Higher thermal and ethanol tolerance of a yeast strain isolated from oral cavity

Efficient bioethanol producing microorganisms must be endowed with peculiar physiological and technological traits, such as, higher thermal and ethanol tolerance. This encompasses a strain thermotolerance and an ability to grow at elevated sugar and ethanol concentration or ability to sustain dehydration process such as freeze drying. In this study, we characterized a thermotolerant yeast strain isolated from the human oral cavity regarding the above mentioned parameters. Such an uncommon niche was considered as the great potential reservoir, to isolate strains endowed with metabolic aptitudes requested for ethanol production. In the process, we have defined the YTerm-1strain ability to sustain high sugar and ethanol concentration that are two technological constrains in bioethanol production. Finally, we highlighted that the strain YTerm-1 was able to accumulate, at a high level, trehalose and β-glucan, two compounds conferring the cells an increased resistance to freeze drying process and osmotic stress. Our results suggest that the Yterm-1 strain showed a better growth ability and higher ethanol yield as compared to the industrial strain. Other metabolic traits, such as resistance to dehydration stress, tolerance to ethanol, accumulation of intracellular trehalose or membrane β-glucan confer to that isolate all the characteristics requested in industrial production of ethanol

Combined in situ XRF–LIBS analyses as a novel method to determine the provenance of central Mediterranean obsidians

This work presents a new calibration method for determining the provenance of obsidian artefacts based on the combined use of XRF and laser-induced breakdown spectroscopy (LIBS). At first, obsidian samples collected from the main Mediterranean sources were characterized using portable XRF and LIBS systems. After data treatment, elemental information was used to carry out principal component analysis (PCA) for each technique. Rb, Sr, Zr, Y and Fe elements, detected by using XRF, were found to be the key parameters enabling obsidians discrimination. Likewise, LIBS data helped differentiating the analysed patterns by the intensity of their main elemental components (Ca, Al, Mg and K). After selecting the key parameters detected by each technique, a new data matrix combining XRF and LIBS data was finally built. According to PCA results, the discrimination of Mediterranean sources based on combined XRF–LIBS data ensured a higher reliability over mono-analytical models, by increasing the Euclidean distance between sources projections over three-dimensional principal components plots. Knowing that the representativeness of elemental data could be compromised by the presence of superficial degradation products or deposition patinas, a shot-to-shot comparison of in-depth LIBS analyses is finally proposed as a method to disclose whether the spot under analysis was superficially contaminated or altered. Thus, the proposed strategy based on the combined use of portable XRF and LIBS spectrometers could be particularly useful for the in situ analysis of obsidian artefacts that underwent superficial alteration or could be covered by patina products.This work has been financially supported by the DEMORA project (Grant No. PID2020-113391GB-I00), funded by the Spanish Agency for Research (through the Spanish Ministry of Science and Innovation (Grant No. BIA2017-870´63-P), MICINN, and the European Regional Development Fund (Grant No. BIA2017-870´63-P), FEDER). I. Costantini gratefully acknowledges to the UPV/EHU for her postdoctoral contract. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Adaptations to Climate-Mediated Selective Pressures in Humans

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans.</p

Serum Thioredoxin-80 is associated with age, ApoE4, and neuropathological biomarkers in Alzheimer’s disease: a potential early sign of AD

[EN] Background: Thioredoxin-80 (Trx80) is a cleavage product from the redox-active protein Thioredoxin-1 and has been previously described as a pro-inflammatory cytokine secreted by immune cells. Previous studies in our group reported that Trx80 levels are depleted in Alzheimer's disease (AD) brains. However, no studies so far have investigated peripheral Trx80 levels in the context of AD pathology and whether could be associated with the main known AD risk factors and biomarkers. Methods: Trx80 was measured in serum samples from participants from two different cohorts: the observational memory clinic biobank (GEDOC) (N = 99) with AD CSF biomarker data was available and the population-based lifestyle multidomain intervention trial Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (N = 47), with neuroimaging data and blood markers of inflammation available. The GEDOC cohort consists of participants diagnosed with subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD, whereas the FINGER participants are older adults at-risk of dementia, but without substantial cognitive impairment. One-way ANOVA and multiple comparison tests were used to assess the levels of Trx80 between groups. Linear regression models were used to explore associations of Trx80 with cognition, AD CSF biomarkers (A beta 42, t-tau, p-tau and p-tau/t-tau ratio), inflammatory cytokines, and neuroimaging markers. Results: In the GEDOC cohort, Trx80 was associated to p-tau/t-tau ratio in the MCI group. In the FINGER cohort, serum Trx80 levels correlated with lower hippocampal volume and higher pro-inflammatory cytokine levels. In both GEDOC and FINGER cohorts, ApoE4 carriers had significantly higher serum Trx80 levels compared to non-ApoE4 carriers. However, Trx80 levels in the brain were further decreased in AD patients with ApoE4 genotype. Conclusion: We report that serum Trx80 levels are associated to AD disease stage as well as to several risk factors for AD such as age and ApoE4 genotype, which suggests that Trx80 could have potential as serum AD biomarker. Increased serum Trx80 and decreased brain Trx80 levels was particularly seen in ApoE4 carriers. Whether this could contribute to the mechanism by which ApoE4 show increased vulnerability to develop AD would need to be further investigated.Open access funding provided by Karolinska Institute. This research was supported by the Margaretha af Ugglas Foundation, the Karolinska institutet KID funding, Gun och Bertil Stohnes Stiftelse, Stiftelsen Syskonen Svenssons, the Karolinska Institutet fund for geriatric research Stiftelsen Gamla Tjanarinnor, and the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet

Assessing Associations between the AURKA-HMMR-TPX2-TUBG1 Functional Module and Breast Cancer Risk in BRCA1/2 Mutation Carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood approach. The association of HMMR rs299290 with breast cancer risk in BRCA1 mutation carriers was confirmed: per-allele hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.04-1.15, p = 1.9 x 10(-4) (false discovery rate (FDR)-adjusted p = 0.043). Variation in CSTF1, located next to AURKA, was also found to be associated with breast cancer risk in BRCA2 mutation carriers: rs2426618 per-allele HR = 1.10, 95% CI 1.03-1.16, p = 0.005 (FDR-adjusted p = 0.045). Assessment of pairwise interactions provided suggestions (FDR-adjusted pinteraction values > 0.05) for deviations from the multiplicative model for rs299290 and CSTF1 rs6064391, and rs299290 and TUBG1 rs11649877 in both BRCA1 and BRCA2 mutation carriers. Following these suggestions, the expression of HMMR and AURKA or TUBG1 in sporadic breast tumors was found to potentially interact, influencing patients' survival. Together, the results of this study support the hypothesis of a causative link between altered function of AURKA-HMMR-TPX2-TUBG1 and breast carcinogenesis in BRCA1/2 mutation carriers

Modification of BRCA1-associated breast cancer risk by HMMR overexpression

Breast cancer risk for carriers of BRCA1 pathological variants is modified by genetic factors. Genetic variation in HMMR may contribute to this effect. However, the impact of risk modifiers on cancer biology remains undetermined and the biological basis of increased risk is poorly understood. Here, we depict an interplay of molecular, cellular, and tissue microenvironment alterations that increase BRCA1-associated breast cancer risk. Analysis of genome-wide association results suggests that diverse biological processes, including links to BRCA1-HMMR profiles, influence risk. HMMR overexpression in mouse mammary epithelium increases Brca1-mutant tumorigenesis by modulating the cancer cell phenotype and tumor microenvironment. Elevated HMMR activates AURKA and reduces ARPC2 localization in the mitotic cell cortex, which is correlated with micronucleation and activation of cGAS-STING and non-canonical NF-kappa B signaling. The initial tumorigenic events are genomic instability, epithelial-to-mesenchymal transition, and tissue infiltration of tumor-associated macrophages. The findings reveal a biological foundation for increased risk of BRCA1-associated breast cancer. The effect of hyaluronan-mediated motility receptor (HMMR) expression in BRCA1-associated breast cancer risk remains unknown. Here, HMMR overexpression induces the activation of cGAS-STING and non-canonical NF-kappa B signalling, instigating an immune permissive environment for breast cancer development

Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patientderived xenografts (PDXs), we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2) is associated with this process. A triple-negative BRCA1-mutaied PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/ genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA) supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance

Study of breast cancer incidence in patients of lymphangioleiomyomatosis

The online version of this article (doi:10.1007/s10549-016-3737-8) contains supplementary material, which is available to authorized user

Modern applications of machine learning in quantum sciences

In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.Comment: 268 pages, 87 figures. Comments and feedback are very welcome. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Note