For q ≠ 1, ∞, the diversity profile calculation is thus where . The resulting q D Z (p) is an effective number, and for certain values of q and Z, q D Z (p) corresponds to a commonly used diversity index. For example, for naïve diversity profiles

that do not PND-1186 mw take into account similarity between species, q = 0 is equivalent species richness, q = 1 is proportional to Shannon Diversity [4], q = 2 is proportional to 1/D (inverse Simpson Diversity) [25], and as q moves toward ∞, it is a measure of 1/Berger-Parker Evenness [5]. We calculated diversity profiles for 0 ≤ q ≤ 5. When plotting the profiles, we created larger insets for 1 ≤ q ≤ 2 [26]. For a more detailed description of the formulae used to calculate diversity profiles (e.g., their relationship to well-known AZD0530 molecular weight diversity metrics, their potential benefits in diversity studies, examples of diversity profiles applied to macro-organism community datasets), refer to

Leinster & Cobbold’s work [17]. Environmental microbial datasets Diversity profiles were used to quantify the diversity of four microbial datasets obtained from different environments containing bacterial, archaeal, fungal, and viral communities. The original four studies were conceived independently by co-authors of the current study, and we utilized these existing datasets to explore applications of diversity profiles to microbial community data. Providing complete details of each study is beyond the scope of the current study, but we have included brief descriptions of the studies’ methods below, and the research questions and hypotheses that shaped the design of each study are detailed in Table 1. We have also provided predicted outcomes of each of the studies, based on data and hypotheses from the original studies (Table 2). For further details of each study, please refer to medroxyprogesterone the publications cited below. Table 1 Research questions and hypotheses that shaped the design of the four environmental microbial community datasets   Research

questions Hypotheses Acid mine drainage bacteria and archaea 1) Are environmental (Env) samples more diverse than bioreactor (BR) biofilms? H1: Bioreactor growth conditions usually have a higher pH than the Birinapant concentration environment, and the geochemistry of the drainage might differ from growth media. Thus, environmental biofilms are expected to be more diverse than bioreactor-grown biofilms. 2) Is biofilm diversity higher at higher stages of biofilm development? H2: As biofilms begin to establish, early growth-stage biofilms are expected to be less diverse. As they mature, more organisms join the community, increasing diversity. Hypersaline lake viruses 1) How do viral diversities change across spatiotemporal replicates? H1: Viral diversity will be greatest in pools with larger volume (2010A and 2007A samples). H2: Community dissimilarity will cluster by site, then by year.

1b, 2b, 3b (left side) and 4b (left side); the maximal y-axis values should be 30, 25, 15 and 35, respectively. Most importantly, the equation in Fig. 1b should be: $$ \texty=0.0105 \textx^2+0.4119 \textx+0.3810. $$ None of the chlorophyll per fresh weight data are affected by this erratum, nor is the running text influenced in any way. All R 2 values are unaffected.”
“Erratum to: buy MDV3100 Photosynth Res (2010) 105:249–255 DOI 10.1007/s11120-010-9588-y There was incorrect information in the second, third and

fourth full sentences on page 253 of the orginal publication (‘As is evident…’). They should read as follows: The lifetime of the fastest alpha component was 0.26 ms selleck chemicals llc and contributed 67% of the total amplitude. The beta component was about 7-fold slower (life time ~1.9 ms) and it was responsible for 32% of the total amplitude. The gamma component was very slow with lifetime of ~7 ms and small, being only 1% of the total amplitude in control leaves. These results are in agreement with those obtained on pea leaves, determined with those

obtained on pea leaves, determined with the same method (Toth and Strasser 2005). Reference Toth SZ, Strasser RJ (2005) The specific rate of QA reduction and photosystem II heterogeneity. Proceedings of the 13th international CHIR98014 ic50 congress on photosynthesis, Montreal, Canada, pp 198–200″
“Introduction The capture of solar energy to power industrial processes has been an inviting prospect for decades. The energy density of solar radiation and its potential as a source for production of fuels, if efficiently captured and converted, could support the goals of national energy independence. Analyses of photosynthetic conversion have been driven by this promise (Goldman 1978; Pirt 1983; Bolton and

Hall 1991; Zhu et al. 2008, 2010). The deployment of solar-based industries for fuels has, however, been limited by the lack of efficient Gemcitabine mouse cost-effective technologies. Projects funded between 1976 and 1996 under the US Department of Energy (DOE) aquatic species program explored phototrophic organisms and process technologies for the production of algal oils and their refinement into biodiesel. The results of these efforts were summarized in a report that delineated the technological barriers to industrial development (Sheehan et al. 1998). The traditional photosynthetic fuels process is one wherein triglyceride-producing algae are grown under illumination and stressed to induce the diversion of a fraction of carbon to oil production. The algal biomass is harvested, dewatered and lysed, and processed to yield a product that is chemically refined to an acyl ester biodiesel product. Many companies have been founded since the DOE final report that strive to make incremental improvements in this process to create viable solar energy-to-fuel technologies.

The equivalent to 1 mg of fecal material is loaded on each lane. A RNA fragment size (nt) marker was loaded in the first lane from the left side. B) Summary plot of average RNA integrity numbers (RIN) obtained with samples stored in the above 12 conditions. N = 11 individuals for the 88 samples stored without RNAse inhibitor. Standard deviation

is indicated for each storage condition. N = 6 individuals for the 24 samples stored with RNAse inhibitor. Statistical analysis was LY2603618 ic50 performed using Poisson regression model (the star (*) means that the comparison with the frozen sample RIN number was significant with p < 0.05). In all the conditions tested, the amount of RNA extracted was above 30 μg per 250 mg of stool, which is adequate for downstream analyses such as

Romidepsin mouse Foretinib manufacturer qRT-PCR and microarray experiments. When samples were immediately frozen after collection, extracted RNA had average RIN numbers above the value 7, which is the threshold acceptable for conducting metatranscriptomic studies [17, 18]. However, unfreezing these samples during 1 h or 3 h before starting RNA extraction produced a strong RNA degradation, as illustrated in figure 1A by the fading of the 23S rRNA band and the appearance of numerous bands below the 16S rRNA. Decrease of the RIN numbers was significant after thawing samples for 1 h (p = 0.006, Wilcoxon paired test) and 3 h (p = 0.004, Wilcoxon paired test) compared to frozen samples. Conversely, when samples were kept at room temperature during few hours (3 h to 24 h) rather than immediately

frozen after collection, total RNA extracted did not show signs of fragmentation and average RIN numbers were above 7. Longer storage periods at room temperature (more than 24 h) produced a progressive fragmentation of the RNA. Indeed, decrease in RIN number became significant when samples were kept at room temperature during 48 h (p = 0.036, Wilcoxon paired test). Finally, when samples were kept at room temperature in RNAse inhibitor STK38 solution, they showed less signs of fragmentation even after 4 weeks (figure 3A). In these conditions, however, there was a large RIN number variability among individuals (figure 1B). Thus, our results indicate that the best storing condition to extract high quality RNA for metatranscriptomic analyses is to keep the stool samples at room (or low) temperature no more than few hours (< 24 h) after collection. Alternatively, samples can be kept at −20°C for longer periods as long as defrosting is prevented until the extraction of RNA starts in the laboratory.

H. capsulatum is a fungal pathogen that affects a wide range of mammal species, including the human. Autochthonous clinical cases have been reported between the latitudes 54° 05′ North (Alberta, Canada) and 38° South (Neuquén, Argentina) [1, 2]. The disease associated with this fungus is relevant in the geographical areas where histoplasmosis is endemic or epidemic, such

as the Missouri, Ohio, and Mississippi river valleys, in the United States of America Ricolinostat purchase (USA), and some Latin American countries with a high frequency of outbreaks [3, 4]. In Mexico, histoplasmosis is widely distributed and case reports are rather variable [4]. Infection is caused by the inhalation

of fungal saprobe mycelial-phase propagules (infective form) that develop in special environments and are mainly found in bat guano accumulated in confined spaces such as caves and abandoned mines and buildings. The potential role of bats in spreading H. capsulatum in nature remains unclear. The high risk of natural bat infection with this fungus in Mexican caves has been well-documented [5–8]. According to their genetic diversities, H. capsulatum isolates from different geographical origins have been grouped into eight clades; seven of which are considered phylogenetic species. Among these, highlight the LAm A clade that harbours significant genetic variability Galunisertib [9]. The genus Pneumocystis contains highly diversified fungal pathogens that are harboured by a wide range of mammal hosts [10–16]. Pneumocystis organisms, which are transmitted via host-to-host airborne route, have a marked host-species-related Adenosine diversity that is associated with close host specificity. The high divergence

among Pneumocystis species most likely resulted from a prolonged process of co-evolution with each mammal host, selleck screening library mostly associated with co-speciation, as suggested by Demanche et al. [12] and Hugot et al. [13]. Although most phenotypic and genotypic data supporting Pneumocystis stenoxenism derives from laboratory animal models or captive animals, reports about Pneumocystis prevalence and circulation in wild fauna are scarce [12–16]. Unpublished preliminary data by our team revealed H. capsulatum and Pneumocystis co-infection in two randomly captured bats, identifying these mammals as probable reservoirs and dispersers of both parasites in nature (Dei-Cas E and Taylor ML, comm. pers.). The study of co-infection systems, where the host (i.e. a wild host) usually harbours two or multiple parasites, requires an in-depth investigation to determine a comprehensive understanding of this multi-infectious process in regards to its dynamics and consequences. H.

The first and second scenarios, however, appear rather unlikely, because hardly any macrophages

or monocytes were observed in histopathologic analyses at day one after infection. The third scenario appears quite likely, because histopathological analysis revealed a strong infiltration of neutrophils encasing ungerminated conidia. In contrast, functionally attenuated neutrophils and macrophages in corticosteroid-treated mice allowed development of invasive disease despite robust cellular recruitment in the lung parenchyma.   The treatment of mice with cortisone acetate or the combination of clodrolip and cortisone acetate led to 100% mortality and invasive fungal growth within the lung tissue. Although systemic administration of corticosteroids increases the number of circulating neutrophils by three- to fivefold [31], their ability to damage A. fumigatus hyphae is strongly reduced [32]. One day post-infection,

the lung tissue showed learn more LY294002 nmr an extensive neutrophilic infiltration that surrounded germinating conidia. These neutrophils were able to delay uncontrolled tissue invasion by killing some proportion of fungal hyphae. As a consequence of the neutrophil infiltration severe tissue damage accompanied by parenchymal destruction (necrosis) was observed, leading to a decreased bioluminescence as described above. It is also noteworthy that under cortisone acetate treatment the efficiency of alveolar macrophages in inhibiting conidial germination after phagocytosis was strongly defective. None of the other treatment groups yielded hyphal germlings as early as one day post-infection. It could be assumed that this rapid germination is due to growth stimulation

via A. fumigatus corticosteroid receptors [33]. However, experiments, in which different concentrations of cortisone acetate were added to A. fumigatus cultures, neither stimulated conidia germination, nor increased the light https://www.selleckchem.com/products/pi3k-hdac-inhibitor-i.html emission (data not shown). Since new cortisone acetate itself constitutes an “”inactive”" corticosteroid derivative, which is converted into “”active”" cortisol during metabolism in the liver [34], it might be possible that a stimulation of germination is only mediated by this metabolite rather than by cortisone acetate. Another possibility for the rapid germination of conidia is given by a neutrophil mediated tissue destruction releasing large amounts of nutrients from tissue cells, which enhanced the germination speed under this immunosuppresive regimen. The mild inflammation under RB6-8C5 treatment one day post infection and the absence of inflammation under cyclophosphamide treatment may not provide the same nutritional conditions leading to a delayed germination when compared to the cortisone acetate treatment. Another piece of evidence that supports the dependence on the number and functional integrity of neutrophils in the clearance of A. fumigatus is the observation that RB6-8C5 treatment renders mice highly susceptible to IA.

1), M. leprae TN (AL450380.1), M. marinum M (CP000854.1), M. parascrofulaceum BAA-614 (ADNV00000000), M. smegmatis MC2 155 (CP000480.1), Mycobacterium sp. JLS (CP000580.1), Mycobacterium sp. KMS (CP000518.1), Mycobacterium sp. MCS (CP000384.1), M. tuberculosis CDC1551 (AE000516.2), M. tuberculosis H37Ra (CP000611.1), M. tuberculosis H37Rv (AL123456.2), M. tuberculosis KZN 1435 (CP001658.1), M. ulcerans Agy99 (CP000325.1) and M. vanbaalenii PYR-1 (CP000511.1). (PDF 1 MB) Additional file 3: DNA sequence alignment GSK872 ic50 of conserved proteins in mycobacterial genomes. Sequences are from genomes of M. Selleck Osimertinib abscessus ATCC 19977 (CU458896.1), M. avium 104 (CP000479.1), M. avium subsp. paratuberculosis K10 (AE016958.1), M.

bovis subsp. bovis AF2122/97 (BX248333.1), M. bovis BCG Pasteur 1173P2 (AM408590.1), M. bovis BCG Tokyo 172 (AP010918.1), M. gilvum PYR-GCK (CP000656.1), M. intracellulare ATCC 13950 (ABIN00000000), M. kansasii ATCC 12478 (ACBV00000000), M. leprae Br4923 (FM211192.1), M. leprae TN (AL450380.1), M. marinum Mdivi1 M (CP000854.1), M. parascrofulaceum BAA-614 (ADNV00000000),

M. smegmatis MC2 155 (CP000480.1), Mycobacterium sp. JLS (CP000580.1), Mycobacterium sp. KMS (CP000518.1), Mycobacterium sp. MCS (CP000384.1), M. tuberculosis CDC1551 (AE000516.2), M. tuberculosis H37Ra (CP000611.1), M. tuberculosis H37Rv (AL123456.2), M. tuberculosis KZN 1435 (CP001658.1), M. ulcerans Agy99 (CP000325.1) and M. vanbaalenii PYR-1 (CP000511.1). (PDF 3 MB) References 1. Kazda J: The chronology of mycobacteria and the development of mycobacterial ecology. In The ecology of mycobacteria: Impact on animal’s and human’s health. Volume 1. Edited by: Kazda J, Pavlik I, Falkinham JO, Hruska K. Dordrecht Heidelberg London New York: Springer; 2009:1–11.CrossRef 2. Radomski N, Cambau E, Moulin L, Haenn S, Moilleron R, Lucas FS: Comparison of culture methods for isolation of nontuberculous

mycobacteria from surface waters. Appl Environ Thalidomide Microbiol 2010,76(11):3514–3520.PubMedCentralPubMedCrossRef 3. Adékambi T, Drancourt M: Dissection of phylogenetic relationships among 19 rapidly growing Mycobacterium species by 16S rRNA, hsp65, sodA, recA and rpoB gene sequencing. Int J Syst Evol Microbiol 2004,54(6):2095–2105.PubMedCrossRef 4. Gomila M, Ramirez A, Lalucat J: Diversity of environmental Mycobacterium isolates from hemodialysis water as shown by a multigene sequencing approach. Appl Environ Microbiol 2007,73(12):3787–3797.PubMedCentralPubMedCrossRef 5. Mendum TA, Chilima BZ, Hirsch PR: The PCR amplification of non-tuberculous mycobacterial 16S rRNA sequences from soil. FEMS Microbiol Lett 2000,185(2):189–192.PubMedCrossRef 6. Garcia-Quintanilla A, Gonzalez-Martin J, Tudo G, Espasa M, Jiménez de Anta MT: Simultaneous identification of Mycobacterium genus and Mycobacterium tuberculosis complex in clinical samples by 5′-exonuclease fluorogenic PCR. J Clin Microbiol 2002,40(12):4646–4651.PubMedCentralPubMedCrossRef 7.

Several might play a role in the pathology. For instance, we identified two oligopeptidases. One (prolyl-oligopeptidase) was previously shown to be secreted by T. cruzi [41] and presumed to facilitate the infection of host cells by degrading the collagen of the extracellular

JNK inhibitor matrix. Oligopeptidase B is secreted from T. brucei and T. congolense [42, 43]. This enzyme is able to cleave host peptide hormones such as atrial natriuretic factor [44], thus contributing to the increase in blood volume [45] and possibly to the disruption of the blood-brain barrier [46], both associated with the infection. Other symptoms of trypanosomiasis, such as the perturbation of the endocrine rhythms [47], could also involve oligopeptidase B. More generally, GW-572016 chemical structure it can be speculated that oligopeptidases, by cleaving regulatory peptides, could play pleiotropic roles in the pathogenic process developed during HAT. In contrast, for M20-M25-M40 and M17 family peptidases, where evidence also exists for their secretion by other organisms [48, 49], the identification in the secretome of Trypanosoma is novel and it is too early to speculate on its functions. Table 1 Diversity of peptidase families

found in the secretome of T. brucei gambiense bloodstream form and their distribution in other organisms. Families of Peptidases Distribution Serine peptidase family S9 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, Viruse Cysteine peptidase family C2 Bacteria, ———-, Protozoa, Fungi, Plants, Animals, ——– Cysteine

peptidase family C13 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Cysteine peptidase family C19 Bacteria, ———-, Protozoa, Fungi, Plants, Animals, Viruse Metallo-peptidase family M1 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Metallo-peptidase family M3 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Metallo-peptidase family M16 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, Viruse Metallo-peptidase http://www.selleck.co.jp/products/Neratinib(HKI-272).html family M17 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Metallo-peptidase family M20 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Metallo-peptidase family M24 Bacteria, Archaea, Protozoa, Fungi, Plants, Animals, ——– Metallo-peptidase family M32 Bacteria, Archaea, Protozoa, ——-, Plants, ———-, ——– Among metallopeptidases, the thimet oligopeptidase A is the first member of the M3 family to be identified in Protozoa. Thus, this protease, which processes neuropeptides in humans [50], may be a good candidate for a specific diagnostic marker. Another metallopeptidase in the secretome belongs to the M32 family, absent in selleck compound eukaryotic genomes other than trypanosomatids [51]. Although of unknown biological function, it might offer attractive drug targets against Trypanosoma.

However, the results are not statistically find more Ruxolitinib clinical trial different from those of the controls. It was also confirmed by incubating AuNPs with medium only and checking the absorption at a wavelength used for MTT assay that the presence of all tested AuNPs did not interfere with the assay. Figure 7 The effect of AuNPs on cell viability of MDA-MB-231 human breast cancer cells. MDA-MB-231 human breast cancer cells were treated with bio-AuNPs (A) or chem-AuNPs (B) at various concentrations from 0 to 100 μM/mL for 24 h, and cell viability was determined by

the MTT method. The results are expressed as the mean ± SD of three separate experiments, each of which contained three replicates. Treated groups were not statistically different from the control group based on the Student’s t test. Shukla et al. [59] suggested that AuNPs are not cytotoxic, reduce the production of reactive oxygen and nitrite species, and do not stimulate secretion of proinflammatory cytokines, such as TNF-alpha and IL1-beta, making them suitable candidates for nanomedicine. VS-4718 Using a human leukaemia cell line, gold nanospheres

of different sizes (4, 12, and 18 nm in diameter) and capping agents were found to be nontoxic based on the MTT assay [60]. Similarly, Arnida et al. [61] found that plain spherical AuNPs and PEGylated spheres and rods did not interfere with the proliferation of PC-3 cells when cells were exposed to as high as 1.5 nM of AuNPs for a period of over two population doubling times (88 h). Plain spherical particles that were 50 and 90 nm in diameter slightly stimulated the proliferation of PC-3 cells. Parab et al. [58] investigated the biocompatibility effect of sodium hexametaphosphate (HMP)-stabilized AuNPs (Au-HMPs) in tumor and fibroblast cells. Synthesized Au-HMP nanoparticles and their surface-modified

counterparts revealed non-cytotoxic properties in tested cells and showed biocompatibility. Mukherjee et al. [38] designed and developed an AuNP-based drug delivery system (DDS) (Au-DOX) containing doxorubicin (DOX). Administration of this DDS to breast cancer Liothyronine Sodium cells (MCF-7 and MDA-MB-231) showed significant inhibition of breast cancer cell proliferation compared with pristine doxorubicin. The viability of the bovine retinal pigment epithelial cells was not affected with an AuNP concentration of up to 300 nM, and increasing the concentrations above 300 nM resulted in significant cell death [62]. AuNPs have anti-oxidative and anti-hyperglycemic activities in streptozotocin-induced diabetic mice by balancing or inhibiting ROS generation in hyperglycemic conditions by scavenging free radicals and leading to increased anti-oxidant defense enzymes in mice.

Between the chromatographic www.selleckchem.com/products/VX-765.html relationships for the structures of α-adrenergic agonists and some antagonists optimized in aquatic environment,

similar dependencies were observed. Furthermore, BLZ945 mw for the Suplex column, a second parameter appears the TDM with R ~ 0.98. On the other hand, analyzing the relationships for the structures of only α-adrenergic agonists, n = 8, optimized in vacuo by PCM method in all cases the values of the regression coefficients R > 0.93 with only one independent variable. The most frequent parameter appeared isotropic polarizability (IPOL), R ~ 0.94 for the IAM column and R ~ 0.95 for the Spheri column. However, for the Suplex and Aluspher columns appeared electronic spatial extent (ESE), with R ~ 0.97 and ~0.93, respectively. Analyzing the dependencies of α-adrenergic agonists and log P two independent variables appeared only as a statistically significant parameters in vacuo: MAX_NEG and TDM, with the regression coefficient, R ~ 0.9, that could demonstrate the importance of bulkiness type parameters and associated dispersion interactions, to a lesser extent polar parameters. For the antihypertensive activity of agonists (pC25) and a relatively not too large number of cases (n = 8), relationship

with only one the independent variable—the lowest energy unoccupied molecular orbital (E_LUMO) with a regression coefficient value R ~ 0.87 in vacuo and R ~ 0.88 in the case of hydrated structures—was obtained. For the biological SSR128129E activity of antagonists (n = 11), statistically JQEZ5 research buy significant dependencies of pA2 (α1) in vivo and in vitro activity for the both hydrated and non-hydrated molecules were obtained. In the case of in vacuo structures with pA2 (α1)in

vivo as the first parameter appears HE and as the second the lowest energy unoccupied molecular orbital (E_LUMO), with R ~ 0.89, while with pA2 (α1)in vitro there is a the inverse order of the same parameters also with R ~ 0.89. On the other hand, In the case of hydrated structures with pA2 (α1)in vivo as the first parameter appears again HE and as the second the lowest energy unoccupied molecular orbital (E_LUMO), with R ~ 0.90, whereas with pA2 (α1)in vitro there is an inverse order of the same parameters also with R ~ 0.89, whereas as the first parameter appears the lowest energy unoccupied molecular orbital (E_LUMO) and as the second the HF, with R ~ 0.90. It can be concluded that for the parameters of the binding affinity of the receptor, a major role is played by E_LUMO energy orbitals, which may indicate the nature of the interactions between the drug molecule and receptor. It seems that the regression is mostly affected by the type of the dependent variable, and in fact the complexity of the phenomena affecting the measured value of this variable, as well as the uncertainty of measurement of the variable.

001) than those in the normal adjacent mucosa (Figure 1). Figure 1 Quantitative reverse transcription-PCR showed mRNA MS-275 nmr expression of ANKRD12 in CRC tumor tissues (T) and adjacent normal mucosa (N). ANKRD12 expression levels were lower in tumor tissue than in normal adjacent mucosa (p < 0.001, Student’s t test). Relationship between ANKRD12 mRNA expression and clinicopathological features The mRNA expression of the ANKRD12 was categorized as low or high in relation to the median value.

The experimental samples were divided into two groups [the selleck chemicals high ANKRD12 expression group (n = 34) and the low ANKRD12 expression group (n = 34)] to investigate ANKRD12 mRNA expression in association with clinicopathologic variables (Table 1). The ANKRD12 mRNA expression was not related to age, gender, histological see more type, depth of invasion(T), lymph node metastasis, tumor location. However, the incidence in liver metastasis was significantly higher (P = 0.015) in the low expression group (14 of 34, 41.2%) than in the high expression group (5

of 34, 14.7%), and the incidence of cancer death was significantly higher (P = 0.015) in the low expression group (22 of 34, 64.7%) than in the high expression group (12 of 34, 35.3%). Table 1 Clinicopathologic variables and ANKRD12 mRNA expression in 68 colorectal cancers Variables Expression P value   ANKRD12 high ANKRD12 low   (n = 34) (n = 34) Age 58.0 ± 15.0 61.6 ± 14.1 0.309 Sex     0.215 Male 18 23   Female 16 11   Histological type     0.793 Well, Moderate 23 24   Poor and others 11 10   Depth of invasion     0.380 T1,2,3 25 28   T4 9 GNA12 6   Location     0.086 Colon 23 16   Rectum 11 18   Lymph node metastasis     0.209 Absent 15 10   Present 19 24   Liver metastasis     0.015* Absent 29 20   Present 5 14   Cancer-related death     0.015* Alive 22 12   Death 12 22   n Number of patients, * <0.05. ANKRD12 mRNA expression and prognosis of CRC patients Overall survival

curves were plotted according to ANKRD12 mRNA expression by the Kaplan–Meier method. In the study group of CRC without liver metastasis (49 patients), the overall survival rate was significantly lower in the patients with low ANKRD12 mRNA expression than that in those with high expression (P = 0.041; Figure 2). Figure 2 Kaplan-Meier survival curves of CRC patients without liver metastasis according to the status of ANKRD12 expression. Patients with low ANKRD12 mRNA expression showed significantly poorer prognosis than those with high ANKRD12 mRNA expression (P = 0.041, log-rank test). Univariate analysis with Cox proportional hazards model identified four prognostic factors: location, lymph node metastasis, liver metastasis, and ANKRD12 expression. The other clinicopathological features, such as age, gender, histological type and depth of invasion were not statistically significant prognosis factors (Table 2).