Author
Eran Kotler
Other affiliations: Weizmann Institute of Science
Bio: Eran Kotler is an academic researcher from Stanford University. The author has contributed to research in topics: Breast cancer & Neoadjuvant therapy. The author has an hindex of 8, co-authored 14 publications receiving 1540 citations. Previous affiliations of Eran Kotler include Weizmann Institute of Science.
Topics: Breast cancer, Neoadjuvant therapy, Cancer, Microbiome, Lapatinib
Papers
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TL;DR: The metagenomically characterized the murine and human mucosal-associated gastrointestinal microbiome and found it to only partially correlate with stool microbiome, indicating that empiric probiotics supplementation may be limited in universally and persistently impacting the gut mucosa.
900 citations
TL;DR: Collectively, potential post-antibiotic probiotic benefits may be offset by a compromised gut mucosal recovery, highlighting a need of developing aFMT or personalized probiotic approaches achieving mucosal protection without compromising microbiome recolonization in the antibiotics-perturbed host.
669 citations
TL;DR: The Human Tumor Atlas Network (HTAN), part of the NCI Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types.
279 citations
TL;DR: It is suggested that optimal ESC differentiation requires dynamic changes in H2B ubiquitylation patterns, which must occur in a timely and well-coordinated manner.
190 citations
TL;DR: While loss of anti-proliferative functionality largely correlates with the occurrence of cancer-associated p53 mutations, it is observed that selective gain-of-function may further favor particular mutants in vivo, and seemingly neutral TP53 SNPs may modulate phenotypic outcome and, presumably, tumor progression.
167 citations
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TL;DR: Future studies will focus on understanding the mechanisms underlying the microbiota-gut-brain axis and attempt to elucidate microbial-based intervention and therapeutic strategies for neuropsychiatric disorders.
Abstract: The importance of the gut-brain axis in maintaining homeostasis has long been appreciated. However, the past 15 yr have seen the emergence of the microbiota (the trillions of microorganisms within ...
1,775 citations
TL;DR: How the gut microbiota and derived microbial compounds may contribute to human metabolic health and to the pathogenesis of common metabolic diseases are discussed, and examples of microbiota-targeted interventions aiming to optimize metabolic health are highlighted.
Abstract: Observational findings achieved during the past two decades suggest that the intestinal microbiota may contribute to the metabolic health of the human host and, when aberrant, to the pathogenesis of various common metabolic disorders including obesity, type 2 diabetes, non-alcoholic liver disease, cardio-metabolic diseases and malnutrition. However, to gain a mechanistic understanding of how the gut microbiota affects host metabolism, research is moving from descriptive microbiota census analyses to cause-and-effect studies. Joint analyses of high-throughput human multi-omics data, including metagenomics and metabolomics data, together with measures of host physiology and mechanistic experiments in humans, animals and cells hold potential as initial steps in the identification of potential molecular mechanisms behind reported associations. In this Review, we discuss the current knowledge on how gut microbiota and derived microbial compounds may link to metabolism of the healthy host or to the pathogenesis of common metabolic diseases. We highlight examples of microbiota-targeted interventions aiming to optimize metabolic health, and we provide perspectives for future basic and translational investigations within the nascent and promising research field. In this Review, Fan and Pedersen discuss how the gut microbiota and derived microbial compounds may contribute to human metabolic health and to the pathogenesis of common metabolic diseases, and highlight examples of microbiota-targeted interventions aiming to optimize metabolic health.
1,445 citations
Journal Article•
TL;DR: In this article, a nucleosome-DNA interaction model was proposed to predict the genome-wide organization of nucleosomes, and it was shown that genomes encode an intrinsic nucleosomal organization and that this intrinsic organization can explain ∼50% of the in-vivo positions.
Abstract: Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.
1,399 citations
25 Apr 2017
TL;DR: This presentation is a case study taken from the travel and holiday industry and describes the effectiveness of various techniques as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib).
Abstract: This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.
1,338 citations
TL;DR: The metagenomically characterized the murine and human mucosal-associated gastrointestinal microbiome and found it to only partially correlate with stool microbiome, indicating that empiric probiotics supplementation may be limited in universally and persistently impacting the gut mucosa.
900 citations