SummaryIdentifying the genetic mechanisms underlying phenotypic change is essential to understanding how gene regulatory networks and ultimately the genotype-to-phenotype map evolve. It is recognized that microRNAs (miRNAs) have the potential to facilitate evolutionary change [1–3]; however, there are no known examples of natural morphological variation caused by evolutionary changes in miRNA expression. Therefore, the contribution of miRNAs to evolutionary change remains unknown [1, 4]. Drosophila melanogaster subgroup species display a portion of trichome-free cuticle on the femur of the second leg called the “naked valley.” It was previously shown that Ultrabithorax (Ubx) is involved in naked valley variation between D. melanogaster and D. simulans [5, 6]. However, naked valley size also varies among populations of D. melanogaster, ranging from 1,000 up to 30,000 μm2. We investigated the genetic basis of intraspecific differences in the naked valley in D. melanogaster and found that neither Ubx nor shavenbaby (svb) [7, 8] contributes to this morphological difference. Instead, we show that changes in mir-92a expression underlie the evolution of naked valley size in D. melanogaster through repression of shavenoid (sha) [9]. Therefore, our results reveal a novel mechanism for morphological evolution and suggest that modulation of the expression of miRNAs potentially plays a prominent role in generating organismal diversity.

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Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture‐confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture‐negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data‐driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR‐based diagnostics for use in endemic settings.