Anitha Kannan

Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Comparative Proteomics Profiling of a Phospholamban Mutant Mouse Model of Dilated Cardiomyopathy Reveals Progressive Intracellular Stress Responses

Defective mobilization of Ca2+ by cardiomyocytes can lead to cardiac insufficiency, but the causative mechanisms leading to congestive heart failure (HF) remain unclear. In the present study we performed exhaustive global proteomics surveys of cardiac ventricle isolated from a mouse model of cardiomyopathy overexpressing a phospholamban mutant, R9C (PLN-R9C), and exhibiting impaired Ca2+ handling and death at 24 weeks and compared them with normal control littermates. The relative expression patterns of 6190 high confidence proteins were monitored by shotgun tandem mass spectrometry at 8, 16, and 24 weeks of disease progression. Significant differential abundance of 593 proteins was detected. These proteins mapped to select biological pathways such as endoplasmic reticulum stress response, cytoskeletal remodeling, and apoptosis and included known biomarkers of HF (e.g. brain natriuretic peptide/atrial natriuretic factor and angiotensin-converting enzyme) and other indicators of presymptomatic functional impairment. These altered proteomic profiles were concordant with cognate mRNA patterns recorded in parallel using high density mRNA microarrays, and top candidates were validated by RT-PCR and Western blotting. Mapping of our highest ranked proteins against a human diseased explant and to available data sets indicated that many of these proteins could serve as markers of disease. Indeed we showed that several of these proteins are detectable in mouse and human plasma and display differential abundance in the plasma of diseased mice and affected patients. These results offer a systems-wide perspective of the dynamic maladaptions associated with impaired Ca2+ homeostasis that perturb myocyte function and ultimately converge to cause HF.

Active objects: actions for entity-centric search

We introduce an entity-centric search experience, called Active Objects, in which entity-bearing queries are paired with actions that can be performed on the entities. For example, given a query for a specific flashlight, we aim to present actions such as reading reviews, watching demo videos, and finding the best price online. In an annotation study conducted over a random sample of user query sessions, we found that a large proportion of queries in query logs involve actions on entities, calling for an automatic approach to identifying relevant actions for entity-bearing queries. In this paper, we pose the problem of finding actions that can be performed on entities as the problem of probabilistic inference in a graphical model that captures how an entity bearing query is generated. We design models of increasing complexity that capture latent factors such as entity type and intended actions that determine how a user writes a query in a search box, and the URL that they click on. Given a large collection of real-world queries and clicks from a commercial search engine, the models are learned efficiently through maximum likelihood estimation using an EM algorithm. Given a new query, probabilistic inference enables recommendation of a set of pertinent actions and hosts. We propose an evaluation methodology for measuring the relevance of our recommended actions, and show empirical evidence of the quality and the diversity of the discovered actions.

Recognizing proxemics in personal photos

Proxemics is the study of how people interact. We present a computational formulation of visual proxemics by attempting to label each pair of people in an image with a subset of physically based “touch codes.” A baseline approach would be to first perform pose estimation and then detect the touch codes based on the estimated joint locations. We found that this sequential approach does not perform well because pose estimation step is too unreliable for images of interacting people, due to difficulties with occlusion and limb ambiguities. Instead, we propose a direct approach where we build an articulated model tuned for each touch code. Each such model contains two people, connected in an appropriate manner for the touch code in question. We fit this model to the image and then base classification on the fitting error. Experiments show that this approach significantly outperforms the sequential baseline as well as other related approches.

Integrated proteomic and transcriptomic profiling of mouse lung development and Nmyc target genes.

Although microarray analysis has provided information regarding the dynamics of gene expression during development of the mouse lung, no extensive correlations have been made to the levels of corresponding protein products. Here, we present a global survey of protein expression during mouse lung organogenesis from embryonic day E13.5 until adulthood using gel‐free two‐dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (MudPIT). Mathematical modeling of the proteomic profiles with parallel DNA microarray data identified large groups of gene products with statistically significant correlation or divergence in coregulation of protein and transcript levels during lung development. We also present an integrative analysis of mRNA and protein expression in Nmyc loss‐ and gain‐of‐function mutants. This revealed a set of 90 positively and negatively regulated putative target genes. These targets are evidence that Nmyc is a regulator of genes involved in mRNA processing and a repressor of the imprinted gene Igf2r in the developing lung.

Epitomic Location Recognition

This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. Experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.

Learning appearance and transparency manifolds of occluded objects in layers

By mapping a set of input images to points in a low-dimensional manifold or subspace, it is possible to efficiently account for a small number of degrees of freedom. For example, images of a person walking can be mapped to a one-dimensional manifold that measures the phase of the persons gait. However, when the object is moving around the frame and being occluded by other objects, standard manifold modeling techniques (e.g., principal components analysis, factor analysis, locally linear embedding) try to account for global motion and occlusion. We show how factor analysis can be incorporated into a generative model of layered, 2.5-dimensional vision, to jointly locate objects, resolve occlusion ambiguities, and learn models of the appearance manifolds of objects. We demonstrate the algorithm on a video consisting of four occluding objects, two of which are people who are walking, and occlude each other for most of the duration of the video. Whereas standard manifold modeling techniques fail to extract information about the gaits, the layered model successfully extracts a periodic representation of the gait of each person.

Matching unstructured product offers to structured product specifications

An e-commerce catalog typically comprises of specifications for millions of products. The search engine receives millions of sales offers from thousands of independent merchants that must be matched to the right products. We describe the challenges that a system for matching unstructured offers to structured product descriptions must address, drawing upon our experience from building such a system for Bing Shopping. The heart of our system is a data-driven component that learns the matching function off-line, which is then applied at run-time for matching offers to products. We provide the design of this and other critical components of the system as well as the details of the extensive experiments we performed to assess the readiness of the system. This system is currently deployed in an experimental Commerce Search Engine and is used to match all the offers received by Bing Shopping to the Bing product catalog.

Epitomic location recognition

This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yield enhanced generalization with economical training. Experiments on both existing and new labelled image databases result in recognition accuracy superior to state of the art with real-time computational performance.

Enriching textbooks with images

Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.