scholarly journals Genome Resolved Biogeography of Mamiellales

Genes ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 66
Author(s):  
Jade Leconte ◽  
L. Felipe Benites ◽  
Thomas Vannier ◽  
Patrick Wincker ◽  
Gwenael Piganeau ◽  
...  
Keyword(s):  
Sexual Reproduction ◽  
Single Gene ◽  
Marine Phytoplankton ◽  
Taxonomic Resolution ◽  
Mating Types ◽  
Gene Markers ◽  
High Prevalence ◽  
Different Temperatures ◽  
Micromonas Pusilla ◽  
Ostreococcus Lucimarinus

Among marine phytoplankton, Mamiellales encompass several species from the genera Micromonas, Ostreococcus and Bathycoccus, which are important contributors to primary production. Previous studies based on single gene markers described their wide geographical distribution but led to discussion because of the uneven taxonomic resolution of the method. Here, we leverage genome sequences for six Mamiellales species, two from each genus Micromonas, Ostreococcus and Bathycoccus, to investigate their distribution across 133 stations sampled during the Tara Oceans expedition. Our study confirms the cosmopolitan distribution of Mamiellales and further suggests non-random distribution of species, with two triplets of co-occurring genomes associated with different temperatures: Ostreococcus lucimarinus, Bathycoccus prasinos and Micromonas pusilla were found in colder waters, whereas Ostreococcus spp. RCC809, Bathycoccus spp. TOSAG39-1 and Micromonas commoda were more abundant in warmer conditions. We also report the distribution of the two candidate mating-types of Ostreococcus for which the frequency of sexual reproduction was previously assumed to be very low. Indeed, both mating types were systematically detected together in agreement with either frequent sexual reproduction or the high prevalence of a diploid stage. Altogether, these analyses provide novel insights into Mamiellales’ biogeography and raise novel testable hypotheses about their life cycle and ecology.

scholarly journals The PI3K signaling pathway as a pharmacological target in Autism related disorders and Schizophrenia

2016 ◽  
Author(s):  
Lilian Enriquez-barreto ◽  
Miguel Morales
Keyword(s):  
Signaling Pathway ◽  
Single Gene ◽  
Gene Mutations ◽  
Pi3k Pathway ◽  
Synaptic Function ◽  
Pi3k Signaling ◽  
Gene Markers ◽  
High Prevalence ◽  
Synaptic Formation ◽  
Pi3k Signaling Pathway

This review is focused in PI3K’s involvement in two widespread mental disorders: Autism and Schizophrenia. A largebody of evidence points to synaptic dysfunction as a cause of these diseases, either during the initial phases ofbrain synaptic circuit’s development or later modulating synaptic function and plasticity. Autism related disordersand Schizophrenia are complex genetic conditions in which the identification of gene markers has proved difficult,although the existence of single-gene mutations with a high prevalence in both diseases offers insight into the roleof the PI3K signaling pathway. In the brain, components of the PI3K pathway regulate synaptic formation and plasticity;thus, disruption of this pathway leads to synapse dysfunction and pathological behaviors. Here, we recapitulate recentevidences that demonstrate the imbalance of several PI3K elements as leading causes of Autism and Schizophrenia,together with the plausible new pharmacological paths targeting this signaling pathway.

Identification of biosynthetic intermediates for the mating hormone α2 of the plant pathogen Phytophthora

2021 ◽  
Author(s):  
Suguru Ariyoshi ◽  
Yusuke Imazu ◽  
Ryuji Ohguri ◽  
Ryo Katsuta ◽  
Arata Yajima ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Sexual Reproduction ◽  
Mating Type ◽  
Plant Pathogen ◽  
Type Strain ◽  
Mating Types ◽  
Synthetic Intermediates ◽  
The Cross

Abstract The heterothallic group of the plant pathogen Phytophthora can sexually reproduce between the cross-compatible mating types A1 and A2. The mating hormone α2, produced by A2 mating type and utilized to promote the sexual reproduction of the partner A1 type, is known to be biosynthesized from phytol. In this study, we identified two biosynthetic intermediates, 11- and 16-hydroxyphytols (1 and 2), for α2 by administering the synthetic intermediates to an A2 type strain to produce α2 and by administering phytol to A2 strains to detect the intermediates in the mycelia. The results suggest that α2 is biosynthesized by possibly two cytochrome P450 oxygenases via two hydroxyphytol intermediates (1 and 2) in A2 hyphae and secreted outside.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Gamete signalling underlies the evolution of mating types and their number

2016 ◽  
Vol 371 (1706) ◽  
pp. 20150531 ◽  
Author(s):  
Zena Hadjivasiliou ◽  
Andrew Pomiankowski
Keyword(s):  
Mathematical Model ◽  
Sexual Reproduction ◽  
Mating Types ◽  
Simple Mathematical Model ◽  
Unicellular Eukaryotes ◽  
Evolutionarily Stable

The gametes of unicellular eukaryotes are morphologically identical, but are nonetheless divided into distinct mating types. The number of mating types varies enormously and can reach several thousand, yet most species have only two. Why do morphologically identical gametes need to be differentiated into self-incompatible mating types, and why is two the most common number of mating types? In this work, we explore a neglected hypothesis that there is a need for asymmetric signalling interactions between mating partners. Our review shows that isogamous gametes always interact asymmetrically throughout sex and argue that this asymmetry is favoured because it enhances the efficiency of the mating process. We further develop a simple mathematical model that allows us to study the evolution of the number of mating types based on the strength of signalling interactions between gametes. Novel mating types have an advantage as they are compatible with all others and rarely meet their own type. But if existing mating types coevolve to have strong mutual interactions, this restricts the spread of novel types. Similarly, coevolution is likely to drive out less attractive mating types. These countervailing forces specify the number of mating types that are evolutionarily stable. This article is part of the themed issue ‘Weird sex: the underappreciated diversity of sexual reproduction’.

scholarly journals Genomic and Population Analyses of the Mating Type Loci in Coccidioides Species Reveal Evidence for Sexual Reproduction and Gene Acquisition

2007 ◽  
Vol 6 (7) ◽  
pp. 1189-1199 ◽  
Author(s):  
M. Alejandra Mandel ◽  
Bridget M. Barker ◽  
Scott Kroken ◽  
Steven D. Rounsley ◽  
Marc J. Orbach
Keyword(s):  
Sexual Reproduction ◽  
Mating Type ◽  
Mating Types ◽  
Coccidioides Immitis ◽  
Genome Sequences ◽  
Valley Fever ◽  
Population Analyses ◽  
Gene Acquisition ◽  
Close Proximity

ABSTRACT Coccidioides species, the fungi responsible for the valley fever disease, are known to reproduce asexually through the production of arthroconidia that are the infectious propagules. The possible role of sexual reproduction in the survival and dispersal of these pathogens is unexplored. To determine the potential for mating of Coccidioides, we analyzed genome sequences and identified mating type loci characteristic of heterothallic ascomycetes. Coccidioides strains contain either a MAT1-1 or a MAT1-2 idiomorph, which is 8.1 or 9 kb in length, respectively, the longest reported for any ascomycete species. These idiomorphs contain four or five genes, respectively, more than are present in the MAT loci of most ascomycetes. Along with their cDNA structures, we determined that all genes in the MAT loci are transcribed. Two genes frequently found in common sequences flanking MAT idiomorphs, APN2 and COX13, are within the MAT loci in Coccidioides, but the MAT1-1 and MAT1-2 copies have diverged dramatically from each other. Data indicate that the acquisition of these genes in the MAT loci occurred prior to the separation of Coccidioides from Uncinocarpus reesii. An analysis of 436 Coccidioides isolates from patients and the environment indicates that in both Coccidioides immitis and C. posadasii, there is a 1:1 distribution of MAT loci, as would be expected for sexually reproducing species. In addition, an analysis of isolates obtained from 11 soil samples demonstrated that at three sampling sites, strains of both mating types were present, indicating that compatible strains were in close proximity in the environment.

Biofilm Formation and Morphotypes of Salmonella enterica subsp. arizonae Differs from Those of Other Salmonella enterica Subspecies in Isolates from Poultry Houses

2016 ◽  
Vol 79 (7) ◽  
pp. 1127-1134 ◽  
Author(s):  
A. LAMAS ◽  
I. C. FERNANDEZ-NO ◽  
J. M. MIRANDA ◽  
B. VÁZQUEZ ◽  
A. CEPEDA ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Biofilm Formation ◽  
Public Health Problem ◽  
Nutrient Concentrations ◽  
Cellulose Production ◽  
Biofilm Production ◽  
Poultry Houses ◽  
High Prevalence ◽  
Different Temperatures ◽  
Strain Dependent

ABSTRACT Salmonella serovars are responsible for foodborne diseases around the world. The ability to form biofilms allows microorganisms to survive in the environment. In this study, 73 Salmonella strains, belonging to four different subspecies, were isolated from poultry houses and foodstuffs and tested. Biofilm formation was measured at four different temperatures and two nutrient concentrations. Morphotypes and cellulose production were evaluated at three different temperatures. The presence of several genes related to biofilm production was also examined. All strains and subspecies of Salmonella had the ability to form biofilms, and 46.57% of strains produced biofilms under all conditions tested. Biofilm formation was strain dependent and varied according to the conditions. This is the first study to analyze biofilm formation in a wide number of Salmonella enterica subsp. arizonae strains, and no direct relationship between the high prevalence of Salmonella enterica subsp. arizonae strains and their ability to form biofilm was established. Morphotypes and cellulose production varied as the temperature changed, with 20°C being the optimum temperature for expression of the red, dry, and rough morphotype and cellulose. Salmonella enterica subsp. arizonae, whose morphotype is poorly studied, only showed a smooth and white morphotype and lacked the csgD and gcpA genes that are implicated in biofilm production. Thus, Salmonella biofilm formation under different environmental conditions is a public health problem because it can survive and advance through the food chain to reach the consumer.

scholarly journals Invasion and extinction dynamics of mating types under facultative sexual reproduction

2019 ◽  
Author(s):  
Peter Czuppon ◽  
George W. A. Constable
Keyword(s):  
Sexual Reproduction ◽  
Mating Type ◽  
Average Rate ◽  
Natural Populations ◽  
Null Model ◽  
Mating Types ◽  
Stationary Probability Distribution ◽  
Time To Extinction ◽  
Establishment Probability ◽  
Mean Time

AbstractIn sexually reproducing isogamous species, syngamy between gametes is generally not indiscriminate, but rather restricted to occurring between complementary self-incompatible mating types. A longstanding question regards the evolutionary pressures that control the number of mating types observed in natural populations, which ranges from two to many thousands. Here, we describe a population genetic null model of this reproductive system and derive expressions for the stationary probability distribution of the number of mating types, the establishment probability of a newly arising mating type and the mean time to extinction of a resident type. Our results yield that the average rate of sexual reproduction in a population correlates positively with the expected number of mating types observed. We further show that the low number of mating types predicted in the rare-sex regime is primarily driven by low invasion probabilities of new mating type alleles, with established resident alleles being very stable over long evolutionary periods. Moreover, our model naturally exhibits varying selection strength dependent on the number of present mating types. This results in higher extinction and lower invasion rates for an increasing number of residents.

scholarly journals Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2927 ◽  
Author(s):  
Linh Nguyen ◽  
Cuong C Dang ◽  
Pedro J. Ballester
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cell Line ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Single Gene ◽  
Cancer Cell Line ◽  
Expression Data ◽  
Gene Markers ◽  
Pan Cancer

Background:Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.Methods:Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation.Results and Discussion:Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.Conclusions:We now know that this type of models can predictin vitrotumour response to these drugs. These models can thus be further investigated onin vivotumour models.

scholarly journals Machine learning models to predictin vivodrug response via optimal dimensionality reduction of tumour molecular profiles

2018 ◽  
Author(s):  
Linh Nguyen ◽  
Stefan Naulaerts ◽  
Alexandra Bomane ◽  
Alejandra Bruna ◽  
Ghita Ghislat ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Single Gene ◽  
Response Prediction ◽  
Gene Marker ◽  
Data Sets ◽  
Precision Oncology ◽  
Gene Markers ◽  
Combining Data ◽  
Gene Alterations

ABSTRACTInter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

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