PIWI-interacting RNAs (piRNAs) silence retrotransposons in Drosophila germ lines by associating with the PIWI proteins Argonaute 3 (AGO3), Aubergine (Aub) and Piwi. piRNAs in Drosophila are produced from intergenic repetitive genes and piRNA clusters by two systems: the primary processing pathway and the amplification loop. The amplification loop occurs in a Dicer-independent, PIWI-Slicer-dependent manner. However, primary piRNA processing remains elusive. Here we analysed piRNA processing in a Drosophila ovarian somatic cell line where Piwi, but not Aub or AGO3, is expressed; thus, only the primary piRNAs exist. In addition to flamenco, a Piwi-specific piRNA cluster, traffic jam (tj), a large Maf gene, was determined as a new piRNA cluster. piRNAs arising from tj correspond to the untranslated regions of tj messenger RNA and are sense-oriented. piRNA loading on to Piwi may occur in the cytoplasm. zucchini, a gene encoding a putative cytoplasmic nuclease, is required for tj-derived piRNA production. In tj and piwi mutant ovaries, somatic cells fail to intermingle with germ cells and Fasciclin III is overexpressed. Loss of tj abolishes Piwi expression in gonadal somatic cells. Thus, in gonadal somatic cells, tj gives rise simultaneously to two different molecules: the TJ protein, which activates Piwi expression, and piRNAs, which define the Piwi targets for silencing.

PIWI-interacting RNAs (piRNAs) protect genome integrity from transposons. In Drosophila ovarian somas, primary piRNAs are produced and loaded onto Piwi. Here, we describe roles for the cytoplasmic Yb body components Armitage and Yb in somatic primary piRNA biogenesis. Armitage binds to Piwi and is required for localizing Piwi into Yb bodies. Without Armitage or Yb, Piwi is freed from the piRNAs and does not enter the nucleus. Thus, piRNA loading is required for Piwi nuclear entry. We propose that a functional Piwi–piRNA complex is formed and inspected in Yb bodies before its nuclear entry to exert transposon silencing.

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype–genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.Laboratory evolution of microorganisms is a powerful approach to the elucidation of the nature of evolutionary dynamics 1,2 . Recent advances in measurement technology, including high-throughput sequencing, have enabled us to quantify phenotypic and genotypic changes during laboratory evolution, which have provided valuable information on the mechanisms and principles of adaptive evolution [3][4][5] . The impact of laboratory evolution has extended beyond the field of evolutionary biology into engineering and medicine. For example, by using laboratory evolution approaches, some candidate mutations that contribute to antibiotic resistance have been identified, and this sheds light on how to control the emergence of antibiotic-resistant strains [6][7][8][9] . Laboratory evolution has also become a widely used tool for bioengineering applications 10-13 -to generate cells with improved growth, production titer, and stress tolerance, which are essential for improving industrial microbial production.The evolutionary adaptation to a specific environment is frequently accompanied by changes in fitness in response to other environments. For example, it was demonstrated that the acquisition of resistance to one antibiotic can give rise to resistance to other drugs simultaneously, which is called cross-resistance, while it can also increase sensitivity to other drugs, which is called collateral sensitivity [14][15][16][17][18][19] . Such links between changes in fitness affect the course of evolution considerably. This can be utilized for predicting and controlling evolutionary dynamics, such as the suppression of resistance acquisition by use of multiple antibiotics in combination, with collateral sensitivity interactions. Such a "design" of evolutionary dynamics based on the links between fitness changes can contribute to the suppression of emerging multidrug-resistant pathogens [20][21][22] , and to the development of useful microorganisms for bioprodu...

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