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Hussain, A.

Publications and source records attributed to Hussain, A..

3 recordsLinked to original sources

Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles.

To understand the molecular differences between right-sided colon cancer (RCC), left-sided colon cancer (LCC) and rectal cancer, we analyzed colorectal tumors at the DNA, RNA, miRNA and protein levels using previously sequenced data from The Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center. Clonal evolution analysis identified the same tumor-initiating events involving APC, KRAS and TP53 genes in RCC, LCC and rectal cancers. However, the individual role-played by each event, their order in tumor dynamics and selection of downstream mutations were distinct in all three anatomical locations, with some similarities noted between LCC and rectal cancer. We found a potentially targetable alteration APC R1450* specific to RCC that has not been previously described. Differential gene expression analysis revealed multiple genes within the homeobox, G-protein coupled receptor binding and transcription regulation families were dysregulated in RCC, LCC, and rectal cancers and may have a pathological role in these cancers. Further, using a novel in silico proteomic analytic tool developed by our research group, we found distinct central or hub proteins with unique interactomes in each location. Protein expression signatures were not necessarily concordant with the tumor profiles obtained at the DNA and RNA levels, underscoring the relevance of post-transcriptional events in defining the biology of these cancers beyond molecular changes at the DNA and/or RNA level. Ultimately, not only tumor location and the respective genomic profile but also protein-protein interactions will need to be taken into account to improve treatment outcomes of colorectal cancers. Further studies that take into account the alterations found in this study may help in developing more tailored, and perhaps more effective, treatment strategies.\n\nAuthor summaryPatients with right-sided colon cancer (RCC) has a worse prognosis compared to left-sided colon cancer (LCC). Recent data has also shown that wild-type RAS metastatic RCCs have poor outcomes when treated with the combination of chemotherapy and anti-EGFR therapy compared to LCC and rectal cancers. Therefore, There is an urgent unmet need to understand the molecular differences between RCC, LCC, and rectal cancers. In this study, we demonstrate clonal evolutionary trajectory and the order of mutations in RCC, LCC, and rectal cancers are distinct with some similarities between LCC and rectal cancers. The order of the mutations that lead to the acquisition of crucial driver alterations may have prognostic and therapeutic implications. We also discovered a novel targetable alteration, APC R1450* to be significantly enriched in early, late and metastatic RCC but not in LCC and rectal cancers. Amazingly, proteomic signatures were discordant with DNA and RNA levels. These distinct differences in DNA, RNA and post-transcriptional events may contribute to their unique clinicopathological features.\n\nConflict of Interest StatementAshiq Masood Advisory board and speaker Bureau Bristol-Myers Squibb and Boehringer Ingelheim\n\nJanakiraman Subramanian Advisory board - Astra Zeneca, Pfizer, Boehringer Ingelheim, Alexion, Paradigm, Bristol-Myers Squibb Speakers Bureau - Astra Zeneca, Boehringer Ingelheim, Lilly Research Support - Biocept and Paradigm\n\nArif Hussain Advisory board - Novartis, Bayer, Astra Zeneca Consultant - Bristol-Myers-Squibb All other authors have no conflict of interest.

genomics

An Automated Workflow for Segmenting Single Adult Cardiac Cells from Large-Volume Serial Block-Face Scanning Electron Microscopy Data

This paper presents a new algorithm to automatically segment the myofibrils, mitochondria and nuclei within single adult cardiac cells that are part of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The algorithm only requires a set of manually drawn contours that roughly demarcate the cell boundary at routine slice intervals (every 50th, for example). The algorithm correctly classified pixels within the single cell with 97% accuracy when compared to manual segmentations. One entire cell and the partial volumes of two cells were segmented. Analysis of segmentations within these cells showed that myofibrils and mitochondria occupied 47.5% and 51.6% on average respectively, while the nuclei occupy 0.7% of the cell for which the entire volume was captured in the SBF-SEM dataset. Mitochondria clustering increased at the periphery of the nucleus region and branching points of the cardiac cell. The segmentations also showed high area fraction of mitochondria (up to 70% of the 2D image slice) in the sub-sarcolemmal region, whilst it was closer to 50% in the intermyofibrillar space. We finally demonstrate that our segmentations can be turned into 3D finite element meshes for cardiac cell computational physiology studies. We offer our large dataset and MATLAB implementation of the algorithm for research use at www.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/. We anticipate that this timely tool will be of use to cardiac computational and experimental physiologists alike who study cardiac ultrastructure and its role in heart function.

bioengineering

Transcriptomic Dynamics of a non-coding trinucleotide repeat expansion disorder SCA12 in iPSC derived neuronal cells: signatures of interferon induced response

Spinocerebellar ataxia type-12 (SCA12) is a neurological disorder that exhibits a unique progressive tremor/ataxia syndrome induced by triplet (CAG) repeat expansion in 5 UTR of PPP2R2B. SCA12 is one of the most prominent SCA-subtype in India and till date no appropriate disease models have been described. Our aim was to establish human iPSC derived neuronal cell lines of SCA12 and study transcriptomic level alterations induced by CAG expansion. For translational application, peripheral blood transcriptomics of SCA12 patients was also performed. Lymphoblastoid cell lines of three SCA12 patients were reprogrammed to iPSCs and then re-differentiated into pan-neuronal lineage. RNA-sequencing based comparative transcriptomics was performed for disease and control cell lineages. Microarray based transcriptomic profiling of peripheral blood of SCA12 patients was performed in a case/control (n=15/9) design. We have successfully created human neuronal cell lines of SCA12 patient as exhibited by their molecular profiling. Differential expression analysis of RNA-Seq data has shown enrichment for type-I interferon signaling and other relevant cellular processes in SCA12-neurons. At the splice-isoform level, we observed an upregulation of expanded CAG containing non-coding transcript of PPP2R2B. Peripheral blood transcriptomics analysis and targeted validation of RNA-Seq data has allowed us to identify inflammatory signatures as potential markers of molecular pathology in SCA12. Our study has allowed us to establish first iPSC based neuronal cell lines of SCA12. We have identified pro-inflammatory signatures in SCA12-neurons suggestive of a dsRNA mediated activation of interferon signaling and that corroborates with the emerging evidence of neuronal atrophy due to neuro-inflammation in common neurodegenerative diseases. This study involved development of an iPSCs derived neuronal cells of SCA12 and look through signatures of neurodegeneration by whole RNA sequencing. This model sheds light upon key role of RNA mediated induced response in Interferon signaling for neurodegeneration.

molecular biology