Biology subjects

Ananth Grama

Publications and source records attributed to Ananth Grama.

3 recordsLinked to original sources

bioRxiv2016

A convex optimization approach for identification of human tissue-specific interactomes

MotivationAnalysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell-type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/ selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.\n\nResultsWe present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (ESEA, GO Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state of the art techniques. Finally, using case studies of Alzheimers and Parkinsons diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.\n\nAvailabilityhttp://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Systems Biology↗

bioRxiv2015

A novel method to enhance the sensitivity of marker detection using a refined hierarchical prior of tissue similarities

MotivationIdentification of biochemical processes that drive the transformation of a totipotent cell into various cell types is essential to our understanding of living systems. This complex machinery determines how tissues differ in terms of their anatomy, physiology, morphology, and, more importantly, how various cellular control mechanisms contribute to the observed similarities/ differences. Tissue-selective genes orchestrate various aspects of cellular machinery in different tissues, and are known to be implicated in a number of tissue-specific pathologies.\n\nResultsWe propose a novel statistical approach that identifies and removes the effect of universally expressed genes in groups of tissues. This allows us to better characterize tissue similarities, as well as to identify tissue-selective genes. We use our method to construct a reliable hierarchy of tissue similarities. The groupings of tissues in this hierarchy are used to specify successively refined priors for identifying tissue-selective functions and their corresponding genes in the reduced subspace. We show that our refinement process enhances the signal-to-noise ratio in the identification of markers. Using case studies of immune cells and brain tissues, we show that our approach significantly outperforms the state-of-the-art methods, both in terms of coverage and reliability of the predicted tissue-selective genes.\n\nConclusionsOur statistical approach provides a general framework for enhancing the sensitivity of marker detection methods, which can be used in conjunction with other techniques. Even in cases where the number of available expression datasets is limited, we show that our marker detection method outperforms existing techniques. We present detailed validation on immune cells and brain tissues in this paper. Our approach can be applied to construct similar datasets of other human tissues as well, for identifying tissue-specific genes. We demonstrate how these tissue-selective genes enhance our understanding of differentiating biochemical features of brain tissues, shed light on how tissue-selective pathologies progress, and help us identify specific biomarkers and targets for future interventions.

Systems Biology↗

bioRxiv2014

On the scope and limitations of baker’s yeast as a model organism for studying human tissue-specific pathways

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue.\n\nWe propose a novel computational and statistical framework to systematically quantify the suitability of yeast as a model organism for different human tissues. We develop a computational method for dissecting the human interactome into tissue-specific cellular networks. Using these networks, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. We study these subspaces in detail, and relate them to the overall similarity of each tissue with yeast.\n\nMany complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. We show that human-specific subsets of tissue-selective genes are significantly associated with the onset and development of a number of pathologies. Consequently, they provide excellent candidates as drug targets for therapeutic interventions. We also present a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans.

Systems Biology↗