Biology subjects

Bagheri, M.

Publications and source records attributed to Bagheri, M..

3 recordsLinked to original sources

bioRxiv2020

Limiting Self-Renewal of the Basal Compartment Induces Differentiation and Alters Evolution of Mammary Tumors

Differentiation therapy is an approach that utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift towards terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have proven challenging. In this study we show that activation of PKA drives aberrant mammary differentiation by diminishing the self-renewing potential of the basal compartment. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. PKA activation represents a promising approach as an adjuvant to chemotherapy for certain breast cancers, reviving the paradigm of differentiation therapy for solid tumors.

cancer biology↗

bioRxiv2019

Self-Incompatibility Alleles in Iranian Pear Cultivars

In the present study, the S-alleles of eighteen pear cultivars, (including fourteen cultivars planted commercially in Iran and four controls) are determined. 34 out of 36 S-alleles are detected using nine allele-specific primers, which are designed for amplification of S101/S102, S105, S106, S107, S108, S109, S111, S112 and S114, as well as consensus primers, PycomC1F and PycomC5R. S104, S101 and S105 were the most common S-alleles observed, respectively, in eight, seven and six cultivars. In 16 cultivars, ( Bartlett (S101S102), Beurre Giffard (S101S106), Comice (S104S105), Doshes (S104S107), Koshia (S104S108), Paskolmar (S101S105), Felestini (S101S107), Domkaj (S104S120), Ghousi (S104S107), Kaftar Bache (S104S120), Konjoni (S104S108), Laleh (S105S108), Natanzi (S104S105), Sebri (S101S104), Se Fasleh (S101S105) and Louise Bonne (S101S108)) both alleles are identified but in two cultivars, ( Pighambari (S105) and Shah Miveh Esfahan (S107)) only one allele is recognized. It is concluded that allele-specific PCR amplification can be considered as an efficient and rapid method to identify S-genotype of Iranian pear cultivars.

molecular biology↗

bioRxiv2019

Taxonomically informed scoring enhances confidence in natural products annotation

Mass spectrometry (MS) hyphenated to liquid chromatography (LC)-MS offers unrivalled sensitivity for metabolite profiling of complex biological matrices encountered in natural products (NP) research. With advanced platforms LC, MS/MS spectra are acquired in an untargeted manner on most detected features. This generates massive and complex sets of spectral data that provide valuable structural information on most analytes. To interpret such datasets, computational methods are mandatory. To this extent, computerized annotation of metabolites links spectral data to candidate structures. When profiling complex extracts spectra are often organized in clusters by similarity via Molecular Networking (MN). A spectral matching score is usually established between the acquired data and experimental or theoretical spectral databases (DB). The process leads to various candidate structures for each MS features. At this stage, obtaining high annotation confidence level remains a challenge notably due to the high chemodiversity of specialized metabolomes.\n\nThe integration of additional information in a meta-score is a way to capture complementary experimental attributes and improve the annotation process. Here we show that integrating unambiguous taxonomic position of analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and weight the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5 to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata (particularly biological sources) is timely and critical for the NP research community.

bioinformatics↗