Electrophoresis gel analysis: Gelanalyzer software

The analysis of the electrophoresis gel  admits sometimes rigorous analysis to extract the information displayed (quantitative PCR, SDS page ...). There are several software able to performing this task as Gelanalyzer (http://www.gelanalyzer.com/download.html). This is a directly executable software, very easy to handle that analyzes three models of correlations between the intensities of spots and their surfaces,depending on the selected marker.

Adapted from   http://www.gelanalyzer.com

Macherki M E

Prédiction de profil de restriction//Macherki M E

La digestion enzymatique d’un fragment d’ADN par des enzymes de restriction est une manipulation très rependu dans les analyses génétique. Comme tout procédé d’analyse, il y avait un but et des contraintes :

+Un résultat parfait qui répond à la question dans ce cas : quel est le nombre des bondes à trouver avec cette digestion ?

-Quel sera le résultat sachant qu’on utilise un gel d’agarose 1%(les bonde visualisées sont comprises entre 50 et 1000 paires des bases) ?

C’est question est typique disant académique pour apprendre la manipulation des séquences avec un logiciel. Nous proposons l’algorithme suivant élaborer avec le langage R  juste avec une fonction de trois lignes.

library("seqinr")

  mon_fonction_digestion<-function(Sequence,Marqueur){

  position<-unlist(gregexpr(Marqueur,c2s(Sequence))) ###en majuscule

  stripchart(diff(position)[diff(position)<1000& diff(position)>100],vertical = T,ylim=c(50,1050))

    }

Avec  Sequence: un vecteur contenant la séquence à analyser.

         Marqueur: la séquence palindromique (mode char)de l'enzyme de restriction

Exemple

se<-ec999[[1]] # un exemple de séquence

mr<-"ata"## un exemple de marquer

mon_fonction_digestion(se,mr)

Macherki M E:R course and applications in genomic 2014

Application de filtre linaire dans l’analyse de génome//Macherki M E

L’application de filtre linaire pour de série temporelle est très applicable. Dans le cas des acides nucléiques, il est très simple d’appliqué un tel genre d’analyse en utilisant un logicielle simple d’analyse tel que R. Le vecteur contenant les positions d’un oligo dans une séquence est la base de l’analyse en appliquant la fonction diff qui permettre de déterminer alors la distance entre deux oligo consécutif soit disant un dérivative de premier dégrée. En utilisant la fonction lm entre la somme cumulatif (position) de dérivatif et le vecteur normal, nous pouvons exprimer de  façon globale la fréquence de l’oligo étudié. Le graphique de résiduelle permet de schématiser la structure de chromosome avec détaille (origine et  terminus pour une bactérie par exemple E.coli K12 en utilisant l'oligo ‘GGG’  fig).

Il est simple de déterminer la valeur originale sur la séquence par juste une étape de retour en arrière. L’intérêt de filtre linéaire est plus clair si en va effectuer un alignement des séquences. Notant que la fréquence d’un oligo de la séquence à alignée est bien connu, en appliquant un retour en arrière    selon le coefficient de corrélation de séquence cible, nous pouvons  déterminer un intervalle ou la séquence sera exister. En utilisant plusieurs oligos (exemple pour n=5), nous somme capable de  prédire l’emplacement avec une exactitude parfaite.
Macherki M E:R course and applications in genomic 2014

In yeast genetics

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“Genetics is, therefore, one of a trio of methods, the others being molecular biology/biochemistry and cell biology, which are required to understand the function of individual genes in vivo”

                         Susan L. Forsburg

Yeast geneticists half-mockingly talk about the cult of APYG: the ‘awesome power of yeast genetics’. But mocking aside, these simple, single-celled fungi have proven themselves to be the workhorses of cell biology because of the ease of their genetic manipulation. The budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe are quite different in their biology (FIG. 1), but they share a similar tool set that makes the process of gene discovery, and subsequent characterization of gene function, remarkably easy.

 Historically, S. cerevisiae has been the more popular experimental system. The first eukaryote to be transformed by plasmids, it was also the first eukaryote for which precise gene knockouts were constructed, and the first to have its genome sequenced1–3. The cell biological issues that have been explored in this ASCOMYCETE range from signal transduction to cell-cycle control, chromosome structure to secretion. The identified genes have been used as probes to uncover further pathways and to identify metazoan homologues. Despite the completion of its genome sequence several years ago1 , the roles of many of its 6,000+ genes remain unclear. The process of mutant analysis and discovery of gene function continues with the added tools of genomics4,5. By contrast, the experimental history of S. pombe involves a smaller, but growing, community. Its genome sequence is essentially complete but shares no conserved synteny (gene order) with the budding yeast in its 4,900+ genes6 . Although yeast phylogeny is still unclear, S. pombe is thus quite distinct from S. cerevisiae and filamentous fungi7–9. The fission yeast has a symmetrical pattern of cell division and has been particularly popular for studies of cell growth and division, and chromosome dynamics. Other cell biological questions have been addressed more recently, inspired by the power of a comparative approach between these two superficially similar organisms. Each of them offers unique insights as a model organism for elucidating the biology of more complex cell types10. Both yeasts are adaptable to several forms of genetic analysis that allow the identification of new genes, or the functional analysis of previously identified genes11,12. Because both can grow and divide as haploids, recessive mutations are easily recovered. However, a diploid sexual cycle exists for both, allowing facile genetic analysis, including tests of COMPLEMENTATION, RECOMBINATION and EPISTASIS. The identification of replication origin sequences allows plasmids to be maintained as free episomes, and these are easily introduced into the cell by transformation. In addition, both species have high rates of homologous recombination, allowing precise manipulation of the genome for the construction of gene disruptions and allele-specific replacements. Their nomenclature is distinct, but they benefit from similar genetic and molecular tools, which provide the basic requirements for the genetic screens described below. With the genome sequences of both species now completed, the challenge is to identify the functions associated with their genes. The classical genetic analysis described here complements the newest genomic approaches13: genetics moves from a function, defined by mutation, to identify the gene responsible, whereas genomics moves from the catalogue of genes to identify their function. The true power of genetics is its predictive value; genetic interactions predict physical interactions, and these can be tested using standard molecular and biochemical techniques. Genetics is, therefore, one of a trio of methods, the others being molecular biology/biochemistry and cell biology, which are required to understand the function of individual genes in vivo.

1. Goffeau, A. et al. Life with 6000 genes. Science 274,

546–567 (1996).

2. Beggs, J. D. Transformation of yeast by a replicating

hybrid plasmid. Nature 275, 104–109 (1978).

3. Rothstein, R. One step gene disruption in yeast. Methods

Enzymol. 101, 202–211 (1983).

4. Oliver, S. G. From gene to screen with yeast. Curr. Opin.

Genet. Dev. 7, 405–409 (1997).

5. Oliver, S. G., Winson, M. K., Kell, D. B. & Banganz, F.

Systematic functional analysis of the yeast genome.

Trends Biotechnol. 16, 373–378 (1998).

6. Sipiczki, M. Phylogenesis of fission yeasts —

contradictions surrounding the origin of a century old

genus. Antonie Van Leeuwenhoek 68, 119–149 (1995).

7. Paquin, B. et al. The fungal mitochondrial genome project:

evolution of fungal mitochondrial genomes and their gene

expression. Curr. Genet. 31, 380–395 (1997).

8. Berbee, M. L. & Taylor, J. W. Dating the evolutionary

radiations of the true fungi. Can. J. Bot. 71, 1114–1127

(1993).

9. Keogh, R. S., Seoighe, C. & Wolfe, K. H. Evolution of gene

order and chromosome number in Saccharomyces,

Kluyveromyces and related fungi. Yeast 14, 443–457

(1998).

10. Forsburg, S. L. The best yeast. Trends Genet. 15,

340–344 (1999).

Summarizes some differences in the biology of the

two yeast species.

11. Guthrie, C. & Fink, G. R. (eds) Guide to yeast genetics and

molecular biology. Methods Enzymol. 194, 1–863 (1991).

Describes more specific methods and protocols for

both yeast species.

12. Moreno, S., Klar, A. & Nurse, P. Molecular genetic analysis

of the fission yeast Schizosaccharomyces pombe.

Methods Enzymol. 194, 795–823 (1991).

13. Kumar, A. & Snyder, M. Emerging technologies in yeast

genomics. Nature Rev. Genet. 2, 302–312 (2001).

The genomics revolution complements the classical

genetics approach.

Adapted from: THE ART AND DESIGN OF GENETIC SCREENS; Susan L. Forsburg; YEAST, NATURE REVIEWS | GENETICS ; VOLUME 2 | SEPTEMBER 2001 | 659-668

Macherki M E

La descrimination de la longeur de CDS //Macherki M E

L’analyse de la phylogénie nous permettre de déterminer les relations entre les groupes d’individus qui sont discriminatoire et  différents. Dans ce contexte, il est commun d’utiliser l’ADN dans la comparaison étant qu’il représente le support de l’information génétique. Avec tout les critiques qu’il entoure, la taille de génome était toujours une grandeur de base dans cette analyse. Le pourcentage en GC reste le paramètre dépendant puis qu’il est stable au nivaux de génome. Autrement, nous avons essayé d’utiliser l’ADNc dans la comparaison et non l’ADN génomique pour bien cibler la partie transcrite et non hérédité. Nous avons utilisé une méthode de permutation de Monte Carlo (n=10000) pour déterminer le moyenne  de Log de longueur exprimé en paire de base (table I)

Macherki M E:R course and applications in genomic 2014

Tentative en biologie computationnelle//Macherki M E

L’étude  de l’ADN permet de révéler plusieurs informations permettant l’identification des différents compartiments dans le  génome et entre des génomes distincts. L’information la plus commun est la fréquence d’un oligo au sein d’une séquence. Par contre cet information est non explicatif .C’est juste un indice calculé qui vari énormément au sein de la séquence. En utilisant la méthode de "DNA walk", cette fréquence suit par conséquence une loi normale puisque cette fréquence change d’un emplacement à un autre dans le génome. Nous étudions la distance entre les oligo présent dans la séquence. Nous considérons alors que les bases symbolisent un enchaînement de pièces de dominos mis l’une après l’autre. Nous avons déterminé la loi de probabilité pour ce variable discret notamment géométrique  et une corrélation entre la fréquence et la distance. Pour des buts comparatifs, nous avons essayé de stabiliser nos statistique en utilisant la méthode de karlin S. et al.et comparer les séquences des génomique après une normalisation à des prédit issu de l’approximation géométrique.

En fin, nous avons essayé de comparer  la structure eucaryote (Chr X de l’homme) et celle de procaryote (E.coli k12)en utilisant notre approximation d’indépendance telle que l’indice de rho avec deux oligos à la fois . Nous avons signalé un effet d'usage des  codons  chez E.coli (aire segmentée dans la FIG) avec des zone sur exprimées et sous exprimées consécutives tel qu’il signaler ultérieurement avec karlin et al.

Dans le chromosome X, l’effet d'usage des  codons est omis et nous reportons que les effets d’abondance( aire bleu dans la FIG indique la répression GC). Cette remarque permet de nous conclure que notre tentative peut permettre de distinguer entre une zone transcrit et non transcrit se qui permet de distinguer l’ADN eucaryote de celle procaryote facilement.

Macherki M E:R course and applications in genomic 2014

Strategies using bacteria to target tumors

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The hypothesis that living bacteria may function as anticancer therapeutic agents was first advanced in the middle of the twentieth century. Due to the obstacles of hypoxia and necrosis, accessing tumor tissue with traditional treatments has proved difficult. However, bacteria may actively migrate away from the vasculature and penetrate deep into tumor tissue and accumulate (Fig. 1A). Three classes of anaerobic and facultative anaerobes have been examined for use inanticancer therapy (1,2): Bifidobacteria, facultative intracellular bacteria and strictly anaerobic bacteria. The ideal criteria for the selection of therapeutic bacteria (3,4) are as follows: Non-toxic to the host; selective for a specific type of tumor; has the ability to penetrate deeply into the tumor where ordinary treatment does not reach; non-immunogenic (does not trigger an immune response immediately but may be cleared by the host); harmless to normal tissue; able to be manipulated easily; and has a drug carrier that may be controlled. In addition to studies of bacteria designed to induce immune responses (5)and mediate antiangiogenesis therapy (6), a recent study has focused on the usage of bacterial products as anticancer agents (7). Three main strategies in bacterial cancer treatment are discussed : i) Bacteria as tumor markers; ii) Bacteria engineered to express anticancer agents (Fig. 1B); and iii) Bacteria for oncolytic therapy (Fig. 1C).

Bacteria as tumor markers

As replicating anaerobic bacteria are able to selectively target tumors, the use of these bacteria may be an innovative approach for locating tumors that is simple and direct, but practical and effective. Two types of non-bacterial material have served as tumor markers: Viral vectors, including adenovirus, adeno-associated virus, herpes simplex virus (HSV)-1, HSV amplicon, Sindbis, poliovirus replicon and lentivirus/Moloney murine leukemia virus; and non-viral vectors, such as therapeutic DNA, microRNA, short hairpin (sh)RNA, small interfering (si)RNA and oligodeoxynucleotides (ODNs) (14-16). However, anaerobic bacteria are preferable to these other two types of tumor marker due to increased mobility. Once the marker has been administered, a number of methods may be used to locate the tumor, including bioluminescence, fluorescence and magnetic resonance imaging (MRI), as well as positron emission tomography (17). Bacteria may be detected using light, MRI or positron emission tomography (18,19).

Bacteria engineered to express anticancer agents

 Bacteria exhibit the ability to manufacture and deliver specific materials; these can be artificially coupled to certain anticancer agents (Fig. 1B) (18). The most common current carriers employed ingene therapy are viral vectors, such as retrovirus, adenovirus, viral vaccines, herpes simplex virus and adeno-associated virus. Non-viral delivery systems have been gradually established with the development of technology; currently, the gene therapy field has evolved to encompass not only the delivery of therapeutic DNA, but also of microRNA, shRNA, siRNA and ODNs (4). However, non-viral gene delivery systems exhibit lower transfection potency, resulting in lowered ability to traverse the various obstacles encountered during treatment. Conversely, bacteria have great advantages in the drug carrier field. Two predominant mechanisms have been investigated: The direct expression of antitumor proteins and the transfer of eukaryotic expression vectors into infected cancer cells. In direct expression, four categories of anticancer therapies may be utilized: Proteins with physiological activity against tumors, cytotoxic agents, antiangiogenic agents or enzymes that convert the nonfunctional prodrug to an anticancer drug. In the transfer of eukaryotic expression vectors, gene-silencing shRNAs (20), cytokines and growth factors, and tumor antigens have been investigated (21). Furthermore, the number of useful agents is increasing due to new developments in combinatorial synthesis and the advent of metagenomics, which is an unlimited source of novel anticancer bacterial products. Bacterial oncolytic therapy. The employment of bacteria in oncolytic therapy is the initial treatment and most direct method to kill tumor cells. Clostridial spores are the main components in oncolytic therapy and have been thoroughly analyzed (6,22,23). Bacterial-based cancer therapies usingClostridium spores have the advantage of overcoming the obstacles of hypoxia and necrosis (24). Clostridium spp. are strictly anaerobic and only colonize areas devoid of oxygen; therefore, when Clostridium spp. are systematically injected into solid tumors, spores germinate and multiply in the hypoxic/necrotic regions. Parker et al were the first to demonstrate clostridial oncolysis and tumor regression in mouse tumors by injecting a Clostridium spore suspension into transplanted mouse sarcomas 25). However, during follow-up studies, spore treatment with wild-type Clostridium was not sufficient to eradicate solid tumors (2,8,9). Thus, genetic engineering and repetitive screens are required to enhance the tumor oncolytic capacity of Clostridium. M-55, which was isolated from a non-pathogenic Clostridium oncolyticum strain by Carey et al (10,11), broke this impasse. Since then, multitudinous recombinant Clostridium strains have been used in tumor treatment. Among these, C. histolyticium, C. tetani, C. oncolyticum, C. oncolyticum (sporogenes), C. beijer‑ inckii (acetobutylicum) and C. novyi‑NT have been the most commonly investigated (12,13).

References:

1. Bernardes N, Chakrabarty AM and Fialho AM: Engineering

of bacterial strains and their products for cancer therapy. Appl

Microbiol Biotechnol 97: 5189-5199, 2013.

2. Xu J, Liu XS, Zhou SF and Wei MQ: Combination of immunotherapy

with anaerobic bacteria for immunogene therapy of

solid tumours. Gene Ther Mol Biol 13: 36-52, 2009.

3. Inoue M, Mukai M, Hamanaka Y, et al: Targeting hypoxic

cancer cells with a protein prodrug is effective in experimental

malignant ascites. Int J Oncol 25: 713-720, 2004.

19. Forbes NS: Profile of a bacterial tumor killer. Nat Biotechnol 24:

1484-1485, 2006.

4. Schmidt-Wolf GD and Schmidt-Wolf IG: Non-viral and hybrid

vectors in human gene therapy: an update. Trends Mol Med 9:

67-72, 2003.

5. Cebra JJ: Influences of microbiota on intestinal immune system

development. Am J Clin Nutr 69: 1046S-1051S, 1999.

6. Gardlik R, Behuliak M, Palffy R, Celec P and Li C: Gene

therapy for cancer: bacteria-mediated anti-angiogenesis

therapy. Gene Ther 18: 425-431, 2011.

7. Jain KK: Use of bacteria as anticancer agents. Expert Opin Biol

Ther 1: 291-300, 2001.

8. Gericke D and Engelbart K: Oncolysis by Clostridia.

II. Experiments on a tumor spectrum with a variety of Clostridia

in combination with heavy metal. Cancer Res 24: 217-221, 1964.

9. Dietzel F and Gericke D: Intensification of the oncolysis

by Clostridia by means of radio-frequency hyperthermy

in experiments on animals-dependence on dosage and on

intervals (author's transl). Strahlentherapie 153: 263‑266, 1977

(In German).

10. Brown JM: Tumor hypoxia in cancer therapy. Methods

Enzymol 435: 297-321, 2007.

11. Brown JM and Wilson WR: Exploiting tumour hypoxia in cancer

treatment. Nat Rev Cancer 4: 437-447, 2004.

12. Wei MQ, Mengesha A, Good D and Anné J: Bacterial targeted

tumour therapy-dawn of a new era. Cancer Lett 259: 16-27, 2008.

13. Mengesha A, Dubois L, Paesmans K, et al: Clostridia in

anti-tumour therapy. In: Clostridia: Molecular Biology in the

Post-Genomic Era. Brüggemann H and Gottschalk G (Eds).

Caister Academic Press, Norfolk, UK, pp199-214, 2009.

14. Thomas CE, Ehrhardt A and Kay MA: Progress and problems

with the use of viral vectors for gene therapy. Nat Rev Genet 4:

346-358, 2003.

15. Ji SR, Liu C, Zhang B, et al: Carbon nanotubes in cancer

diagnosis and therapy. Biochim Biophys Acta 1806: 29-35,

2010.

16. McCrudden CM and McCarthy HO: Cancer gene therapy - key

biological concepts in the design of multifunctional non-viral delivery

systems. In: Gene Therapy - Tools and Potential Applications. Martin

Molina F (ed). InTech, Rijeka, pp213‑248, 2013.

17. Ptak C and Petronis A: Epigenetics and complex disease: from

etiology to new therapeutics. Annu Rev Pharmacol Toxicol 48:

257-276, 2008.

18. Forbes NS: Engineering the perfect (bacterial) cancer therapy.

Nat Rev Cancer 10: 785-794, 2010.

19. van der Meel R, Gallagher WM, Oliveira S, et al: Recent

advances in molecular imaging biomarkers in cancer: application

of bench to bedside technologies. Drug Discov Today 15:

102-114, 2010.

20. Xu DQ, Zhang L, Kopecko DJ, et al: Bacterial delivery of

siRNAs: a new approach to solid tumor therapy. In: siRNA and

miRNA Gene Silencing. Sioud M (ed). Springer, New York,

NY, pp1-27, 2009.

21. Patyar S, Joshi R, Byrav DS, et al: Review bacteria in cancer

therapy: a novel experimental strategy. J Biomed Sci 17: 21, 2010.

22. Barbé S, Van Mellaert L and Anné J: The use of clostridial

spores for cancer treatment. J Appl Microbiol 101: 571-578,

2006.

23. Minton NP, Mauchline ML, Lemmon MJ, et al: Chemotherapeutic

tumour targeting using clostridial spores. FEMS Microbiol

Rev 17: 357-364, 1995.

24. Umer B, Good D, Anné J, Duan W and Wei MQ: Clostridial

spores for cancer therapy: targeting solid tumour microenvironment.

J Toxicol 2012: 862764, 2012.

25. Parker RC, Plummer HC, et al: Effect of histolyticus infection

and toxin on transplantable mouse tumors. Proc Soc Exp Biol

Med 66: 461-467, 1947.

***Adapted from:LIU et al: TUMOR-TARGETING BACTERIAL THERAPY IN TREATMENT OF ORAL CANCERONCOLOGY LETTERS 8: 2359-2366, 2014

Macherki M E