The Kruppel-like factor (KLF) family of transcription factors regulates diverse biological processes that include proliferation, differentiation, growth, development, survival, and responses to external stress. Seventeen mammalian KLFs have been identified, and numerous studies have been published that describe their basic biology and contribution to human diseases. KLF proteins have received much attention because of their involvement in the development and homeostasis of numerous organ systems. KLFs are critical regulators of physiological systems that include the cardiovascular, digestive, respiratory, hematological, and immune systems and are involved in disorders such as obesity, cardiovascular disease, cancer, and inflammatory conditions. Furthermore, KLFs play an important role in reprogramming somatic cells into induced pluripotent stem (iPS) cells and maintaining the pluripotent state of embryonic stem cells. As research on KLF proteins progresses, additional KLF functions and associations with disease are likely to be discovered. Here, we review the current knowledge of KLF proteins and describe common attributes of their biochemical and physiological functions and their pathophysiological roles.

Mammalian Krüppel-Like Factors in Health and Diseases

Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution's mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption.

Krüppel-like Factor 4 (KLF4): What We Currently Know

Kruppel-like factors (KLFs) are a family of DNA-binding transcriptional regulators with diverse and essential functions in a multitude of cellular processes, including proliferation, differentiation, migration, inflammation and pluripotency. In this Review, we discuss the roles and regulation of the 17 known KLFs in various cancer-relevant processes. Importantly, the functions of KLFs are context dependent, with some KLFs having different roles in normal cells and cancer, during cancer development and progression and in different cancer types. We also identify key questions for the field that are likely to lead to important new translational research and discoveries in cancer biology.

Krüppel-like factors in cancer

Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci

Estrogen Inhibits Transforming Growth Factor β Signaling by Promoting Smad2/3 Degradation

Kruppel-like factor 4 (KLF4) is a transcription factor that participates in both tumor suppression and oncogenesis. To determine the association of KLF4 with tumorigenesis, we integrated data assembled in the Oncomine database and discovered a decrease in KLF4 gene transcripts in breast cancers. Further analysis of the database also showed a correlation between KLF4 expression and estrogen receptor-α (ERα) positivity. Knockdown of KLF4 in MCF-7 cells elevated the growth rate of these cells in the presence of estrogen. Therefore, we examined the interaction between KLF4 and ERα, and found that KLF4 bound to the DNA-binding region of ERα. KLF4 thus inhibits the binding of ERα to estrogen response elements in promoter regions, resulting in a reduction in ERα target gene transcription. Earlier studies have reported that KLF4 is transcriptionally activated by p53 following DNA damage. We also showed that activation of p53 decreased the transcriptional activity of ERα by elevating KLF4 expression. Our studies discovered a novel molecular network between p53, KLF4 and ERα. As both p53 and ERα are involved in cell growth and apoptosis, these results may explain why KLF4 possesses both tumor suppressive and oncogenic functions in breast cancers.

KLF4 suppresses estrogen-dependent breast cancer growth by inhibiting the transcriptional activity of ERα

Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data.

Logistic regression is the method of choice in most genome-wide association studies (GWAS). Due to the heavy cost of performing iterative parameter updates when training such a model, existing methods have prohibitive communication and computational complexities that make them unpractical for real-life usage. We propose a new sampling-based secure protocol to compute exact statistics, that requires a constant number of communication rounds and a much lower number of computations. The publicly available implementation of our protocol (and its many optional optimisations adapted to different security scenarios) can, in a matter of hours, perform statistical testing of over 600 SNP variables across thousands of patients while accounting for potential confounding factors in the clinical data.

Privacy-Preserving Statistical Analysis by Exact Logistic Regression

In recent years, there has been a growing trend towards outsourcing of computational tasks with the development of cloud services. The Gentry’s pioneering work of fully homomorphic encryption (FHE) and successive works have opened a new vista for secure and practical cloud computing. In this paper, we consider performing statistical analysis on encrypted data. To improve the efficiency of the computations, we take advantage of the batched computation based on the ChineseRemainder-Theorem. We propose two building blocks that work with FHE: a novel batch greater-than primitive, and matrix primitive for encrypted matrices. With these building blocks, we construct secure procedures and protocols for different types of statistics including the histogram (count), contingency table (with cell suppression) for categorical data; k-percentile for ordinal data; and principal component analysis and linear regression for numerical data. To demonstrate the effectiveness of our methods, we ran experiments in five real datasets. For instance, we can compute a contingency table with more than 50 cells from 4000 of data in just 5 minutes, and we can train a linear regression model with more than 40k of data and dimension as high as 6 within 15 minutes. We show that the FHE is not as slow as commonly believed and it becomes feasible to perform a broad range of statistical analysis on thousands of encrypted data.

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Shohei Kawasaki

Papers

Estrogen Inhibits Transforming Growth Factor β Signaling by Promoting Smad2/3 Degradation

KLF4 suppresses estrogen-dependent breast cancer growth by inhibiting the transcriptional activity of ERα

Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data.

Privacy-Preserving Statistical Analysis by Exact Logistic Regression

Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data.