What is the meaning and significance of extreme pathways

What is the meaning and significance of extreme pathways

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Can someone please explain me what extreme pathways are? I found this definition in this article:

Extreme pathways are a unique and minimal set of vectors that completely characterize the steady-state capabilities of genome-scale metabolic networks.

Now, frankly speaking, I did not understand the head or tail of this definition? What is steady state? What does it mean by steady state capabilities? Can someone please explain me in a simpler way? PS: I am a computer Science student

For any dynamic system defined by different components, the steady state is the state of the system in which the components remain constant over time. If you consider the example of growth (by cell division) the point at which the total number of cells remain constant would be the steady state. This is a point at which birth rate = death rate. Similarly for a multi-component system such as a gene and its activator, the steady state would be the point where the mRNAs and the proteins of the gene and its activator are constant. If only few components are constant then the system is said to be in a quasi-steady state.

A point to be noted that in chemical (or biochemical) systems steady state, as a term, is different from equilibrium (in physics these terms are used interchangeably). Equilibrium is a condition when, for a single reversible reaction, the forward rate = backward rate.

In mathematical terms steady state is a point where the rate of change of components = 0. In an ordinary differential equations based model:

$$frac{dar{X}}{dt}=0 $$ where $ar{X}$ is a vector in which each element is a component of the system. In case of the transcription example, $X(1),X(2)… $ would be mRNA, protein, activator mRNA and so on. In other words $dfrac{dX(i)}{dt}=0$ for all $i$.

Steady state capabilities should mean the properties of the system at its steady state.

The kind of study described in the question is called metabolic flux balance analysis in which different metabolic reactions are described in the form of linear equations represented by $Sv=0$ where $v$ is the vector of all fluxes (metabolic reaction rates) $S$ is the stoichiometry matrix (which has the stoichiometry of all components in different metabolic reactions). The RHS is zero because we are evaluating the system at its steady state i.e. we are interested in finding out the condition in which the net metabolic reaction flux is 0. In other words all metabolic reactions, balance each other.

You need to read more about this. There are a lot of books on this topic. You can start with this review.

In flux balance analysis, the linear equations are overdetermined i.e. there are many solutions. People generally use linear programming (simplex algorithm) to find optimal solutions. In the space of the optimal solutions, the vertices represent extreme reactions. The entire optimal solution space can be described as a linear combination of these extreme reactions.

Schematic representation of a convex cone characterized by five extreme pathways. Extreme Pathways 1-5 (EP1, EP2, EP3, EP4, and EP5) circumscribe the solution space for the three fluxes indicated (vA, vB, vC). EP4 lies in the plane formed by fluxes vA and vB. Consequently, flux vC does not participate in that extreme pathway. EP3, EP4, and EP5 are all close and represent different uses of a network to achieve a similar overall result. All points within the convex cone can be described as a non-negative linear combination of the extreme pathways [1].

Extreme pathway analysis reveals the organizing rules of metabolic regulation

Affiliations Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Computer Science and Technology, Fudan University, Shanghai, China, Shanghai Ji Ai Genetics & IVF Institute, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

Roles Conceptualization, Supervision, Validation, Writing – review & editing

Affiliations Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Computer Science and Technology, Fudan University, Shanghai, China


Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

These authors contributed equally to this work.

Author to whom correspondence should be addressed. E-mail: [email protected]

Absolute vs Relative Refractory Period

With the above information, it is now possible to understand the difference between the absolute refractory period and relative refractory period. In terms of an action potential, refractory periods prevent the overlapping of stimuli.

In theory, each action potential requires around one millisecond to be transmitted. This means we could expect a single axon to forward at least one thousand action potentials every second in reality, this number is much lower. The absolute refractory period lasts for approximately one millisecond the relative refractory period takes approximately two milliseconds.

Multiple action potentials do not occur in the same neuron at exactly the same time. This is because a neuron experiences two different situations in which it is either impossible or difficult to initiate a second action potential. These two situations describe the two types of refractory periods.

During the depolarization phase when Na + ion channels are open, no subsequent stimulus can create a further effect. An ion channel does not open by degrees – it is either open or closed. This is the absolute refractory period (ARP) of an action potential. A second action potential ‘absolutely’ cannot occur at this time. Only after the Na + ion channels in this part of the membrane have closed can they react to a second stimulus.

The relative refractory period (RRP) occurs during the hyperpolarization phase. The neuron membrane is more negatively-charged than when at resting state K + ion channels are only just starting to close. However, all sodium ion channels are closed so it is – in principle – possible to initiate a second action potential. This requires a stronger stimulus as the intracellular space is more negatively charged. To excite a neuron by reaching the threshold level of – 55 mV, a greater stimulus is required. It is, therefore, ‘relatively’ difficult but not impossible to start up a second action potential during the relative refractory period.

The relative refractory period is extremely important in terms of stimulus strength. The rate at which a neuron transmits action potentials decides how important that stimulus is. There is no such thing as a weak or strong action potential as all require the same level of electrical or chemical stimulus to occur. Either threshold level is achieved and the neuron fires, or it does not.

It is the firing rate not the firing strength that causes different effects. For example, in low light levels, cells in the retina of the eye transmit fewer action potentials than in the presence of bright light. We see much better when light levels are high because more information is passed from the retina to the brain in a short time.

IV. Controlling the Family-Wise Error Rate (FWER)

Definition The family-wise error rate (FWER) is the probability of at least one (1 or more) type I error

The Bonferroni Correction

The most intuitive way to control for the FWER is to make the significance level lower as the number of tests increase. Ensuring that the FWER is maintained at across independent tests

is achieved by setting the significance level to .

Fix the significance level at . Suppose that each independent test generates a p-value and define

Caveats, concerns, and objections

The Bonferroni correction is a very strict form of type I error control in the sense that it controls for the probability of even a single erroneous rejection of the null hypothesis (i.e. ). One practical argument against this form of correction is that it is overly conservative and impinges upon statistical power (Whitley 2002b).

Definition The statistical power of a test is the probability of rejecting a null hypothesis when the alternative is true

Indeed our discussion above would indicate that large-scale experiments are exploratory in nature and that we should be assured that testing errors are of minor consequence. We could accept more potential errors as a reasonable trade-off for identifying more significant genes. There are many other arguments made over the past few decades against using such control procedures, some of which border on the philosophical (Goodman 1998, Savitz 1995). Some even have gone as far as to call for the abandonment of correction procedures altogether (Rothman 1990). At least two arguments are relevant to the context of multiple testing involving large-scale experimental data.

1. The composite “universal” null hypothesis is irrelevant

The origin of the Bonferroni correction is predicated on the universal hypothesis that only purely random processes govern all the variability of all the observations in hand. The omnibus alternative hypothesis is that some associations are present in the data. Rejection of the null hypothesis amounts to a statement merely that at least one of the assumptions underlying the null hypothesis is invalid, however, it does not specify exactly what aspect.

Concretely, testing a multitude of genes for differential expression in treatment and control cells on a microarray could be grounds for Bonferroni correction. However, rejecting the composite null hypothesis that purely random processes governs expression of all genes represented on the array is not very interesting. Rather, researchers are more interested in which genes or subsets demonstrate these non-random expression patterns following treatment.

2. Penalty for peeking and ‘p hacking’

This argument boils down to the argument: Why should one independent test result impact the outcome of another?

Imagine a situation in which 20 tests are performed using the Bonferroni correction with and each one is deemed ‘significant’ with each having . For fun, we perform 80 more tests with the same p-value, but now none are significant since now our . This disturbing result is referred to as the ‘penalty for peeking’.

Alternatively, ‘p-hacking’ is the process of creatively organizing data sets in such a fashion such that the p-values remain below the significance threshold. For example, imagine we perform 100 tests and each results in a . A Bonferroni-adjusted significance level is meaning none of the latter results are deemed significant. Suppose that we break these 100 tests into 5 groups of 20 and publish each group separately. In this case the significance level is and in all cases the tests are significant.

What Is the Importance of Biology?

Biology is important because it allows people to understand the diversity of life forms and their conservation and exploitation. Through various biological disciplines, people obtain knowledge about life and living organisms, including the origin, growth, evolution, structure, distribution and function of these organisms.

Biology implies an essential responsibility for the welfare and protection of all living species. It studies all living beings and how organisms interact in the biosphere. This is essential because it enables people to know the behavior and functions of each population that interacts with individuals from other populations or communities. Biologists discover how the specific aspects of the biosphere affect and benefit from the behaviors of a particular population.

Biology also studies the origin of diseases and plagues, such as infections, pathologies of animals and damage to plants and trees. Biology encompasses the study of the functions of living beings, enhancement of useful species, factors that cause illnesses, discovery and production of medicines and sustainable use of natural resources. Through biotechnology, biologists find efficient ways to produce food and other supplies for people. They investigate the processes involved in producing various nutritional substances.

Furthermore, biologists investigate environmental factors surrounding living beings and seek effective methods to grasp the variations of the environment that threaten the existence of living organisms on Earth.

The Difference Between Apoplast and Symplast

Apoplast refers to the non protoplasmic components of a plant, including the cell wall and the intracellular spaces.

Symplast refers to the continuous arrangement of protoplasts of a plant, which are interconnected by plasmodesmata.

Apoplast consists of non protoplasmic parts such as cell wall and intracellular space.

Symplast Consists of protoplast

Apoplast composed of nonliving parts of a plant.

Symplast composed of living parts of a plant.

In apoplast, the water movement occurs by passive diffusion.

In symplast, the water movement occurs by osmosis.

In apoplast, the water movement is rapid.

In the symplast, the water movement is slower.

The metabolic rate of the cells in the root cortex does not affect the water movement.

The metabolic rate of the cells in the root cortex highly affects the water movement.

It shows less resistance to the water movement.

It shows some resistance to the water movement.

With the secondary growth of the root, most of the water moves by the apoplast route.

Beyond the cortex, water moves through the symplast route.

Similarities Between Apoplast and Symplast:

Apoplast and symplast are two ways in which the water moves from root hair cells to the xylem.

Both the apoplast and symplast occur in the root cortex.

Both the apoplast and symplast carry water and nutrients towards the xylem.

Pathways For Root Absorption Through Apoplast:

The apoplastic pathway provides a way towards the vascular cell through free spaces and cell walls of the epidermis and cortex. An additional apoplastic route that allows the direct access to the xylem and phloem is along the margins of the secondary roots. The secondary root is developed from the pericycle, a cell layer just inside the endodermis. The endodermis is characterized by the Casparian strip. Apoplast was previously defined as the whole thing but the symplast, consisting of cell walls and spaces between cells in which water and solutes can move freely.


Shanafelt TD, Hanson C, Dewald GW, Witzig TE, LaPlant B, Abrahamzon J et al. Karyotype evolution on fluorescent in situ hybridization analysis is associated with short survival in patients with chronic lymphocytic leukemia and is related to CD49d expression. J Clin Oncol 2008 26: e5–e6.

Dohner H, Stilgenbauer S, Benner A, Leupolt E, Krober A, Bullinger L et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000 343: 1910–1916.

Pettitt AR, Jackson R, Carruthers S, Dodd J, Dodd S, Oates M et al. Alemtuzumab in combination with methylprednisolone is a highly effective induction regimen for patients with chronic lymphocytic leukemia and deletion of TP53: Final results of the National Cancer Research Institute CLL206 Trial. J Clin Oncol 2012 30: 1647–1655.

Spaner DE . Oral high-dose glucocorticoids and ofatumumab in fludarabine-resistant chronic lymphocytic leukemia. Leukemia 2012 26: 1144–1145.

Stilgenbauer S, Zenz T, Winkler D, Buhler A, Schlenk RF, Groner S et al. Subcutaneous alemtuzumab in fludarabine-refractory chronic lymphocytic leukemia: clinical results and prognostic marker analyses from the CLL2H study of the German Chronic Lymphocytic Leukemia Study Group. J Clin Oncol 2009 27: 3994–4001.

Castro JE, James DF, Sandoval-Sus JD, Jain S, Bole J, Rassenti L et al. Rituximab in combination with high-dose methylprednisolone for the treatment of chronic lymphocytic leukemia. Leukemia 2009 23: 1779–1789.

Parikh SA, Keating MJ, O’Brien S, Wang X, Ferrajoli A, Faderl S et al. Frontline chemoimmunotherapy with fludarabine, cyclophosphamide, alemtuzumab, and rituximab for high-risk chronic lymphocytic leukemia. Blood 2011 118: 2062–2068.

Dreger P, Dohner H, Ritgen M, Bottcher S, Busch R, Dietrich S et al. Allogeneic stem cell transplantation provides durable disease control in poor-risk chronic lymphocytic leukemia: long-term clinical and MRD results of the German CLL Study Group CLL3X trial. Blood 2010 116: 2438–2447.

Wang L, Lawrence MS, Wan Y, Stojanov P, Sougnez C, Stevenson K et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N Engl J Med 2011 365: 2497–2506.

Quesada V, Conde L, Villamor N, Ordonez GR, Jares P, Bassaganyas L et al. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat Genet 2011 44: 47–52.

Zhang X, Reis M, Khoriaty R, Li Y, Ouillette P, Samayoa J et al. Sequence analysis of 515 kinase genes in chronic lymphocytic leukemia. Leukemia 2011 25: 1908–1910.

Ouillette P, Collins R, Shakhan S, Li J, Peres E, Kujawski L et al. Acquired genomic copy number aberrations and survival in chronic lymphocytic leukemia. Blood 2011 118: 3051–3061.

Gunnarsson R, Mansouri L, Isaksson A, Goransson H, Cahill N, Jansson M et al. Array-based genomic screening at diagnosis and during follow-up in chronic lymphocytic leukemia. Haematologica 2011 96: 1161–1169.

Gunnarsson R, Isaksson A, Mansouri M, Goransson H, Jansson M, Cahill N et al. Large but not small copy-number alterations correlate to high-risk genomic aberrations and survival in chronic lymphocytic leukemia: a high-resolution genomic screening of newly diagnosed patients. Leukemia 2010 24: 211–215.

Lehmann S, Ogawa S, Raynaud SD, Sanada M, Nannya Y, Ticchioni M et al. Molecular allelokaryotyping of early-stage, untreated chronic lymphocytic leukemia. Cancer 2008 112: 1296–1305.

Pfeifer D, Pantic M, Skatulla I, Rawluk J, Kreutz C, Martens UM et al. Genome-wide analysis of DNA copy number changes and LOH in CLL using high-density SNP arrays. Blood 2007 109: 1202–1210.

Brown JR, Hanna M, Tesar B, Werner L, Pochet N, Asara JM et al. Integrative genomic analysis implicates gain of PIK3CA at 3q26 and MYC at 8q24 in chronic lymphocytic leukemia. Clin Cancer Res 2012 18: 3791–3802.

Grubor V, Krasnitz A, Troge JE, Meth JL, Lakshmi B, Kendall JT et al. Novel genomic alterations and clonal evolution in chronic lymphocytic leukemia revealed by representational oligonucleotide microarray analysis (ROMA). Blood 2009 113: 1294–1303.

Kay NE, Eckel-Passow JE, Braggio E, Vanwier S, Shanafelt TD, Van Dyke DL et al. Progressive but previously untreated CLL patients with greater array CGH complexity exhibit a less durable response to chemoimmunotherapy. Cancer Genet Cytogenet 2010 203: 161–168.

Saddler C, Ouillette P, Kujawski L, Shangary S, Talpaz M, Kaminski M et al. Comprehensive biomarker and genomic analysis identifies p53 status as the major determinant of response to MDM2 inhibitors in chronic lymphocytic leukemia. Blood 2008 111: 1584–1593.

Ouillette P, Erba H, Kujawski L, Kaminski M, Shedden K, Malek SN . Integrated genomic profiling of chronic lymphocytic leukemia identifies subtypes of deletion 13q14. Cancer Res 2008 68: 1012–1021.

Kujawski L, Ouillette P, Erba H, Saddler C, Jakubowiak A, Kaminski M et al. Genomic complexity identifies patients with aggressive chronic lymphocytic leukemia. Blood 2008 112: 1993–2003.

Haferlach C, Dicker F, Schnittger S, Kern W, Haferlach T . Comprehensive genetic characterization of CLL: a study on 506 cases analysed with chromosome banding analysis, interphase FISH, IgV(H) status and immunophenotyping. Leukemia 2007 21: 2442–2451.

Ouillette P, Fossum S, Parkin B, Ding L, Bockenstedt P, Al-Zoubi A et al. Aggressive chronic lymphocytic leukemia with elevated genomic complexity is associated with multiple gene defects in the response to DNA double-strand breaks. Clin Cancer Res 2010 16: 835–847.

Britt-Compton B, Lin TT, Ahmed G, Weston V, Jones RE, Fegan C et al. Extreme telomere erosion in ATM-mutated and 11q-deleted CLL patients is independent of disease stage. Leukemia 2012 26: 826–830.

Augereau A, T’Kint de Roodenbeke C, Simonet T, Bauwens S, Horard B, Callanan M et al. Telomeric damage in early stage of chronic lymphocytic leukemia correlates with shelterin dysregulation. Blood 2011 118: 1316–1322.

Lin TT, Letsolo BT, Jones RE, Rowson J, Pratt G, Hewamana S et al. Telomere dysfunction and fusion during the progression of chronic lymphocytic leukemia: evidence for a telomere crisis. Blood 2010 116: 1899–1907.

Brugat T, Nguyen-Khac F, Grelier A, Merle-Beral H, Delic J . Telomere dysfunction-induced foci arise with the onset of telomeric deletions and complex chromosomal aberrations in resistant chronic lymphocytic leukemia cells. Blood 2010 116: 239–249.

Roos G, Krober A, Grabowski P, Kienle D, Buhler A, Dohner H et al. Short telomeres are associated with genetic complexity, high-risk genomic aberrations, and short survival in chronic lymphocytic leukemia. Blood 2008 111: 2246–2252.

Bullrich F, Veronese ML, Kitada S, Jurlander J, Caligiuri MA, Reed JC et al. Minimal region of loss at 13q14 in B-cell chronic lymphocytic leukemia. Blood 1996 88: 3109–3115.

Liu Y, Hermanson M, Grander D, Merup M, Wu X, Heyman M et al. 13q deletions in lymphoid malignancies. Blood 1995 86: 1911–1915.

Kalachikov S, Migliazza A, Cayanis E, Fracchiolla NS, Bonaldo MF, Lawton L et al. Cloning and gene mapping of the chromosome 13q14 region deleted in chronic lymphocytic leukemia. Genomics 1997 42: 369–377.

Kitamura E, Su G, Sossey-Alaoui K, Malaj E, Lewis J, Pan HQ et al. A transcription map of the minimally deleted region from 13q14 in B-cell chronic lymphocytic leukemia as defined by large scale sequencing of the 650 kb critical region. Oncogene 2000 19: 5772–5780.

Kapanadze B, Makeeva N, Corcoran M, Jareborg N, Hammarsund M, Baranova A et al. Comparative sequence analysis of a region on human chromosome 13q14, frequently deleted in B-cell chronic lymphocytic leukemia, and its homologous region on mouse chromosome 14. Genomics 2000 70: 327–334.

Migliazza A, Bosch F, Komatsu H, Cayanis E, Martinotti S, Toniato E et al. Nucleotide sequence, transcription map, and mutation analysis of the 13q14 chromosomal region deleted in B-cell chronic lymphocytic leukemia. Blood 2001 97: 2098–2104.

Mabuchi H, Fujii H, Calin G, Alder H, Negrini M, Rassenti L et al. Cloning and characterization of CLLD6, CLLD7, and CLLD8, novel candidate genes for leukemogenesis at chromosome 13q14, a region commonly deleted in B-cell chronic lymphocytic leukemia. Cancer Res 2001 61: 2870–2877.

Ouillette P, Collins R, Shakhan S, Li J, Li C, Shedden K et al. The prognostic significance of various 13q14 deletions in chronic lymphocytic leukemia. Clin Cancer Res 2011 17: 6778–6790.

Mosca L, Fabris S, Lionetti M, Todoerti K, Agnelli L, Morabito F et al. Integrative genomics analyses reveal molecularly distinct subgroups of B-cell chronic lymphocytic leukemia patients with 13q14 deletion. Clin Cancer Res 2010 16: 5641–5653.

Parker H, Rose-Zerilli MJ, Parker A, Chaplin T, Wade R, Gardiner A et al. 13q deletion anatomy and disease progression in patients with chronic lymphocytic leukemia. Leukemia 2011 25: 489–497.

Fazi C, Scarfo L, Pecciarini L, Cottini F, Dagklis A, Janus A et al. General population low-count CLL-like MBL persists over time without clinical progression, although carrying the same cytogenetic abnormalities of CLL. Blood 2011 118: 6618–6625.

Lanasa MC, Allgood SD, Slager SL, Dave SS, Love C, Marti GE et al. Immunophenotypic and gene expression analysis of monoclonal B-cell lymphocytosis shows biologic characteristics associated with good prognosis CLL. Leukemia 2011 25: 1459–1466.

Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E et al. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 2002 99: 15524–15529.

Sampath D, Liu C, Vasan K, Sulda M, Puduvalli VK, Wierda WG et al. Histone deacetylases mediate the silencing of miR-15a, miR-16 and miR-29b in chronic lymphocytic leukemia. Blood 2012 119: 1162–1172.

Klein U, Lia M, Crespo M, Siegel R, Shen Q, Mo T et al. The DLEU2/miR-15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia. Cancer cell 2010 17: 28–40.

Lia M, Carette A, Tang H, Shen Q, Mo T, Bhagat G et al. Functional dissection of the chromosome 13q14 tumor-suppressor locus using transgenic mouse lines. Blood 2012 119: 2981–2990.

Raveche ES, Salerno E, Scaglione BJ, Manohar V, Abbasi F, Lin YC et al. Abnormal microRNA-16 locus with synteny to human 13q14 linked to CLL in NZB mice. Blood 2007 109: 5079–5086.

Hammarsund M, Corcoran MM, Wilson W, Zhu C, Einhorn S, Sangfelt O et al. Characterization of a novel B-CLL candidate gene--DLEU7--located in the 13q14 tumor suppressor locus. FEBS Lett 2004 556: 75–80.

Brown JR, Hanna M, Tesar B, Pochet N, Vartanov A, Fernandes SM et al. Germline copy number variation associated with Mendelian inheritance of CLL in two families. Leukemia 2012 26: 1710–1713.

Palamarchuk A, Efanov A, Nazaryan N, Santanam U, Alder H, Rassenti L et al. 13q14 deletions in CLL involve cooperating tumor suppressors. Blood 2010 115: 3916–3922.

Dal Bo M, Rossi FM, Rossi D, Deambrogi C, Bertoni F, Del Giudice I et al. 13q14 deletion size and number of deleted cells both influence prognosis in chronic lymphocytic leukemia. Genes Chromosomes Cancer 2011 50: 633–643.

Liu Y, Szekely L, Grander D, Soderhall S, Juliusson G, Gahrton G et al. Chronic lymphocytic leukemia cells with allelic deletions at 13q14 commonly have one intact RB1 gene: evidence for a role of an adjacent locus. Proc Natl Acad Sci USA 1993 90: 8697–8701.

Stilgenbauer S, Dohner H, Bulgay-Morschel M, Weitz S, Bentz M, Lichter P . High frequency of monoallelic retinoblastoma gene deletion in B-cell chronic lymphoid leukemia shown by interphase cytogenetics. Blood 1993 81: 2118–2124.

Shanafelt TD, Witzig TE, Fink SR, Jenkins RB, Paternoster SF, Smoley SA et al. Prospective evaluation of clonal evolution during long-term follow-up of patients with untreated early-stage chronic lymphocytic leukemia. J Clin Oncol 2006 24: 4634–4641.

Malcikova J, Smardova J, Rocnova L, Tichy B, Kuglik P, Vranova V et al. Monoallelic and biallelic inactivation of TP53 gene in chronic lymphocytic leukemia: selection, impact on survival and response to DNA damage. Blood 2009 114: 5307–5314.

Zenz T, Habe S, Denzel T, Mohr J, Winkler D, Buhler A et al. Detailed analysis of p53 pathway defects in fludarabine-refractory chronic lymphocytic leukemia (CLL): dissecting the contribution of 17p deletion, TP53 mutation, p53-p21 dysfunction, and miR34a in a prospective clinical trial. Blood 2009 114: 2589–2597.

Rossi D, Cerri M, Deambrogi C, Sozzi E, Cresta S, Rasi S et al. The prognostic value of TP53 mutations in chronic lymphocytic leukemia is independent of Del17p13: implications for overall survival and chemorefractoriness. Clin Cancer Res 2009 15: 995–1004.

Dohner H, Fischer K, Bentz M, Hansen K, Benner A, Cabot G et al. p53 gene deletion predicts for poor survival and non-response to therapy with purine analogs in chronic B-cell leukemias. Blood 1995 85: 1580–1589.

Zenz T, Krober A, Scherer K, Habe S, Buhler A, Benner A et al. Monoallelic TP53 inactivation is associated with poor prognosis in chronic lymphocytic leukemia: results from a detailed genetic characterization with long-term follow-up. Blood 2008 112: 3322–3329.

Dicker F, Herholz H, Schnittger S, Nakao A, Patten N, Wu L et al. The detection of TP53 mutations in chronic lymphocytic leukemia independently predicts rapid disease progression and is highly correlated with a complex aberrant karyotype. Leukemia 2009 23: 117–124.

Rosenwald A, Chuang EY, Davis RE, Wiestner A, Alizadeh AA, Arthur DC et al. Fludarabine treatment of patients with chronic lymphocytic leukemia induces a p53-dependent gene expression response. Blood 2004 104: 1428–1434.

Tam CS, Shanafelt TD, Wierda WG, Abruzzo LV, Van Dyke DL, O’Brien S et al. De novo deletion 17p13.1 chronic lymphocytic leukemia shows significant clinical heterogeneity: the M. D. Anderson and Mayo Clinic experience. Blood 2009 114: 957–964.

Best OG, Gardiner AC, Davis ZA, Tracy I, Ibbotson RE, Majid A et al. A subset of Binet stage A CLL patients with TP53 abnormalities and mutated IGHV genes have stable disease. Leukemia 2009 23: 212–214.

Knight SJ, Yau C, Clifford R, Timbs AT, Sadighi Akha E, Dreau HM et al. Quantification of subclonal distributions of recurrent genomic aberrations in paired pre-treatment and relapse samples from patients with B-cell chronic lymphocytic leukemia. Leukemia 2012 26: 1564–1575.

Braggio E, Kay NE, Vanwier S, Tschumper RC, Smoley S, Eckel-Passow JE et al. Longitudinal genome-wide analysis of patients with chronic lymphocytic leukemia reveals complex evolution of clonal architecture at disease progression and at the time of relapse. Leukemia 2012 26: 1698–1701.

Stilgenbauer S, Sander S, Bullinger L, Benner A, Leupolt E, Winkler D et al. Clonal evolution in chronic lymphocytic leukemia: acquisition of high-risk genomic aberrations associated with unmutated VH, resistance to therapy, and short survival. Haematologica 2007 92: 1242–1245.

Rossi S, Shimizu M, Barbarotto E, Nicoloso MS, Dimitri F, Sampath D et al. microRNA fingerprinting of CLL patients with chromosome 17p deletion identify a miR-21 score that stratifies early survival. Blood 2010 116: 945–952.

Fegan C, Robinson H, Thompson P, Whittaker JA, White D . Karyotypic evolution in CLL: identification of a new sub-group of patients with deletions of 11q and advanced or progressive disease. Leukemia 1995 9: 2003–2008.

Neilson JR, Auer R, White D, Bienz N, Waters JJ, Whittaker JA et al. Deletions at 11q identify a subset of patients with typical CLL who show consistent disease progression and reduced survival. Leukemia 1997 11: 1929–1932.

Saiya-Cork K, Collins R, Parkin B, Ouillette P, Kuizon E, Kujawski L et al. A pathobiological role of the insulin receptor in chronic lymphocytic leukemia. Clin Cancer Res 2011 17: 2679–2692.

Rossi D, Fangazio M, Rasi S, Vaisitti T, Monti S, Cresta S et al. Disruption of BIRC3 associates with fludarabine chemorefractoriness in TP53 wild-type chronic lymphocytic leukemia. Blood 2012 119: 2854–2862.

Mohr J, Helfrich H, Fuge M, Eldering E, Buhler A, Winkler D et al. DNA damage-induced transcriptional program in CLL: biological and diagnostic implications for functional p53 testing. Blood 2011 117: 1622–1632.

Herling M, Patel KA, Weit N, Lilienthal N, Hallek M, Keating MJ et al. High TCL1 levels are a marker of B-cell receptor pathway responsiveness and adverse outcome in chronic lymphocytic leukemia. Blood 2009 114: 4675–4686.

Tsimberidou AM, Tam C, Abruzzo LV, O’Brien S, Wierda WG, Lerner S et al. Chemoimmunotherapy may overcome the adverse prognostic significance of 11q deletion in previously untreated patients with chronic lymphocytic leukemia. Cancer 2009 115: 373–380.

Dohner H, Stilgenbauer S, James MR, Benner A, Weilguni T, Bentz M et al. 11q deletions identify a new subset of B-cell chronic lymphocytic leukemia characterized by extensive nodal involvement and inferior prognosis. Blood 1997 89: 2516–2522.

Shedden K, Li Y, Ouillette P, Malek SN . Characteristics of chronic lymphocytic leukemia with somatically acquired mutations in NOTCH1 exon 34. Leukemia 2012 26: 1108–1110.

Balatti V, Bottoni A, Palamarchuk A, Alder H, Rassenti LZ, Kipps TJ et al. NOTCH1 mutations in CLL associated with trisomy 12. Blood 2012 119: 329–331.

Lopez C, Delgado J, Costa D, Conde L, Ghita G, Villamor N et al. Different distribution of NOTCH1 mutations in chronic lymphocytic leukemia with isolated trisomy 12 or associated with other chromosomal alterations. Genes Chromosomes Cancer 2012 51: 881–889.

Del Giudice I, Rossi D, Chiaretti S, Marinelli M, Tavolaro S, Gabrielli S et al. NOTCH1 mutations in +12 chronic lymphocytic leukemia (CLL) confer an unfavorable prognosis, induce a distinctive transcriptional profiling and refine the intermediate prognosis of +12 CLL. Haematologica 2012 97: 437–441.

Decker S, Zirlik K, Djebatchie L, Hartmann D, Ihorst G, Schmitt-Graeff A et al. Trisomy 12 and elevated GLI1 and PTCH1 transcript levels are biomarkers for Hedgehog-inhibitor responsiveness in CLL. Blood 2012 119: 997–1007.

Zenz T, Vollmer D, Trbusek M, Smardova J, Benner A, Soussi T et al. TP53 mutation profile in chronic lymphocytic leukemia: evidence for a disease specific profile from a comprehensive analysis of 268 mutations. Leukemia 2010 24: 2072–2079.

Puente XS, Pinyol M, Quesada V, Conde L, Ordonez GR, Villamor N et al. Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 2011 475: 101–105.

Fabbri G, Rasi S, Rossi D, Trifonov V, Khiabanian H, Ma J et al. Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation. J Exp Med 2011 208: 1389–1401.

Trbusek M, Smardova J, Malcikova J, Sebejova L, Dobes P, Svitakova M et al. Missense mutations located in structural p53 DNA-binding motifs are associated with extremely poor survival in chronic lymphocytic leukemia. J Clin Oncol 2011 29: 2703–2708.

Gaidano G, Ballerini P, Gong JZ, Inghirami G, Neri A, Newcomb EW et al. p53 mutations in human lymphoid malignancies: association with Burkitt lymphoma and chronic lymphocytic leukemia. Proc Natl Acad Sci USA 1991 88: 5413–5417.

Zainuddin N, Murray F, Kanduri M, Gunnarsson R, Smedby KE, Enblad G et al. TP53 mutations are infrequent in newly diagnosed chronic lymphocytic leukemia. Leuk Res 2011 35: 272–274.

Johnson GG, Sherrington PD, Carter A, Lin K, Liloglou T, Field JK et al. A novel type of p53 pathway dysfunction in chronic lymphocytic leukemia resulting from two interacting single nucleotide polymorphisms within the p21 gene. Cancer Res 2009 69: 5210–5217.

el Rouby S, Thomas A, Costin D, Rosenberg CR, Potmesil M, Silber R et al. p53 gene mutation in B-cell chronic lymphocytic leukemia is associated with drug resistance and is independent of MDR1/MDR3 gene expression. Blood 1993 82: 3452–3459.

Wattel E, Preudhomme C, Hecquet B, Vanrumbeke M, Quesnel B, Dervite I et al. p53 mutations are associated with resistance to chemotherapy and short survival in hematologic malignancies. Blood 1994 84: 3148–3157.

Gonzalez D, Martinez P, Wade R, Hockley S, Oscier D, Matutes E et al. Mutational status of the TP53 gene as a predictor of response and survival in patients with chronic lymphocytic leukemia: results from the LRF CLL4 trial. J Clin Oncol 2011 29: 2223–2229.

Zenz T, Eichhorst B, Busch R, Denzel T, Habe S, Winkler D et al. TP53 mutation and survival in chronic lymphocytic leukemia. J Clin Oncol 2010 28: 4473–4479.

Sorror ML, Storer BE, Sandmaier BM, Maris M, Shizuru J, Maziarz R et al. Five-year follow-up of patients with advanced chronic lymphocytic leukemia treated with allogeneic hematopoietic cell transplantation after nonmyeloablative conditioning. J Clin Oncol 2008 26: 4912–4920.

Malek S . Clinical utility of prognostic markers in chronic lymphocytic leukemia. ASCO Education Book 2010 [review] 263–267.

Bullrich F, Rasio D, Kitada S, Starostik P, Kipps T, Keating M et al. ATM mutations in B-cell chronic lymphocytic leukemia. Cancer Res 1999 59: 24–27.

Schaffner C, Stilgenbauer S, Rappold GA, Dohner H, Lichter P . Somatic ATM mutations indicate a pathogenic role of ATM in B-cell chronic lymphocytic leukemia. Blood 1999 94: 748–753.

Stankovic T, Weber P, Stewart G, Bedenham T, Murray J, Byrd PJ et al. Inactivation of ataxia telangiectasia mutated gene in B-cell chronic lymphocytic leukaemia. Lancet 1999 353: 26–29.

Austen B, Skowronska A, Baker C, Powell JE, Gardiner A, Oscier D et al. Mutation status of the residual ATM allele is an important determinant of the cellular response to chemotherapy and survival in patients with chronic lymphocytic leukemia containing an 11q deletion. J Clin Oncol 2007 25: 5448–5457.

Ouillette P, Li J, Shaknovich R, Li Y, Melnick A, Shedden K et al. Incidence and clinical implications of ATM aberrations in chronic lymphocytic leukemia. Genes Chromosomes Cancer 2012, (in press).

Austen B, Powell JE, Alvi A, Edwards I, Hooper L, Starczynski J et al. Mutations in the ATM gene lead to impaired overall and treatment-free survival that is independent of IGVH mutation status in patients with B-CLL. Blood 2005 106: 3175–3182.

Cejkova S, Rocnova L, Potesil D, Smardova J, Novakova V, Chumchalova J et al. Presence of heterozygous ATM deletion may not be critical in the primary response of chronic lymphocytic leukemia cells to fludarabine. Eur J Haematol 2009 82: 133–142.

Lozanski G, Ruppert AS, Heerem NA, Lozanski A, Luca DM, Gordon A et al. Variations of the ATM gene in chronic lymphocytic leukemia patients lack substantial impact on progression-free survival and overall survival: a Cancer and Leukemia Group B Study. Leuk Lymphoma 2012 53: 1743–1748.

Balatti V, Bottoni A, Palamarchuk A, Alder H, Rassenti LZ, Kipps TJ et al. NOTCH1 mutations in CLL associated with trisomy 12. Blood 2012 119: 329–331.

Rossi D, Rasi S, Fabbri G, Spina V, Fangazio M, Forconi F et al. Mutations of NOTCH1 are an independent predictor of survival in chronic lymphocytic leukemia. Blood 2012 119: 521–529.

Rossi D, Bruscaggin A, Spina V, Rasi S, Khiabanian H, Messina M et al. Mutations of the SF3B1 splicing factor in chronic lymphocytic leukemia: association with progression and fludarabine-refractoriness. Blood 2011 118: 6904–6908.

Brown JR, Levine RL, Thompson C, Basile G, Gilliland DG, Freedman AS . Systematic genomic screen for tyrosine kinase mutations in CLL. Leukemia 2008 22: 1966–1969.

Ngo VN, Young RM, Schmitz R, Jhavar S, Xiao W, Lim KH et al. Oncogenically active MYD88 mutations in human lymphoma. Nature 2011 470: 115–119.

Gahrton G, Robert KH, Friberg K, Zech L, Bird AG . Nonrandom chromosomal aberrations in chronic lymphocytic leukemia revealed by polyclonal B-cell-mitogen stimulation. Blood 1980 56: 640–647.

Juliusson G, Oscier DG, Fitchett M, Ross FM, Stockdill G, Mackie MJ et al. Prognostic subgroups in B-cell chronic lymphocytic leukemia defined by specific chromosomal abnormalities. N Engl J Med 1990 323: 720–724.

Peterson LC, Lindquist LL, Church S, Kay NE . Frequent clonal abnormalities of chromosome band 13q14 in B-cell chronic lymphocytic leukemia: multiple clones, subclones, and nonclonal alterations in 82 midwestern patients. Genes Chromosomes Cancer 1992 4: 273–280.

Dohner H, Stilgenbauer S, Fischer K, Bentz M, Lichter P . Cytogenetic and molecular cytogenetic analysis of B cell chronic lymphocytic leukemia: specific chromosome aberrations identify prognostic subgroups of patients and point to loci of candidate genes. Leukemia 1997 11: S19–S24.

Dicker F, Schnittger S, Haferlach T, Kern W, Schoch C . Immunostimulatory oligonucleotide-induced metaphase cytogenetics detect chromosomal aberrations in 80% of CLL patients: A study of 132 CLL cases with correlation to FISH, IgVH status, and CD38 expression. Blood 2006 108: 3152–3160.

Put N, Konings P, Rack K, Jamar M, Van Roy N, Libouton JM et al. Improved detection of chromosomal abnormalities in chronic lymphocytic leukemia by conventional cytogenetics using CpG oligonucleotide and interleukin-2 stimulation: a Belgian multicentric study. Genes Chromosomes Cancer 2009 48: 843–853.

Mayr C, Speicher MR, Kofler DM, Buhmann R, Strehl J, Busch R et al. Chromosomal translocations are associated with poor prognosis in chronic lymphocytic leukemia. Blood 2006 107: 742–751.

Heerema NA, Byrd JC, Dal Cin PS, Dell’ Aquila ML, Koduru PR, Aviram A et al. Stimulation of chronic lymphocytic leukemia cells with CpG oligodeoxynucleotide gives consistent karyotypic results among laboratories: a CLL Research Consortium (CRC) Study. Cancer Genet Cytogenet 2010 203: 134–140.

Muthusamy N, Breidenbach H, Andritsos L, Flynn J, Jones J, Ramanunni A et al. Enhanced detection of chromosomal abnormalities in chronic lymphocytic leukemia by conventional cytogenetics using CpG oligonucleotide in combination with pokeweed mitogen and phorbol myristate acetate. Cancer Genet 2011 204: 77–83.

Huh YO, Schweighofer CD, Ketterling RP, Knudson RA, Vega F, Kim JE et al. Chronic lymphocytic leukemia with t(1419)(q32q13) is characterized by atypical morphologic and immunophenotypic features and distinctive genetic features. Am J Clin Pathol 2011 135: 686–696.

Martin-Subero JI, Ibbotson R, Klapper W, Michaux L, Callet-Bauchu E, Berger F et al. A comprehensive genetic and histopathologic analysis identifies two subgroups of B-cell malignancies carrying a t(1419)(q32q13) or variant BCL3-translocation. Leukemia 2007 21: 1532–1544.

Ueshima Y, Bird ML, Vardiman JW, Rowley JDA . 1419 translocation in B-cell chronic lymphocytic leukemia: a new recurring chromosome aberration. Int J Cancer 1985 36: 287–290.

Nguyen-Khac F, Chapiro E, Lesty C, Grelier A, Luquet I, Radford-Weiss I et al. Specific chromosomal IG translocations have different prognoses in chronic lymphocytic leukemia. Am J Blood Res 2011 1: 13–21.

Van Den Neste E, Robin V, Francart J, Hagemeijer A, Stul M, Vandenberghe P et al. Chromosomal translocations independently predict treatment failure, treatment-free survival and overall survival in B-cell chronic lymphocytic leukemia patients treated with cladribine. Leukemia 2007 21: 1715–1722.

Rigolin GM, Cibien F, Martinelli S, Formigaro L, Rizzotto L, Tammiso E et al. Chromosome aberrations detected by conventional karyotyping using novel mitogens in chronic lymphocytic leukemia with ‘normal’ FISH: correlations with clinicobiologic parameters. Blood 2012 119: 2310–2313.

Visone R, Rassenti LZ, Veronese A, Taccioli C, Costinean S, Aguda BD et al. Karyotype-specific microRNA signature in chronic lymphocytic leukemia. Blood 2009 114: 3872–3879.

Kanduri M, Cahill N, Göransson H, Enström C, Ryan F, Isaksson A et al. Differential genome-wide array-based methylation profiles in prognostic subsets of chronic lymphocytic leukemia. Blood 2010 115: 296–305.

Herman SE, Gordon AL, Hertlein E, Ramanunni A, Zhang X, Jaglowski S et al. Bruton tyrosine kinase represents a promising therapeutic target for treatment of chronic lymphocytic leukemia and is effectively targeted by PCI-32765. Blood 2011 117: 6287–6296.

Hoellenriegel J, Meadows SA, Sivina M, Wierda WG, Kantarjian H, Keating MJ et al. The phosphoinositide 3′-kinase delta inhibitor, CAL-101, inhibits B-cell receptor signaling and chemokine networks in chronic lymphocytic leukemia. Blood 2011 118: 3603–3612.

Ponader S, Chen SS, Buggy JJ, Balakrishnan K, Gandhi V, Wierda WG et al. The Bruton tyrosine kinase inhibitor PCI-32765 thwarts chronic lymphocytic leukemia cell survival and tissue homing in vitro and in vivo. Blood 2012 119: 1182–1189.

What is the meaning and significance of extreme pathways - Biology

8 hours due to maintenance in our data center. This interval could potentially be shorter depending on the progress of the work. We apologize for any inconvenience. *** --> *** DAVID will be down from 5pm EST Friday 6/24/2011 to 3pm EST Sunday 6/26/2011 due to maintenance in our data center. This interval could potentially be shorter depending on the progress of the work. We apologize for any inconvenience. *** --> *** We are currently accepting Beta users for our new DAVID Web Service which allows access to DAVID from various programming languages. Please contact us for access. *** --> *** The Gene Symbol mapping for list upload and conversion has changed. Please see the DAVID forum announcement for details. --> *** Announcing the new DAVID Web Service which allows access to DAVID from various programming languages. More info. *** --> *** DAVID 6.8 will be down for maintenance on Thursday, 2/23/2016, from 9AM-1PM EST *** -->
*** Welcome to DAVID 6.8 ***
*** If you are looking for DAVID 6.7, please visit our development site. ***
*** Welcome to DAVID 6.8 with updated Knowledgebase ( more info). ***
*** If you are looking for DAVID 6.7, please visit our development site. ***
*** Welcome to DAVID 6.8 with updated Knowledgebase ( more info). ***
*** The DAVID 6.7 server is currently down for maintenance. ***
--> *** Please read: Due to data center maintenance, DAVID will be offline from Friday, June 17th @ 4pm EST through Sunday, June 19th with the possibility of being back online sooner. *** -->

Materials and methods

DNA microarray data

The nine data sets used to compare the gene-set activation metrics were selected from eight studies in the GEO database. Each data set contained two relatively homogeneous subsets of samples. One study (GDS1329) provided two data sets. These subsets consisted of a baseline type and pathological samples or, in some cases, two different but related disease types. (Samples not in either subset were omitted from the comparisons.) We treated these single-channel data sets as ratio data sets by computing the median for each gene over all the baseline samples and dividing all expression values by the corresponding median and taking the base-10 logarithm. For each data set, Table 1 contains the GEO identifier and nature and sizes (in parentheses) of the two sample subgroups. In each data set the samples in Subgroup 1 constitute the baseline set.

To create the human body atlas, oligonucleotide probes were placed at each exon-exon junction of 11,138 RefSeq transcripts [35]. Purchased mRNA from 44 tissues in normal physiological state, pooled from multiple individuals, and 8 cell lines were amplified and labeled using a full-length amplification protocol and hybridized in duplicate in a two-color dye swap experiment[54]. In Johnson et al. [35], six of 52 tissues contained data for only 80% of the genes. For five of these tissues (pancreas, kidney, Burkitt's lymphoma (Raji), lung carcinoma (A549), and melanoma (G361)), new hybridizations were performed here to fill in the missing data. After background normalization, the intensity value of each probe in each tissue was divided by the average intensity across all 52 tissues to determine a ratio, and then the log10 of that ratio used for further analysis. Standard deviations (SDs) for each intensity measurement were calculated using the equation:

SD = a + b ∗ i n t e n s i t y [email protected]@[email protected]@+=feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2Caerbhv2BYDwAHbqedmvETj2BSbqee0evGueE0jxyaibaiKI8=vI8tuQ8FMI8Gi=hEeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciGacaGaaeqabaqadeqadaaakeaacaqGtbGaaeiraiabg2da9maakaaabaGaamyyaiabgUcaRiaadkgacqGHxiIkieGacaWFPbG[email protected][email protected]

where a = 100 and b = 0.2 were empirically derived from individual same-versus-same and same-versus-different hybridization experiments and represent single-hybridization, single-probe estimates of background (a) and fractional error (b). As we used multiple probes per gene and two hybridizations per sample pair (a dye-swap), final error estimates for gene expression are a combination of both propagation of this model measurement error and variance over the repeat measurements. These error estimates were then propagated to ratio and log10 ratio error estimates (Supplemental Tables T10 and T11 in Additional data file 2). Since the initial array design, NCBI has removed over 300 of the RefSeq transcripts from their databases. After removing these transcripts and any other transcripts currently unmapped to Entrez gene identifiers from our data set, the remaining 10,815 RefSeq transcripts map to 9,982 genes. Finally, using all gene-associated probes, we calculated an error-weighted average of log10 ratios for each gene in each tissue. Probe-level expression data have been deposited in the GEO database [35] (GSE740), and all gene and pathway expression data are available online [21].

Gene sets and coherence filtering

We compiled 1,281 gene sets from the 1 November 2004 Release of GO (241 from cellular component and 1,040 from biological process), and 117 gene sets from KEGG Release 33, downloaded 11 January 2005. The mean number of genes in each set was 23.8 ± 28.5 (mean ± SD minimum 1, maximum 159). To build the human pathway expression map we reduced these to 290 gene sets (23 from KEGG, 89 from the GO Cellular Component hierarchy, and 178 from the GO Biological Process hierarchy) by applying two filters. First, we required that each gene set retained contain at least five genes and no more than 200 genes. Second, we filtered gene sets based on their coherence, the percentage of total variance of the expression values within a gene set captured by the first principal component across all tissues. This idea has been discussed previously [20], although we used a different test for coherence here. To determine the appropriate cutoff for a gene set of size |S|, we generated 1,000 random gene sets of size |S|, and calculated the distribution of coherence values. The random-set coherence distribution was approximately normal, although its mean and standard deviation were size-dependent. Of the initial 1,401 gene sets, 290 had a coherence over the human body atlas data set that was more than 2.6 standard deviations greater than the mean of the random-set distribution for that size (corresponding to a one-sided p value of 0.005), and these 290 sets were retained for further analysis. The mean number of genes in these 290 coherent gene sets was 33.8 ± 32.9 (mean ± SD minimum 5, maximum 159).

Some of the 290 gene sets overlap in component genes, and some gene sets are subsets of others. This is due to the hierarchical nature of GO and functional overlap with gene sets in KEGG. Rather than merge these sets we kept them all in order to maximize the functional annotation conveyed by the gene set names. To measure the overlap between two gene sets we used the average of the two ratios of the number of genes in the intersection of the two gene sets to the total number of genes in each gene set. The overlap is most significant between gene sets in the same block ranging from a low of 7% in the Cell-selective block to a high of 85% in the Hemoglobin block with a mean within-block overlap over all 14 blocks of 31%.

Measuring gene set expression

We compared five gene-set activation metrics. Given a gene g, let X tgbe the expression value (log10 fold change, relative to background) for gene g in tissue t. Let S be the set of genes in a pathway. For tissue t, if <X tS> and <X t> are the mean of X tgover the genes in S and all the genes on the microarray, respectively, and σ tis the standard deviation of X tgover all the genes on the microarray, then the Z-score activation metric used to measure the relative expression level of pathway S in tissue t is:

Z t S = < X t S > − < X t > σ t | S | [email protected]@[email protected]@+=feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2Caerbhv2BYDwAHbqedmvETj2BSbqee0evGueE0jxyaibaiKI8=vI8tuQ8FMI8Gi=hEeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciGacaGaaeqabaqadeqadaaakeaacaWGAbWaaSbaaSqaaiaadshacaWGtbaabeaakiabg2da9maalaaabaGaeyipaWJaamiwamaaBaaaleaacaWG0bGaam4uaaqabaGccqGH+aGpcqGHsislcqGH8aapcaWGybWaaSbaaSqaaiaadshaaeqaaOGaeyOpa4dabaGaeq4Wdm3aaSbaaSqaaiaad[email protected][email protected]

where |S| is the number of genes in S. The value of Z is expressed in units of standard deviation and is a measure of violation of the null hypothesis that the genes in S are independently sampled from a distribution similar to that of all the genes on the microarray. If the null hypothesis is valid, then Z will have approximately a standard normal distribution, and so a large positive value of Z tsuggests collective upregulation of the genes in S (which we consider to represent 'activation' of S) in tissue t a large negative value suggests collective downregulation. The normalization by | S | [email protected]@[email protected]@+=feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2Caerbhv2BYDwAHbqedmvETj2BSbqee0evGueE0jxyaibaiKI8=vI8tuQ8FMI8Gi=hEeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciGacaGaaeqabaqa[email protected][email protected] makes comparison of different-sized gene sets possible and reflects the fact that, for larger gene sets, even a slight collective shift in fold change can be significant.

Because the Z-statistic essentially measures a shift in location (mean expression) for the genes in S, we compared its sensitivity to several other possible signed measures of location shift, which were created by modifying, where necessary, standard statistics with a sign to indicate the direction of expression change. The Wilcoxon Z statistic is a well-known statistic that is calculated according to a similar formula, but using the ranks of the X tgamong all genes in tissue t, rather than the actual fold changes. To calculate a signed KS statistic, we computed each of the two one-sided KS statistics, comparing the distribution of the expression values in S with the distribution of the genes on the microarray as a whole, and took the larger of the two statistics, with the appropriate sign. To calculate a hypergeometric p value, we used a threshold of two-fold differential expression (other threshold values showed qualitatively similar results, data not shown) to define an induced or repressed gene, and then calculated the probability that the relative enrichment of differentially expressed genes observed in a gene set in a particular tissue could have been observed by chance, using the hypergeometric distribution. To provide a sign for the hypergeometric p value, the calculation was done separately for the induced and repressed genes in each set, and the smaller of the two p value was used, as well as its 'sign' (negative if repressed genes were more enriched in the gene set than induced genes, positive otherwise). The relative insensitivity of the HG metric was little changed by varying the differential expression threshold. Finally, for the PCA statistic, we calculated PC1, the first principal component of the expression values of the genes in S across all tissues, and used the projection (scalar product) of the expression values in a tissue with PC1 as a measure of activation of the gene set in that tissue.

ROC comparison of activation metrics

We compared the five activation metrics for measuring gene set expression, and the individual genes in the expression data set, for their detection sensitivity. We applied each metric to measure the activation of the gene sets that met a coherence threshold (p ≤ 0.01, 0.05, 0.10, and 1.0) in each of the nine GEO data sets. For each data set we compared two classes that were known to be different (typically one class was normal and the other pathological). Each gene set was measured in each sample in each of the two classes by each metric. We used a two-sided Wilcoxon rank sum test for equal medians to test the null hypothesis that the activation metric values for each gene set in the two classes come from distributions with equal medians. The result of this test is quantified by the returned p value. The smaller the p value, the more unlikely is the null hypothesis that the gene set median values are equal. We performed this test between the two classes for all gene sets. In a similar manner, we used the same test to compare individual gene expression values between the two classes. We used the p value from the two-sided Wilcoxon rank sum statistic to compute a false detection rate for each p value threshold using the adaptive method of Benjamini and Hochberg [55] and displayed the results using ROC curves [56]. The x-axis is the proportion of false positives the percent of gene sets that did not distinguish the two classes at the specified p value threshold. The y-axis is the true positive rate the percent of gene sets that did distinguish the two classes at the specified threshold. The interval of [0, 0.3] was chosen to correspond to what might be an acceptable FDR. The percent of true positives varies between data sets and is presumably indicative of the type(s) of biological differences between the two classes in each data set.

Watch the video: PathWhiz Tutorial: An Introduction to Pathways (July 2022).


  1. Kaemon

    I think mistakes are made.

  2. Ashlan

    Your thought is magnificent

  3. Barwolf

    This is a very valuable piece.

  4. Alis

    I congratulate, your thinking simply excellent

  5. Raja

    You have hit the mark. It seems to me it is very good thought. Completely with you I will agree.

  6. Avinoam

    I congratulate, it is simply excellent thought

  7. Kedar

    By what a charming topic

Write a message