Role of TRIP6 and Angiomotins in the Regulation of the Hippo Signaling Pathway

Mechanical tension is an important regulator of cell proliferation, differentiation, migration and cell death. It is involved in the control of tissue architecture and wound repair and its improper sensing can contribute to cancer. The Hippo tumor suppressor pathway was recently shown to be involved in regulating cell proliferation in response to mechanical tension. The core of the pathway consists of the kinases MST1/2 and LATS1/2, which regulate the target of the pathway, the transcription co-activator YAP/ TAZ (hereafter referred to as YAP). When the Hippo pathway is inactive, YAP remains in the nucleus and promotes cell proliferation and stem cell maintenance. When the Hippo signaling pathway is turned on, MST1/2 phosphorylate and activates LATS1/2. LATS1/2 phosphorylates and inactivates YAP in the cytoplasm which is sequestered and degraded, stopping cell proliferation and promoting differentiation of stem cells. Mechanical forces are transmitted across cells and tissues through the cell-cell junctions and the actin cytoskeleton. However, the factors that connect cell-cell junctions to the Hippo signaling pathway were not clearly known. We identified a LIM domain protein called TRIP6 that functions at the adherens junctions to regulate the Hippo signaling pathway in a tension-dependent manner. TRIP6 responds to mechanical tension at adherens junctions and regulates LATS1/2 activity. Under high mechanical tension, TRIP6 sequesters and inhibits LATS1/2 at adherens junctions to promote YAP activity. Conditions that reduce tension at adherens junctions by inhibition of actin stress fibers or disruption of cell-cell junctions reduce TRIP6-LATS1/2 binding, which activates LATS1/2 to inhibit YAP. Vinculin has been shown to act as part of a mechanosensory complex at adherens junctions. We show that vinculin promotes TRIP6 inhibition of LATS1/2 in response to mechanical tension. Furthermore, we show that TRIP6 competitively inhibits MOB1 (a known LATS1/2 activator) from binding and activating LATS1/2. Together these findings reveal TRIP6 responds to mechanical signals at adherens junctions to regulate the Hippo signaling pathway in mammalian cells

Development of novel Competitive Enzyme-linked immunosorbent assays to detect SARS-CoV-2-specific antibodies in animals

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID-19-causing virus, is a zoonotic pathogen. There is concern about the virus spilling over from humans into wildlife species, which may then serve as reservoirs for future infection of humans and other animals. Furthermore, the level of exposure of potentially susceptible wildlife species is currently not known. There is, therefore, an urgent need to develop a single test that could be used for the serosurveillance of multiple wildlife species for exposure to SARS-CoV-2. Although there are serological techniques to detect the exposure of humans to the virus, few assays have the capacity to detect antibodies in a wide variety of species. Here, I describe the development of a competitive enzyme-linked immunosorbent assay (cELISA) to detect SARS-CoV-2 antibodies in mammals for which species-specific reagents are not available. Therefore, cELISAs were developed to detect SARS-CoV-2 spike S1 and S2 domains and nucleocapsid (N) specific antibodies and were validated using sera from experimentally infected hamsters. We further validated our cELISA by comparing it with results obtained from the surrogate virus neutralization test (cPASS, GenScript) and indirect ELISA using anti-hamster horse radish peroxidase (HRP) conjugated reagents. Our initial cELISA was based on the ability of test antibodies to displace the binding of commercially obtained rabbit antibodies against viral proteins coated on the ELISA plate. Rabbit antibody reagents are expensive and anti-rabbit detection antibody may cross-react with other mammalian antibodies. Therefore, I explored the use of antibodies produced in hen eggs (IgY) as a substitute for rabbit sera. Hens were immunized against SARS-CoV-2 antigens: S1, S2 and N. IgY antibodies were purified from egg yolk, and the assay was optimized to use specific antibody and antigen combinations. Among S1, S2 and N-IgYs, only the S2-IgY based cELISA was specific and comparable with both the rabbit anti serum based cELISA and the surrogate virus neutralization test (cPASS). This assay will be a valuable tool which can be implemented in surveillance programs investigating exposure to and transmission of SARS-CoV-2 in multiple domestic, captive, or wildlife species

Angiomotins link F-actin architecture to Hippo pathway signaling

The Hippo pathway regulates the transcriptional coactivator YAP to control cell proliferation, organ size, and stem cell maintenance. Multiple factors, such as substrate stiffness, cell density, and G protein-coupled receptor signaling, regulate YAP through their effects on the F-actin cytoskeleton, although the mechanism is not known. Here we show that angiomotin proteins (AMOT130, AMOTL1, and AMOTL2) connect F-actin architecture to YAP regulation. First, we show that angiomotins are required to relocalize YAP to the cytoplasm in response to various manipulations that perturb the actin cytoskeleton. Second, angiomotins associate with F-actin through a conserved F-actin-binding domain, and mutants defective for F-actin binding show enhanced ability to retain YAP in the cytoplasm. Third, F-actin and YAP compete for binding to AMOT130, explaining how F-actin inhibits AMOT130-mediated cytoplasmic retention of YAP. Furthermore, we find that LATS can synergize with F-actin perturbations by phosphorylating free AMOT130 to keep it from associating with F-actin. Together these results uncover a mechanism for how F-actin levels modulate YAP localization, allowing cells to make developmental and proliferative decisions based on diverse inputs that regulate actin architecture

Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons

Nonlinear activation functions endow neural networks with the ability to learn complex high-dimensional functions. The choice of activation function is a crucial hyperparameter that determines the performance of deep neural networks. It significantly affects the gradient flow, speed of training and ultimately the representation power of the neural network. Saturating activation functions like sigmoids suffer from the vanishing gradient problem and cannot be used in deep neural networks. Universal approximation theorems guarantee that multilayer networks of sigmoids and ReLU can learn arbitrarily complex continuous functions to any accuracy. Despite the ability of multilayer neural networks to learn arbitrarily complex activation functions, each neuron in a conventional neural network (networks using sigmoids and ReLU like activations) has a single hyperplane as its decision boundary and hence makes a linear classification. Thus single neurons with sigmoidal, ReLU, Swish, and Mish activation functions cannot learn the XOR function. Recent research has discovered biological neurons in layers two and three of the human cortex having oscillating activation functions and capable of individually learning the XOR function. The presence of oscillating activation functions in biological neural neurons might partially explain the performance gap between biological and artificial neural networks. This paper proposes 4 new oscillating activation functions which enable individual neurons to learn the XOR function without manual feature engineering. The paper explores the possibility of using oscillating activation functions to solve classification problems with fewer neurons and reduce training time

Implementation of Automated Training & Placement

Training and placement is the crucial part of any educational institutes in which most of the work till now is being done manually. The aim of this project is that automation of training and placement department that will include minimum manual work and maximum optimization abstraction security. This is the web application as well as mobile application which can use in the android operating system as well as IOS operating system it is developed in ionic framework[1]. Students need to register in this application by filling all basic details like email_id, enrollment number etc. After successful registration students can able to logged into the system and after login he/she need to update his/her profile[4]. Also in this students can able to view company details. The training and placement department contains all the information about the students. The system stores all the personal information of the students like their personal details, qualification details and academic details. Also Admin can able to update the company details. In this project student get the notifications about the companies coming for the campus via SMS and email listing out the students as per company?s criteria provides all the details of the interview[8]. This project reduces the human efforts and maintaining large amount of data properly

Lightweight Knowledge Representations for Automating Data Analysis

The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available data, the operations that can be performed on those data, the algorithms/models that are fed the data, and how all of these facets interweave. In this work, we take the first steps towards automating a key aspect of the data science pipeline: data analysis. We present an extensible taxonomy of data analytic operations that scopes across domains and data, as well as a method for codifying domain-specific knowledge that links this analytics taxonomy to actual data. We validate the functionality of our analytics taxonomy by implementing a system that leverages it, alongside domain labelings for 8 distinct domains, to automatically generate a space of answerable questions and associated analytic plans. In this way, we produce information spaces over data that enable complex analyses and search over this data and pave the way for fully automated data analysis

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe