Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics - Gajbhiye, Akshada; Dabhi, Raju; Taunk, Khushman; Vannuruswamy, Garikapati; RoyChoudhury, Sourav; Adhav, Ragini; Seal, Shubhendu; Mane, Anupama; Bayatigeri, Santhakumari; Santra, Manas K.; Chaudhury, Koel; Rapole, Srikanth (Proteomics, 2016)
Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel-based and gel-free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D-DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM-based validation in a separate cohort testified a panel of 21 proteins such as zinc-alpha2-glycoprotein, A2GL, retinol-binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and alpha 1-antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.
Keywords: 2D-DIGE; Biomedicine; Breast cancer; HER2 enriched; SWATH; Urinary biomarkers

Transcriptomics analysis of propiconazole-treated Cochliobolus sativus reveals new putative azole targets in the plant pathogen - Somani, Deepika; Adhav, Ragini; Prashant, Ramya; Kadoo, Narendra Y. (Functional & Integrative Genomics, 2019)
Cochliobolus sativus (anamorph: Bipolaris sorokiniana) is a filamentous fungus from the class Dothideomycetes. It is a pathogen of cereals including wheat and barley, and causes foliar spot blotch, root rot, black point on grains, head blight, leaf blight, and seedling blight diseases. Annual yields of these economically important cereals are severely reduced due to this pathogen attack. Evolution of fungicide resistant pathogen strains, availability of a limited number of potent antifungal compounds, and their efficacy are the acute issues in field management of phytopathogenic fungi. Propiconazole is a widely used azole fungicide to control the disease in fields. The known targets of azoles are the demethylase enzymes involved in ergosterol biosynthesis. Nonetheless, azoles have multiple modes of action, some of which have not been explored yet. Identifying the off-target effects of fungicides by dissecting gene expression profiles in response to them can provide insights into their modes of action and possible mechanisms of fungicide resistance. Moreover it can also reveal additional targets for development of new fungicides. Hence, we analyzed the global gene expression profile of C. sativus on exposure to sub-lethal doses of propiconazole in a time series. The gene expression patterns were confirmed using quantitative reverse transcriptase PCR (qRT-PCR). This study revealed overexpression of target genes from the sterol biosynthesis pathway supporting the reported mode of resistance against azoles. In addition, some new potential targets have also been identified, which could be explored to develop new fungicides and plant protection strategies.
Keywords: Bipolaris sorokiniana; Cochliobolus sativus; Fungicide resistance; Propiconazole; RNA-seq; Transcriptomics analysis