MicroRNAs: Genomics, Biogenesis, Mechanism, and Function

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。

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

http://bartellab.wi.mit.edu/publication_reprints/Bartel_Cell09.pdf

MicroRNAs: Target Recognition and Regulatory Functions

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.).

Machine learning

Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets

MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression in plants and animals. Although their biological importance has become clear, how they recognize and regulate target genes remains less well understood. Here, we systematically evaluate the minimal requirements for functional miRNA–target duplexes in vivo and distinguish classes of target sites with different functional properties. Target sites can be grouped into two broad categories. 5′ dominant sites have sufficient complementarity to the miRNA 5′ end to function with little or no support from pairing to the miRNA 3′ end. Indeed, sites with 3′ pairing below the random noise level are functional given a strong 5′ end. In contrast, 3′ compensatory sites have insufficient 5′ pairing and require strong 3′ pairing for function. We present examples and genome-wide statistical support to show that both classes of sites are used in biologically relevant genes. We provide evidence that an average miRNA has approximately 100 target sites, indicating that miRNAs regulate a large fraction of protein-coding genes and that miRNA 3′ ends are key determinants of target specificity within miRNA families.

Principles of MicroRNA–Target Recognition

http://web.mit.edu/manoli/www/publications/Brennecke_Cell_07_piwiRNAs.pdf

Discrete Small RNA-Generating Loci as Master Regulators of Transposon Activity in Drosophila

https://www.cell.com/cell/pdf/S0092-8674(03)00231-9.pdf

bantam Encodes a Developmentally Regulated microRNA that Controls Cell Proliferation and Regulates the Proapoptotic Gene hid in Drosophila

Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution.

Increasingly complex networks of small RNAs act through RNA-interference (RNAi) pathways to regulate gene expression, to mediate antiviral responses, to organize chromosomal domains, and to restrain the spread of selfish genetic elements. Historically, RNAi has been defined as a response to double-stranded RNA. However, some small RNA species may not arise from double-stranded RNA precursors. Yet, like microRNAs and small interfering RNAs, such species guide Argonaute proteins to silencing targets through complementary base-pairing. Silencing can be achieved by corecruitment of accessory factors or through the activity of Argonaute itself, which often has endonucleolytic activity. As a specific and adaptive regulatory system, RNAi is used throughout eukarya, which indicates a long evolutionary history. A likely function of RNAi throughout that history is to protect the genome from both pathogenic and parasitic invaders.

https://science.sciencemag.org/content/sci/318/5851/761.full.pdf

Julius Brennecke

Papers

Principles of MicroRNA–Target Recognition

Discrete Small RNA-Generating Loci as Master Regulators of Transposon Activity in Drosophila

bantam Encodes a Developmentally Regulated microRNA that Controls Cell Proliferation and Regulates the Proapoptotic Gene hid in Drosophila

Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolution.

The Piwi-piRNA Pathway Provides an Adaptive Defense in the Transposon Arms Race