Biology subjects

Arif, M.

Publications and source records attributed to Arif, M..

4 recordsLinked to original sources

bioRxiv2019

MOBN: an interactive database of multi-omics biological networks

It is essential to reveal the associations between different omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics and metabolomics as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their own data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and diseases research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

bioinformatics↗

bioRxiv2019

Discovery of functional alternatively spliced PKM transcripts in human cancers

The association of pyruvate kinase muscle type (PKM) with survival of cancer patients is controversial. Here, we focus on different transcripts of PKM and investigate the association between their mRNA expression and the clinical survival of the patients in 25 different cancers. We find that the transcript encoding PKM2, and three other functional transcripts are prognostic in multiple cancers. Our integrative analysis shows that the functions of these four transcripts are highly conservative in different cancers. Next, we validate the prognostic effect of these transcripts in an independent kidney renal clear-cell carcinoma (KIRC) cohort and identify a prognostic signature which could distinguish high- and low-risk KIRC patients. Finally, we reveal the functional role of alternatively spliced PKM transcripts in KIRC, and discover the protein products of different transcripts of PKM. Our analysis demonstrated that alternatively spliced transcripts of not only PKM but also other genes should be considered in cancer studies, since it may enable the discovery and targeting of the right protein product for development of the efficient treatment strategies.

systems biology↗

bioRxiv2019

Synergetic effect of non-complementary 5’ AT-rich sequences on the development of a multiplex TaqMan real-time PCR for specific and robust detection of Clavibacter michiganensis and C. michiganensis subsp. nebraskensis

Clavibacter is an agriculturally important genus comprising a single species, Clavibacter michiganensis, and multiple subspecies, including, C. michiganensis subsp. nebraskensis which causes Gosss wilt/blight of corn and accounts for high yield losses - listed among the five most significant diseases of corn in the United States of America. Our research objective was to develop a robust and rapid multiplex TaqMan real-time PCR (qPCR) to detect C. michiganensis in general and C. michiganensis subsp. nebraskensis with enhanced reliability and accuracy by adding non-complementary AT sequences to the 5 end of the forward and reverse primers. Comparative genomic analyses were performed to identify unique and conserved gene regions for primer and probe design. The unique genomic regions, ABC transporter ATP-binding protein CDS/ABC-transporter permease and MFS transporter were determined for specific detection of C. michiganensis and C. m. subsp. nebraskensis, respectively. The AT-rich sequences at the 5 position of the primers enhanced the reaction efficiency and sensitivity of rapid qPCR cycling; the reliability, accuracy and high efficiency of the developed assay was confirmed after testing with 59 strains from inclusivity and exclusivity panels - no false positives or false negatives were detected. The assays were also validated through naturally and artificially infected corn plant samples; all samples were detected for C. michiganensis and C. m. subsp. nebraskensis with 100% accuracy. The assay with 5 AT-rich sequences detected up to 10- and 100-fg of C. michiganensis and C. michiganensis subsp. nebraskensis genome targets, respectively. No adverse effect was observed when sensitivity assays were spiked with host genomic DNA. Addition of 5 AT rich sequences enhanced the qPCR reaction efficiency from 0.82 (M = -3.83) and 0.91 (M = -3.54) to 1.04 (with optimum slope value; M = -3.23) for both C. michiganensis and C. michiganensis subsp. nebraskensis, respectively; a increase of 10-fold sensitivity was also obtained with C. michiganensis primer set. The methodology proposed here can be used to optimize the reaction efficiency and to harmonize the diagnostic protocols which have prodigious applications in routine diagnostics, biosecurity and microbial forensics.

plant biology↗

bioRxiv2019

Spatiotemporal dissection of the cell cycle regulated human proteome

Cellular division is a fundamental source of cell-to-cell variability, and studies of transcript and protein abundances have revealed several hundred genes that are regulated by the cell cycle1-8. However, none of these studies provide single-cell resolution of protein expression, leaving an incomplete understanding of cell-to-cell heterogeneity and the roles of cycling transcripts and proteins. Here, we present the first comprehensive map of spatiotemporal heterogeneity of the human proteome by integrating proteomics at subcellular resolution, single-cell transcriptomics, and pseudotime measurements of individual cells within the cell cycle. We identify that 17% of the human proteome displays cell-to-cell variability, of which 26% is correlated to cell cycle progression, and we present the first evidence of cell cycle association for 235 proteins. Only 15% of proteomic cell cycle regulation is due to transcriptomic cycling, which points to other means of regulation such as post-translational modifications. For proteins regulated at the transcript level, we observe a 7.7 hour delay between peak expression of transcript and protein on average. This spatially resolved proteomic map of the cell cycle has been integrated into the Human Protein Atlas and serves as a valuable resource for accelerating molecular studies of the human cell cycle and cell proliferation.

cell biology↗