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Ali Shilatifard

Bio: Ali Shilatifard is an academic researcher from Northwestern University. The author has contributed to research in topics: Chromatin & RNA polymerase II. The author has an hindex of 88, co-authored 264 publications receiving 34536 citations. Previous affiliations of Ali Shilatifard include Saint Louis University & University of Pennsylvania.


Papers
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Journal ArticleDOI
30 Mar 2006-Nature
TL;DR: T tandem affinity purification was used to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae to identify protein–protein interactions, which will help future studies on individual proteins as well as functional genomics and systems biology.
Abstract: Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

2,975 citations

Journal ArticleDOI
TL;DR: It was evident that multiple mechanistic steps lead to the stable heritance of the epigenetic phenotype.
Abstract: A recent meeting (December 2008) regarding chromatin-based epigenetics was hosted by the Banbury Conference Center and Cold Spring Harbor Laboratory. The intent was to discuss aspects of epigenetic control of genomic function, and to arrive at a consensus definition of "epigenetics" to be considered by the broader community. It was evident that multiple mechanistic steps lead to the stable heritance of the epigenetic phenotype. Below we provide our view and interpretation of the proceedings at the meeting.

1,640 citations

Journal ArticleDOI
TL;DR: The recent biochemical, molecular, cellular, and physiological functions of histone methylation and ubiquitination involved in the regulation of gene expression as determined by a combination of enzymological, structural, and genetic methodologies are highlighted.
Abstract: It is more evident now than ever that nucleosomes can transmit epigenetic information from one cell generation to the next. It has been demonstrated during the past decade that the posttranslational modifications of histone proteins within the chromosome impact chromatin structure, gene transcription, and epigenetic information. Multiple modifications decorate each histone tail within the nucleosome, including some amino acids that can be modified in several different ways. Covalent modifications of histone tails known thus far include acetylation, phosphorylation, sumoylation, ubiquitination, and methylation. A large body of experimental evidence compiled during the past several years has demonstrated the impact of histone acetylation on transcriptional control. Although histone modification by methylation and ubiquitination was discovered long ago, it was only recently that functional roles for these modifications in transcriptional regulation began to surface. Highlighted in this review are the recent biochemical, molecular, cellular, and physiological functions of histone methylation and ubiquitination involved in the regulation of gene expression as determined by a combination of enzymological, structural, and genetic methodologies.

1,119 citations

Journal ArticleDOI
16 Nov 2007-Cell
TL;DR: The aim of this exhibition was to celebrate the 50th anniversary of the United States Declaration of Independence with a celebration of those who served in the armed forces and those who sacrificed in the conflicts of World War II.

902 citations

Journal ArticleDOI
TL;DR: The process of histone H2B monoubiquitination-dependent and -independent hist one H3K4 methylation as a mark of active transcription, enhancer signatures, and developmentally poised genes is discussed and recent findings in this regard are examined.
Abstract: The Saccharomyces cerevisiae Set1/COMPASS was the first histone H3 lysine 4 (H3K4) methylase identified over 10 years ago. Since then, it has been demonstrated that Set1/COMPASS and its enzymatic product, H3K4 methylation, is highly conserved across the evolutionary tree. Although there is only one COMPASS in yeast, Drosophila possesses three and humans bear six COMPASS family members, each capable of methylating H3K4 with nonredundant functions. In yeast, the histone H2B monoubiquitinase Rad6/Bre1 is required for proper H3K4 and H3K79 trimethylations. The machineries involved in this process are also highly conserved from yeast to human. In this review, the process of histone H2B monoubiquitination-dependent and -independent histone H3K4 methylation as a mark of active transcription, enhancer signatures, and developmentally poised genes is discussed. The misregulation of histone H2B monoubiquitination and H3K4 methylation result in the pathogenesis of human diseases, including cancer. Recent findings in this regard are also examined.

883 citations


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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
23 Feb 2007-Cell
TL;DR: The surface of nucleosomes is studded with a multiplicity of modifications that can dictate the higher-order chromatin structure in which DNA is packaged and can orchestrate the ordered recruitment of enzyme complexes to manipulate DNA.

10,046 citations

Journal ArticleDOI
TL;DR: The known histone modifications are described, where they are found genomically and discussed and some of their functional consequences are discussed, concentrating mostly on transcription where the majority of characterisation has taken place.
Abstract: Chromatin is not an inert structure, but rather an instructive DNA scaffold that can respond to external cues to regulate the many uses of DNA. A principle component of chromatin that plays a key role in this regulation is the modification of histones. There is an ever-growing list of these modifications and the complexity of their action is only just beginning to be understood. However, it is clear that histone modifications play fundamental roles in most biological processes that are involved in the manipulation and expression of DNA. Here, we describe the known histone modifications, define where they are found genomically and discuss some of their functional consequences, concentrating mostly on transcription where the majority of characterisation has taken place.

4,536 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

4,408 citations