Michael T. Hemann

Bio: Michael T. Hemann is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Cancer & DNA damage. The author has an hindex of 56, co-authored 160 publications receiving 15166 citations. Previous affiliations of Michael T. Hemann include Howard Hughes Medical Institute & Broad Institute.

A microRNA polycistron as a potential human oncogene

Abstract: To date, more than 200 microRNAs have been described in humans; however, the precise functions of these regulatory, non-coding RNAs remains largely obscure. One cluster of microRNAs, the mir-17-92 polycistron, is located in a region of DNA that is amplified in human B-cell lymphomas. Here we compared B-cell lymphoma samples and cell lines to normal tissues, and found that the levels of the primary or mature microRNAs derived from the mir-17-92 locus are often substantially increased in these cancers. Enforced expression of the mir-17-92 cluster acted with c-myc expression to accelerate tumour development in a mouse B-cell lymphoma model. Tumours derived from haematopoietic stem cells expressing a subset of the mir-17-92 cluster and c-myc could be distinguished by an absence of apoptosis that was otherwise prevalent in c-myc-induced lymphomas. Together, these studies indicate that non-coding RNAs, specifically microRNAs, can modulate tumour formation, and implicate the mir-17-92 cluster as a potential human oncogene.

Disrupting the Pairing Between let-7 and Hmga2 Enhances Oncogenic Transformation

Abstract: MicroRNAs (miRNAs) are ∼22-nucleotide RNAs that can pair to sites within messenger RNAs to specify posttranscriptional repression of these messages. Aberrant miRNA expression can contribute to tumorigenesis, but which of the many miRNA-target relationships are relevant to this process has been unclear. Here, we report that chromosomal translocations previously associated with human tumors disrupt repression of High Mobility Group A2 (Hmga2) by let-7 miRNA. This disrupted repression promotes anchorage-independent growth, a characteristic of oncogenic transformation. Thus, losing miRNA-directed repression of an oncogene provides a mechanism for tumorigenesis, and disrupting a single miRNA-target interaction can produce an observable phenotype in mammalian cells.

Probing tumor phenotypes using stable and regulated synthetic microRNA precursors

Abstract: RNA interference is a powerful method for suppressing gene expression in mammalian cells. Stable knock-down can be achieved by continuous expression of synthetic short hairpin RNAs, typically from RNA polymerase III promoters. But primary microRNA transcripts, which are endogenous triggers of RNA interference, are normally synthesized by RNA polymerase II. Here we show that RNA polymerase II promoters expressing rationally designed primary microRNA-based short hairpin RNAs produce potent, stable and regulatable gene knock-down in cultured cells and in animals, even when present at a single copy in the genome. Most notably, by tightly regulating Trp53 knock-down using tetracycline-based systems, we show that cultured mouse fibroblasts can be switched between proliferative and senescent states and that tumors induced by Trp53 suppression and cooperating oncogenes regress upon re-expression of Trp53. In practice, this primary microRNA-based short hairpin RNA vector system is markedly similar to cDNA overexpression systems and is a powerful tool for studying gene function in cells and animals.

Rb inactivation promotes genomic instability by uncoupling cell cycle progression from mitotic control

Abstract: Rb inactivation promotes genomic instability by uncoupling cell cycle progression from mitotic control

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

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.).

MicroRNA signatures in human cancers

Abstract: MicroRNA (miRNA ) alterations are involved in the initiation and progression of human cancer. The causes of the widespread differential expression of miRNA genes in malignant compared with normal cells can be explained by the location of these genes in cancer-associated genomic regions, by epigenetic mechanisms and by alterations in the miRNA processing machinery. MiRNA-expression profiling of human tumours has identified signatures associated with diagnosis, staging, progression, prognosis and response to treatment. In addition, profiling has been exploited to identify miRNA genes that might represent downstream targets of activated oncogenic pathways, or that target protein- coding genes involved in cancer.

MicroRNA signatures in human cancers

Abstract: MicroRNA (miRNA) alterations are involved in the initiation and progression of human cancer. The causes of the widespread differential expression of miRNA genes in malignant compared with normal cells can be explained by the location of these genes in cancer-associated genomic regions, by epigenetic mechanisms and by alterations in the miRNA processing machinery. MiRNA-expression profiling of human tumours has identified signatures associated with diagnosis, staging, progression, prognosis and response to treatment. In addition, profiling has been exploited to identify miRNA genes that might represent downstream targets of activated oncogenic pathways, or that target protein- coding genes involved in cancer.