Collective Alignment of Cells in Planar Cell Polarity: Insights from a Spin Model

In metazoans, cells collectively polarize and align along the tissue plane. This phenomenon is called Planar cell polarity (PCP). Polarization means asymmetric segregation of molecules and sub-cellular structures within a cell. In PCP, cells collectively align in a particular direction along the tissue plane through identical polarization. PCP in the Drosophila wing requires local cell-cell interactions in the presence of some global cue. We used a lattice-based equilibrium model and investigated the collective alignment of cells through local interactions and a global cue. This system undergoes a percolation transition and belongs to the universality class of 2D random percolation. We show that the local interaction should be beyond a threshold to trigger system-level coordinated polarization of cells. Under this condition, even a weak global cue can align all cells in the correct direction. With strong local interactions, this system is robust against local aberrations in global signaling, and collective alignment of cells is achieved even with a transient global signal.Comment: 11 pages, 9 figure

High affinity mouse-human chimeric Fab against hepatitis B surface antigen

Aim: Passive immunotherapy using antibody against hepatitis B surface antigen (HBsAg) has been advocated in certain cases of Hepatitis B infection. We had earlier reported on the cloning and expression of a high affinity scFv derived from a mouse monoclonal (5S) against HBsAg. However this mouse antibody cannot be used for therapeutic purposes as it may elicit anti-mouse immune responses. Chimerization by replacing mouse constant domains with human ones can reduce the immunogenicity of this antibody. Methods: We cloned the VH and VL genes of this mouse antibody, and fused them with CH1 domain of human IgG1 and CL domain of human kappa chain respectively. These chimeric genes were cloned into a phagemid vector. After initial screening using the phage display system, the chimeric Fab was expressed in soluble form in E. coli. Results: The chimeric Fab was purified from the bacterial periplasmic extract. We characterized the chimeric Fab using several in vitro techniques and it was observed that the chimeric molecule retained the high affinity and specificity of the original mouse monoclonal. This chimeric antibody fragment was further expressed in different strains of E. coli to increase the yield. Conclusion: We have generated a mouse-human chimeric Fab against HBsAg without any significant loss in binding and epitope specificity. This chimeric Fab fragment can be further modified to generate a full-length chimeric antibody for therapeutic uses

StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization

Large-scale foundation models (e.g., CLIP) have shown promising zero-shot generalization performance on downstream tasks by leveraging carefully designed language prompts. However, despite their success, most prompt learning techniques tend to underperform in the presence of domain shift. Our study addresses this problem and, to improve CLIP's generalization ability across domains, proposes \textsc{StyLIP}, a novel approach for Domain Generalization (DG) based on a domain-agnostic prompt learning strategy. In the absence of explicit domain knowledge, we aim to disentangle the visual style and the content information extracted from the pre-trained CLIP in the prompts so they can be effortlessly adapted to novel domains during inference. Furthermore, we consider a set of style projectors to learn the prompt tokens directly from these multi-scale style features, and the generated prompt embeddings are later fused with the multi-scale visual features learned through a content projector. The projectors are contrastively trained, given CLIP's frozen vision and text encoders. We present extensive experiments in five different DG settings on multiple benchmarks, demonstrating that \textsc{StyLIP} consistently outperforms the relevant state-of-the-art methods.Comment: 23 pages, 7 figures, 9 table

Emergence of Cellular Heterogeneity in Expression of an Oncofetal Protein

Talk give at 34^th Annual Convention of Indian Association for Cancer Research (<b>IACR2015</b>), 19^th– 21^st February 2015 Jaipur. <div><b><br></b><div><b>Abstract: </b>It is now well established that cells in a tumor are heterogeneous. Such heterogeneity originates for various reasons, including accumulation of random mutations. It is commonly assumed that a population of genetically identical, clonally-derived, cells will be homogenous and all the cells in that population would behave similarly. However, that is not true. Like many other cellular processes, expression of a gene is stochastic. Such stochasticity leads to non-genetic cellular heterogeneity in gene expression. Heterogeneity in gene expression often leads to different phenotypic states within a population of cells. Design of the transcriptional circuit affects the heterogeneity. A positive feedback, in a transcriptional circuit, amplifies noise in gene expression and often triggers emergence of two subpopulations. Here, in this work we show that transcription of an oncofetal protein human Cripto-1 (CR-1) is regulated through an autoregulatory positive feedback. We characterize this feedback using biochemical tools. We further show that induction of this circuit leads to spontaneous emergence of two sub-populations, having higher and lower expression of CR-1. Such heterogeneity in CR-1 expression is observed in clinical samples and in multiple cancer cell lines. We use both experimental and mathematical simulation to understand the phenomenon. We have observed that MDR-1, a drug resistance gene, is also co-induced in the subpopulation having higher expression of CR-1. Our work emphasizes involvement of  CR-1 in the phenotypic diversification of cancer cells and spontaneous emergence of probable drug resistant subpopulation.</div><p> <br></p></div

Is it all in your gene?

This is a power point presentation used for a talk to School children (11th standard) on genes, genetics and us. It focuses mostly on the old debate of nature vs nurture and deals with the question how much of us is determined by Genes only

Problems in using statistical analysis of replacement and silent mutations in antibody genes for determining antigen-driven affinity selection

The analysis of molecular signatures of antigen-driven affinity selection of B cells is of immense use in studies on normal and abnormal B cell development. Most of the published literature compares the expected and observed frequencies of replacement (R) and silent (S) mutations in the complementarity-determining regions (CDRs) and the framework regions (FRs) of antibody genes to identify the signature of antigenic selection. The basic assumption of this statistical method is that antigenic selection creates a bias for R mutations in the CDRs and for S mutations in the FRs. However, it has been argued that the differences in intrinsic mutability among different regions of an antibody gene can generate a statistically significant bias even in the absence of any antigenic selection. We have modified the existing statistical method to include the effects of intrinsic mutability of different regions of an antibody gene. We used this method to analyse sequences of several B cell-derived monoclonals against T-dependent antigens, T-independent antigens, clones derived from lymphoma and amyloidogenic clones. Our sequence analysis indicates that even after correcting for the intrinsic mutability of antibody genes, statistical parameters fail to reflect the role of antigen-driven affinity selection in maturation of many clones. We suggest that, contrary to the basic assumption of such statistical methods, selection can act both for and against R mutations in the CDR as well as in the FR regions. In addition we have identified different methodological difficulties in the current uses of such statistical analysis of antibody genes

Organizing Principles in Biology

This is a talk delivered at the Department of Physics, IIT Guwahati in March 2015. It discusses issues of self-organization, network and network behavior in biology

Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition

Epithelial to Mesenchymal Transition (EMT) is a multi-state process. Here, we investigated phenotypic state transition dynamics of Epidermal Growth Factor (EGF)-induced EMT in a breast cancer cell line MDA-MB-468. We have defined phenotypic states of these cells in terms of their morphologies and have shown that these cells have three distinct morphological states&mdash;cobble, spindle, and circular. The spindle and circular states are the migratory phenotypes. Using quantitative image analysis and mathematical modeling, we have deciphered state transition trajectories in different experimental conditions. This analysis shows that the phenotypic state transition during EGF-induced EMT in these cells is reversible, and depends upon the dose of EGF and level of phosphorylation of the EGF receptor (EGFR). The dominant reversible state transition trajectory in this system was cobble to circular to spindle to cobble. We have observed that there exists an ultrasensitive on/off switch involving phospho-EGFR that decides the transition of cells in and out of the circular state. In general, our observations can be explained by the conventional quasi-potential landscape model for phenotypic state transition. As an alternative to this model, we have proposed a simpler discretized energy-level model to explain the observed state transition dynamics

Molecular Signaling Network of Cripto-1

<p>This is a presentation on identification of an incoherent feed-forward loop in Cripto-1 signaling. Cripto-1 is an oncofetal protein. We have shown that Cripto-1 can activate both PI3K pathway as well increases PTEN. This creats a self-regulatory feedforward loop. This presentation is based on the paper: Cancer Lett. 2012 318(2): 189-98 [PMID: 22182448]. This presentation was made at a meeting at B. Borooah Cancer Institute (BBCI), Guwahati, India in 2012</p