,Dc. Inhibitors can also diffuse across space, and this is described by the diffusion coefficient DI. It is generally thought that inhibitors act over a longer range, while promoters act locally [ξ. As before, numerical simulations indicate that results are not changed qualitatively by this simplification. As mentioned above, the model considers tumor spread across space. It is important to point out that we do not consider long-range metastatic spread. Instead, we consider local spread of a tumor within a tissue, such as the breast, liver, brain, or esophagus.The model assumes that tumor cells can migrate, and this is described by the diffusion coefficient  locally ,24. TherHere we investigate the process of tumor growth and progression in relation to the degree of inhibition and promotion. A mathematical analysis is presented in the Materials and Methods section. Here we present biological insights and results of numerical simulations.aI/bI >>ap/bp). This corresponds to the early stages when the tumor is generated. We then investigate how tumor growth changes as the degree of inhibition is reduced relative to the level of promotion . We consider the following parameter regions . Only a small number of cells which do not require promotion for survival would remain.uni-focal). Note that this does not mean that it is in principle impossible to generate more lesions. It means that the space between lesions is bigger than the space provided for cancer growth within the tissue.2. If the degree of inhibition is weaker, the cancer cells can grow. The spread across space is, however, self-limited Figure . The can3. As the production of inhibitors is further reduced, we enter another parameter region. Now fewer inhibitors diffuse across space. We observe that multiple lesions or foci are formed Figure . They ar4. If the degree of inhibition is further reduced and lies below a threshold, spread of inhibitors is sufficiently diminished such that the tumor cells can invade the entire space and tissue Figure . In biolIn summary, as the relative degree of inhibition is reduced, the patterns of tumor growth change from absence of significant growth, to a single self-limited tumor, to the occurrence of multiple foci, and to the maximal invasion of the tissue by tumor cells. Multi-focal cancers may arise through the dynamical interplay between long range inhibition and local promotion. The following section will examine this in the light of somatic evolution.We have shown how the pattern of cancer growth can depend on the relative balance of promoters and inhibitors. Here we consider these results in the context of somatic evolution, and suggest some clinical implications.At early stages of cancer progression, the balance between inhibitors and promoters is in favor of inhibition. Inhibitors are likely to be produced by healthy cells (e.g. in the context of angiogenesis), and they are more abundant than an initiating population of transformed cells. In the context of angiogenesis, specific mutations have been shown to result in the enhanced production of promoters or reduced production of inhibitors in cancer cells. Our model has shown that such mutants have to be produced at a relatively high frequency, so that a sufficient number of promoting cells are present in order to ensure that enough promoters are produced to overcome the effect of inhibition.Once the promoting cells have succeeded to expand, cancer progression can occur in a variety of ways according to the model. How the cancer progresses depends on how much the balance between promotion and inhibition has been shifted in favor of promotion. We distinguish between three possibilities Figures , 3 &4.(i) The balance between inhibition and promotion has been shifted only slightly in favor of promotion, such that self-limited growth of the cancer is observed Figure . That is(ii) The first mutation shifts the balance between promoters and inhibitors to a lager extent which is sufficient to result in the generation of multiple lesions Figure . The mul(iii) Finally, assume that the initial mutation shifts the balance so much in favor of promotion that maximal invasion of the tissue is possible Figure . Now we In summary, the model predicts different modes of cancer progression in relation to the evolution away from tumor inhibition and towards promotion. A single cancer lesion may spread across the tissue without the occurrence of multiple lesions. Alternatively, the cancer can first grow as a single, self-contained lesion. This can then bifurcate to give rise to multiple foci, either as a result of additional mutations, or as a result of the natural pathway by which multiple foci are generated, depending on the degree of tumor promotion conferred by the initial mutation. Further evolutionary events can then induce the multiple foci to become a single, maximally invasive mass. The occurrence of multiple foci therefore represents an intermediate stage in tumor progression towards malignancy.The models discussed here show that multiple foci with a monoclonal origin can develop through a dynamical interplay between tumor promoters and inhibitors. The cancer can only grow to high loads as a single mass if it has largely escaped all inhibitory effects. Otherwise, the cancer is likely to grow via the generation of a relatively small and self limited tumor which then bifurcates into multiple foci until it finally invades the entire tissue. The occurrence of multiple foci is therefore an intermediate stage in cancer progression. The higher the number of foci, the further advanced the stage of cancer progression.A clinically important step in carcinogenesis is the process of metastasis. That is, the spread of tumor cells to the lymph node, entry into the blood supply, and the spread to other tissues. Various studies have investigated the metastatic potential of multi-focal compared to uni-focal cancers ,28,29. IFurther, it is important to note that studies which aim to assess the correlation between multi-focality and metastatic potential should not only concentrate on the number of foci, but also on the size of the foci. As we have shown with the model, cancer progression might start with a small single lesion which can be considered uni-focal. It can then bifurcate to give rise to multiple foci, and finally spread through the entire tissue. When such spread occurs, the multiple foci turn into a big and single mass, and this would again be considered uni-focal. Hence, the cumulative size or volume of the tumor is likely to be the best predictor of malignant progression.In conclusion, we suggest that the balance between tumor promoting and inhibiting factors might be an important driving force which determines the pattern on cancer progression, and can account for the occurrence of multi-focal cancers. The best worked out example of such promoter-inhibitor dynamics is angiogenesis. In this context, inhibitors are produced both by healthy tissue cells and by tumor cells. During the course of progression, tumor cells can mutate and evolve to produce less inhibitors and more promoters. The initial establishment of an angiogenic cell line is the most difficult step. Since the promoting factors are produced by angiogenic cells themselves, their initial abundance has to be sufficiently high, such that the balance can be shifted away from inhibition. This enables the population of cancer cells to expand beyond a very small size. This growth can then give rise to a self-limited uni-focal cancer which can bifurcate to give rise to multi-focal cancers. Further evolutionary events can finally lead to maximal tissue invasion. If the initial mutation allows the cells to sufficiently escape from inhibition, cancer progression can occur as a single expanding mass without the occurrence of multi-focality. These arguments not only apply to angiogenesis, but to any tumor promoting and inhibiting factors where inhibitors act over a long range while promoters act locally. Therefore, the therapeutic use of inhibitors should be further explored. This is an active area of research in the context of angiogenesis , and theHere we present mathematical methodology used to analyze the equations described in the text.P = CaP/bP). It is convenient to denoteHere we discuss a linear stability analysis of system (1–3). Let us first simplify the problem by using a quasistationary approach, that is, we will assume that the level of promoters adjusts instantaneously to its steady-state value 2 - 4εγ > 0, then there are exactly three positive equilibria in the system. If either of these conditions is violated, the (0,0) solution is the only  stable point.It is obvious that if Stability analysis can be performed by the usual methods. For the (0,0) equilibrium, the Jacobian isC±, I±), we get the following Jacobian,that is, this equilibrium is always stable. For the points  we have eigenvaluesand for the solution (Y ± ≡ 2bIW + δ(ε - γ - W ± Γ). We can see that solution  is always unstable and we will not consider it any longer. Solution , which we call for simplicity  from now on, is stable as long aswhere Y+ > 0     (9)P with C defined by , we can rewrite equation (4) asHere we present a linear analysis of system (4–6). As before, we are going to assume that promoters adjust instantaneously to their equilibrium level. By replacing This equation together with equation (6) gives a Turing model.) is a stable equilibrium. Of course, this solution also satisfies the system of PDEs, (10,6). Let us consider a wave-like deviation from this spatially uniform solution:Let us go back to the system of ODEs, (7–8), and assume that solution  can be written asλ2 + λ(bI - α + (DC + DI)ω2) + aIβ - (bI + DIω2)(α - DCω2) = 0.     (12)λ, with the spatial frequency of the perturbation, ω. The stability conditions now are given byThis is the dispersion relation which connects the growth-rate, bI - α + (DC + DI)ω2 > 0,     (13)aIβ - (bI + DIω2)(α - DCω2) > 0.     (14)) of the system of ODEs, (7–8), are obtained automatically from the conditions above by setting ω = 0:Note that the stability conditions for solution (bI - α > 0,     (15)aIβ - bI α > 0.     (16)Inequality (13) is always satisfied because of inequality (15). Let us derive conditions under which the spatially uniform solution is unstable. This requires that condition (14) is reversed. This can be expressed as follows:F(ω) ≡ DIDCω4 - ω2γ1 + γ2 < 0.     (17)where we denoted for simplicity,γ1 = αDI - bIDC, γ2 = αIβ - αbI > 0.ω = 0, with a positive leading term. The points, ±|ω|, satisfyingThis is a fourth order polynomial, symmetrical with respect to the line ω, ±ωc. The condition F(ωc) < 0 defines that the uniform solution  is unstable.correspond to the two minima of the left hand side of inequality (17). Let us call these values of F(ω) for different values of aI, see Figure aI, F(ω) is strictly positive, and the spatially uniform solution is stable. As aI increases, the function F(ω) crosses the line F = 0. The critical value of aI, aI,c, for which F(ωc) = 0, is determined fromLet us plot the function αDI - bIDC)2 = 4DIDC(aIβ - αbI), has only very narrow regions of ω corresponding to negative values. More precisely, Δω ~ L-1, where L is the spatial dimension of the system. In terms of parameter aI, we require that it is sufficiently close to aI,c. Then, we can calculate the "most unstable" wavenumber, that is, ωc defined by equation (18), with aI,c. This value will determine the spatial period of the solution,The applicability of the above analysis depends on the parameters of the system. First of all, we need conditions (15–16) to be satisfied. They mean that without diffusion, a positive, spatially uniform solution is stable. Next, we need to be in a In numerical simulations described in this paper, we used the following (Neumann) boundary conditions:aI is below the critical, aI <aI,c. The system exhibits bistability. If we start in the vicinity of a (0,0) solution, then cancer will not grow and decay to zero. If we start from a point (C, I) in the domain of attraction of the solution , then the system will develop towards this positive spatially homogeneous stationary solution.The simulation results are presented in the main body of the paper. Here we discuss the behavior of the system in the light of the analysis presented above. Let us assume that the value aI >aI,c, but make sure that it is sufficiently close to aI,c . Again, if the initial conditions are close to the zero solution, then the zero state will be the state that the system will attain. However, if we start in the vicinity of the  state, we will observe interesting behavior. Solution  is now unstable, and we will see "ripples" developing on top of this solution. This is Turing instability. The spatial period of the ripple was calculated in the previous section. Long-time evolution of this state is of course not in the realm of linear stability analysis, but we can predict that the spatial scale of the resulting solution will be given by (19).Next, let us suppose we have aI is much higher than critical. Now, solution  is unstable even in the system of ODEs. However, a periodic solution will develop, unless the initial condition is in the domain of attraction of the zero solution. The spatial scale of the periodic solution is determined intrinsically by the parameters of the system, and it grows with aI. Intuitively this is easy to understand, because higher values of aI correspond to higher levels of inhibition, so the distance between regions of large C will become larger. Note that the exact period of the periodic solution is adjusted to fit the boundary conditions of the system. For instance, with the Neumann boundary conditions, the boundary points are forced to be troughs of the wave-like pattern. In other words, the period of the solution must be an integer fraction of L.Finally, let us assume that


New York Times bestseller list and winning accolades from the likes of Jimmy Carter. A wide range of inherited disorders falls under the rubric of muscular dystrophy, but all involve some form of progressive muscle wasting. Stepanek's condition impaired nearly all of his body's functions, but other more common forms, including Emery-Dreifuss muscular dystrophy (EDMD), selectively target skeletal muscle and induce cardiac abnormalities.In June, muscular dystrophy patients lost one of their most passionate advocates to a rare form of this degenerative neuromuscular disorder—thirteen-year-old Mattie Stepanek. In his short life, Stepanek wrote five volumes of inspirational poetry, topping the EDMD is caused by mutations in either of two genes: one encodes lamin A, a structural protein associated with the nucleus, and the other encodes a nuclear membrane protein called emerin. Lamins, a major component of the structural network that supports the nuclear envelope, help the nuclear envelope maintain structural integrity and absorb mechanical stress without rupturing.  Emerin binds to proteins that regulate gene transcription. Emerin and lamins are found in most cell types, yet EDMD attacks only skeletal muscles, major tendons, and the cells that regulate cardiac muscle contraction. So where does this tissue specificity come from?One theory suggests that emerin selectively targets proteins that specifically regulate gene expression in EDMD-affected tissues. Another theory proposes that emerin provides structural support to the nuclear envelope and that emerin mutations are most destructive in tissues subjected to mechanical stress—like skeletal muscle and tendons. Current evidence supports both models. Recent studies suggest that emerin forms complexes with actin—the mother of all structural proteins. Actin proteins can join together (polymerize) to form a variety of filaments. However, given longstanding doubts that actin exists in the nucleus, let alone functions there, researchers were unsure what the findings might indicate. Now James Holaska, Amy Kowalski, and Katherine Wilson propose that emerin not only functions as a structural protein in the nucleus but that it does so by interacting with actin.Evidence that emerin and lamin A can form multiprotein complexes comes primarily from experiments in test tubes. To get a sense of the physiological significance of these findings, Wilson and colleagues purified emerin-binding proteins from the nuclei of living cells. They found that emerin binds to polymerized actin and, in fact, appears to stimulate polymerization. By binding and “capping” a specific end of the actin filament, emerin prevents filament de-polymerization (disassembly), effectively increasing the rate of actin polymerization by four- to twelve-fold. The authors propose that emerin “promotes the formation of a nuclear actin cortical network,” which could serve to anchor membrane proteins and lamin filaments to the inner nuclear membrane and thus enhance the structural integrity of the nuclear envelope. Whether emerin also interconnects the lamin and actin filament networks at the nuclear envelope—which could significantly reinforce its mechanical strength—will have to await further study.Muscle contraction places enormous stress on cell membranes. These results suggest that actin-based networks, in addition to lamin networks, support the structural integrity of the nuclear envelope. Defects in proteins involved in either network could compromise nuclear structure, which could in turn disrupt the cell's gene expression program, for example, or rupture the cell membrane, killing the cell. Subtle defects in proteins important for muscle cell integrity can cause several forms of muscular dystrophy. Now it appears that emerin defects could cause EDMD in part by compromising the mechanical integrity of nuclei in muscle cells and tendons.


In the mouse zygote the paternal genome undergoes dramatic structural and epigenetic changes. Chromosomes are decondensed, protamines replaced by histones and DNA is rapidly and actively demethylated. The epigenetic asymmetry between parental genomes remains at least until the 2-cell stage suggesting functional differences between paternal and maternal genomes during early cleavage stages.Here we analyzed the timing of histone deposition on the paternal pronucleus and the dynamics of histone H3 methylation (H3/K4mono-, H3/K4tri- and H3/K9di-methylation) immediately after fertilization. Whereas maternal chromatin maintains all types of histone H3 methylation throughout the zygotic development, paternal chromosomes acquire new and unmodified histones shortly after fertilization. In the following hours we observe a gradual increase in H3/K4mono-methylation whereas H3/K4tri-methylation is not present before latest pronuclear stages. Histone H3/K9di-methylation is completely absent from the paternal pronucleus, including metaphase chromosomes of the first mitotic stage.Parallel to the epigenetic asymmetry in DNA methylation, chromatin modifications are also different between both parental genomes in the very first hours post fertilization. Whereas methylation at H3/K4 gradually becomes similar between both genomes, H3/K9 methylation remains asymmetric. It is now generally accepted that the properties of a particular DNA sequence in cells are not solely defined by the nucleotide sequence itself, but by "epigenetic" modifications as well. Epigenetic modifications imply the methylation of cytosine residues in CpG dinucleotides and covalent modifications of core histones. These modifications allow for flexible, but heritable at the same time, reprogramming of the genome.In histone H3 five lysine residues can be methylated (K4, K9, K27, K36 and K79) . MethylaThe fertilization of mouse egg causes dramatic changes in organization of both paternal and maternal genomes. Initially arrested in metaphase II oocyte completes the meiosis, forming haploid maternal pronucleus and extruding the second polar body. The densely packed with protamines sperm DNA decondences, protamines get exchanged by histones and DNA undergoes active demethylation. The demethylation in the early mouse zygote occurs asymmetrically on paternal DNA and affects different classes of repetitive and single copy sequences, but not the control regions of imprinted genes ,8. PreviHere we examine time dependent changes of chromatin structure in the mouse zygote, focusing on the dynamics of the acquisition of histones in the paternal pronucleus and methylation status of histone H3 at positions K4 and K9.in vitro fertilization of mature mouse oocytes. Histones and methylated H3/K4 and H3/K9 were detected by using indirect immunofluorescence. In our experiments we used antibodies, which specifically recognize mono- or tri-methylated forms of H3/K4, and di-methylated H3/K9. The zygotes were analyzed after 3, 5, 8, 10, 12 and 18 hours incubation of mature oocytes with capacitated sperm from donor males. After 18 hours most of embryos were found to be at metaphase and some already at telophase stage of the first mitotic division. Even using in vitro fertilization, the obtained zygotes are not completely synchronous in their development and it is more appropriate to use PN stages classification, which is based on the morphological changes of both pronuclei [In order to obtain mouse zygotes at different stages of development and to provide the precise timing for fertilization we used 0/early PN1 stages (approx. 3–5 hours p.f.), exactly when the global demethylation starts [We performed the immunostaining against core histones (anti-PanHistone antibodies) in all the stages tested in combination with antibodies, recognizing the specific methylated forms of histone H3. This served as a positive control for the immunostaining procedure and allowed us to follow the dynamics of histone acquisition in the paternal pronucleus. Histones were first detected shortly after the penetration of sperm into the oocyte and the beginning of the decondensation of sperm chromatin. According to PN stages classification we could clearly detect histones on paternal pronucleus at late PNn starts  Fig. 1)0/early P1 (approx. 5 hours p.f.) stage  and the difference in antibodies staining intensity between paternal and maternal pronuclei becomes indistinguishable in the last pronuclear stage PN5 (approx 12 hours p.f.)  Fig.  and in mf.) Fig. . The facet al. suggest that H3/K9 methylation does not depend on DNA methylation [et al. [1 stage, i.e. at a time when we first observe the appearance of H3/K4mono-methylation  procedures were performed as described in [2 incubator for 3, 5, or 8 hours. For longer incubation time the oocytes were incubated with sperm in HTF medium for 8 hours and then transferred into the drop of pre-gasses and pre-warmed M16 medium and incubated further for 2, 4 or 10 hours.As sperm and oocytes donors we used (C57BL/6 X CBA)Focedures . Sperm iribed in . BrieflyAfter the removal of zona pellucida by treatment with Acidic Tyrode's solution fertilized oocytes were fixed for 20 min in 3.7% paraformaldehyde in PBS, and permeabilized with 0.2% Triton X-100 in PBS for 10 min at room temperature. The fixed zygotes were blocked overnight at 4°C in 1% BSA, 0.1% Triton X-100 in PBS. After blocking the embryos were incubated in the same solution with either anti PanHistones , anti mono-methyl H3/K4 , anti tri-methyl H3/K4  or anti di-methyl H3/K9 , and anti-rabbit secondary antibodies coupled with Rhodamine Red-X (Jackson ImmunoResearch Laboratories Inc.). After final washes the zygotes were placed on slides and mounted with a small drop of Vectashield (VectorLab) mounting medium containing 0.5 μg 4,6-diamino-2-phenylindole (DAPI). At least 20 zygotes have been analyzed for each stage of zygotic development.The slides were analyzed on Zeiss Axiovert 200 M inverted microscope equipped with the fluorescence module and B/W digital camera for imaging. The images were captured, pseudocoloured and merged using AxioVision software (Zeiss).KL conducted the experimental part of the work and co-wrote the manuscript. JW coordinated the study and co-wrote the manuscript. All authors read and approved the final manuscript.


On the Origin of Species—the remains were initially described as either those of a “brutish” race or of someone disfigured by disease. As Darwinian evolution caught on, so did the realization that these fossils were evidence of an earlier human species. Scientists have been debating Neanderthal's place in human evolution ever since.After miners unearthed a skull and bones in a Neander Valley cave in Germany in 1856—three years before the publication of An ongoing question concerns the possibility that Neanderthals and early humans mated, since they likely crossed paths during thousands of years of European cohabitation. In a new study, Mathias Currat and Laurent Excoffier present a simulation model based on what we know about the population density and distribution of Neanderthals and Cro-Magnons. Their results complement recent genetic and morphological evidence indicating that early human and Neanderthal interbreeding was unlikely.The notion that modern Europeans directly descended from Neanderthals has mostly yielded to two competing models: One postulates that modern humans arose in Africa about 130,000 years ago and completely replaced coexisting archaic forms with no interbreeding, while the other proposes a gradual transition with interbreeding.Though mounting genetic evidence  suggests Neanderthals and early humans did not breed, the evidence has been inconclusive. It's possible, for example, that any Neanderthal gene “leakage” could have been lost through genetic drift if the mating populations were small. And because so few fossils are available to analyze, previous studies could rule out only Neanderthal contributions over 25%.Currat and Excoffier's model refines various parameters—such as geographic boundaries, local population variations, range expansion, and competition for resources—based on archeological and demographic data for both populations. Evidence suggests modern humans replaced Neanderthals over 12,500 years, for example, which constrains the speed at which modern humans could expand.The authors started with a scenario based on a set of “plausible” parameter values—their basic scenario—and then varied the local interbreeding rate and, for example, the population size and location of Cro-Magnons, to produce eight alternate scenarios describing how Cro-Magnon colonization of Europe might have proceeded. They estimated the likely proportion of Neanderthal gene contributions to the modern gene pool using “coalescent simulations,” which generate the genealogies and diversity of genes in local populations based on simulations of their population densities and migration histories. The simulations show that if Neanderthals bred with Cro-Magnons without constraints over thousands of years, Neanderthal contributions to the modern gene pool “would be immense.” Surprisingly, the simulations also show that even a very small mixing should lead to high levels of Neanderthal DNA in modern humans.What could account for this counterintuitive result? Given a low population density with small local breeding populations, any introduction of Neanderthal genes would decrease the frequency of Cro-Magnon genes of that population; if these Neanderthal integrations take place as the Cro-Magnon population is expanding, newly acquired Neanderthal genes would also be amplifiedSince no Neanderthal mitochondrial DNA has been found in modern-day Europeans, the authors modeled the maximum number of interbreeding events that would support this observation. The estimated maximum number of events, it turns out, falls between 34 and 120—extremely low values, Currat and Excoffier conclude, “given the fact that the two populations must have coexisted for more than 12,000 years.”While the authors acknowledge their simulations suggest rather than reflect reality, their model does incorporate real historical data such as Cro-Magnon expansion over time and local population growth. At a value of only 0.1%, their new estimate of the rate of interbreeding is about 400 times lower than previous estimates and provides strong support that Neanderthals and Cro-Magnon didn't interbreed and may even have been different species.


There are already 40 cities in Africa with over 1 million inhabitants and the United Nations Environmental Programme estimates that by 2025 over 800 million people will live in urban areas. Recognizing that malaria control can improve the health of the vulnerable and remove a major obstacle to their economic development, the Malaria Knowledge Programme of the Liverpool School of Tropical Medicine and the Systemwide Initiative on Malaria and Agriculture convened a multi-sectoral technical consultation on urban malaria in Pretoria, South Africa from 2nd to 4th December, 2004. The aim of the meeting was to identify strategies for the assessment and control of urban malaria. This commentary reflects the discussions held during the meeting and aims to inform researchers and policy makers of the potential for containing and reversing the emerging problem of urban malaria. Anopheles mosquito breeding decreases with increasing proximity to the centre of urban areas [Africa's population will almost triple by the year 2050. This expansion will occur primarily in urban areas and by 2025, 800 million people will live in urban communities. Especially affected will be West Africa, where the urban population annual growth rate of 6.3% is more than twice the rate of the total population growth. Today in the humid forest zone, more people live in cities than in rural areas and in twenty years time, two out of three West Africans will live in urban centres. While many of Africa's health problems are common to both urban and rural environments, recognizing and meeting the public health challenges in these growing cities is becoming increasingly urgent. Malaria has been considered a predominantly rural disease in Africa, primarily because suitable vector breeding sites are scarce in highly populated areas. Yet, although studies have shown that an areas -3, transan areas ,5.nd-4th December 2004 to develop an evidence-based approach for evaluating and controlling urban malaria. Participants were drawn from seven sub-Saharan countries, Europe, North America and South Asia . Recognizing the need for extensive cross-sectoral involvement and collaboration in dealing with the challenge of urban malaria, representatives from the research/ academic, NGO, development, policy-making and donor communities co-operated in the process to identify key knowledge gaps and opportunities for control. Included in the group were sociologists, clinical epidemiologists, entomologists and control specialists.The Malaria Knowledge Programme of the Liverpool School of Tropical Medicine and the International Water Management Institute/ Systemwide Initiative on Malaria and Agriculture convened a meeting in Pretoria 2Urbanization is a recent phenomenon in Africa: in 1960 there were no African cities with one million inhabitants, today there are forty. Has malaria become a serious problem within these huge cities and their peri-urban environs? Data presented from studies in a number of sub-Saharan African cities  showed clearly that malaria is a considerable urban health problem in Africa. The studies demonstrated great heterogeneities in malariometric indices both between and within cities. It was recognized that not only the major cities of Africa, but also many medium sized regional towns, home to a large proportion of the Africa population, have considerable levels of malaria . With maMisdiagnosis of malaria is a serious problem everywhere, but in areas of low malaria endemicity presumptive treatment of all fevers as malaria can result in over 75% of cases being misdiagnosed as malaria [Effective provision of appropriate treatment also remains a serious challenge in urban settings. The Abuja Declaration stated that by 2005 "At least 60% of those suffering from malaria have prompt access to and are able to use correct, affordable and appropriate treatment within 24 hours of onset of symptoms." Despite the fact that access to quality health care is better on average in urban compared to rural zones, the formal public health facilities are often the last source of treatment used along the pathway to cure. Often malaria care initially involves leftover medicines from the home , the purchase of cheaper herbal medicines or unprescribed conventional medicines. The problems of obtaining treatment from a health facility may be exacerbated by the need to obtain permission from an authority figure, absence from work and loss of income, the need to raise money to fund both the treatment and associated costs such as travel . With HoThe highly focal nature of urban malaria requires targeting of interventions to specific urban districts, and therefore, requires detailed information on each area in advance. However, relationships between administrative boundaries, environment and population distribution are complex in urban areas, which makes them difficult to sample and characterize in a representative way. Strategies for population-representative sampling must incorporate a range of environments and populations to identify accurately environmental and other risk factors. This may be further complicated as urban populations can be highly mobile and in peri-urban areas there may be a high rate of turnover in groups of lower socio-economic status. Presentations from South, East and West Africa clearly demonstrated that Geographic Information Systems (GIS)-based approaches are valuable tools for assessing heterogeneities in risk factors for urban malaria, and for subsequent implementation and monitoring of interventions.Experienced researchers believe that the urban environment has advantages for the effective delivery of appropriate interventions. A number of studies have demonstrated that higher rates of coverage with insecticide-treated bednets can be achieved in urban areas ,11, althLarval control, achieved either by source reduction or larviciding, can be community directed and may be feasible in certain settings as part of a comprehensive, integrated vector management strategy. There is optimism in some communities about its efficacy and the results of further research into the costs and benefits of such interventions are awaited with interest. Environmental modifications may also be feasible if partners from the community and outside the health sector are engaged. Work from Sri Lanka has demonstrated how a very effective scheme to control malaria by modification of irrigation structures was accepted by the agricultural community because of the financial and water savings that the scheme introduced . HoweverThe conclusions of the meeting have been summarised in the Pretoria Statement on Urban Malaria Figure . While iAll authors participated as session chairs in the technical workshop and were instrumental in producing the summary conclusions. All authors read and approved the final manuscript.List of attendees and affiliationsClick here for file


The same molecular qualities that endowed DNA with its capacity to carry hereditary information make it a powerful tool to explore the self-assembly of complex nanostructures How did life emerge from a soup of chemicals? How do patterns such as schools of fish form from individuals? How do voting patterns emerge? Such a diverse array of problems seems completely unrelated. However, they all involve “emergence of complexity.” When individuals come together, they form patterns, structures, and organizations that cannot be discerned from the individuals alone. The study of the emergence of complexity is one of the most active and important areas of research. It is important not only for understanding nature, but also for technological applications, including the fabrication of large-scale integrated nanocircuits using a bottom-up approach, and the preparation of multifunctional “smart” nanomaterials.DNA evolved to be the primary carrier of genetic information because of its extraordinary chemical properties. These same properties also make DNA an excellent system for the study of self-assembly and self-organization. Two complementary molecules of single-stranded DNA have paired bases that bond with each other and form the well-known double helix structure. Two molecules of double-stranded DNA (duplexes) can further associate together if they have complementary single-stranded overhangs (sticky ends). Intermolecular interactions can be precisely predicted by Watson–Crick basepairing (adenine to thymidine and guanine to cytosine). And, these interactions are structurally understood at the atomic level. Given the diversity of the DNA sequences, we can easily engineer a large number of pairs of DNA duplexes that associate with each other with sequence specificity and in a well-defined fashion. This property is not common among other molecular systems. Small organic and inorganic molecular pairs can interact with each other with specificity and in well-defined structures, but the number of such pairs is limited and their chemistry varies greatly. Protein molecules, such as antibody–antigen pairs, have great diversity and high specificity. However, it is extremely difficult, if not impossible, to predict how proteins interact with each other. In contrast, DNA as a molecular system fulfills all the aforementioned criteria.In nature, DNA occurs predominantly as a linear molecule, and if its conformations were limited to linearity, it would not be very useful for studying self-assembly. Fortunately, branched DNA structures can be engineered. Holliday junctions, for example, are intermediates that occur during genetic recombination. To model Holliday junctions, a stable four-arm junction has been constructed in which, by design, no two strands are fully complementary to each other  Figure A. For exExtensive studies have shown that the four-arm junction adopts an X-shape structure B under pOne simple example of self-assembly is the formation of two-dimensional (2D) periodic arrays or 2D crystals. This is also one of the greatest successes in the field of DNA self-assembly . The firi molecule takes the input from the Xi and Yi−1 molecules. In other words, the values of the Xi and Yi−1 molecules determine what Y molecule will be incorporated. There are four different types of Y molecules, whose inputs are (1, 1), (1, 0), (0, 1), and (0, 0). These four, and only four, Y molecules are enough to satisfy any input combination. Two C molecules connect the input and output molecules, which is necessary for the characterization but not essential for the self-assembly process. Sticky ends between the X molecules and the C molecules are longer than those between Y molecules and between Y and X or C molecules. Thus, the C and X molecules assemble first to form the inputs because the association between longer sticky ends is more stable than those between the shorter ones. Then the output Y molecules assemble to the assembled C and X molecules. In that study (Inspired by early theoretical suggestions , experimat study , two difPLoS Biology have extended the algorithmic self-assembly strategy from 1D to 2D (Winfree and co-workers in this issue of 1D to 2D . This ac1D to 2D . ExternaIn principle, a wide range of 2D patterns could be generated with the same set of molecules and the same strategy, changing only the first row of the assembly, which specifies the external inputs. Realization of this goal will critically rely on the elimination of assembly errors, or the introduction of error corrections .The current work represents a neat approach to understanding the emergence of complexity. It integrates both simulation and wet chemistry. It also provides a plausible approach to nanofabrications. Over the last decade, a variety of methods have been developed, which use biomacromolecules as templates to fabricate nanostructures . Limited