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Background tRNA‐derived fragments (tRFs) have been found to play a regulatory role in the occurrence and development of many tumors. The aim of this study was to identify the expression of tRFs in breast cancer and their ability to serve as diagnostic markers for breast cancer. Methods Total RNA was extracted from breast cancer and paracancerous tissues (n = 83), as well as from the sera of breast cancer patients (n = 214) and healthy donors (n = 113) using trizol reagents. Expression of tRFs was then detected by q‐PCR, and analyzed using t‐test and ROC to illuminate their potential as biomarkers for breast cancer. Results Our results demonstrated that tRFs: tRF‐Gly‐CCC‐046, tRF‐Tyr‐GTA‐010 and tRF‐Pro‐TGG‐001 were downregulated in both tissues and sera from breast cancer patients as well as early‐stage patients compared with those in the healthy donors. More importantly, the three tRFs were capable of serving as circulating biomarkers of diagnostics and early diagnosis of breast cancer, possessing areas under the curve (AUC) of 0.7871 and 0.7987, respectively. Conclusions tRFs: tRF‐Gly‐CCC‐046, tRF‐Tyr‐GTA‐010 and tRF‐Pro‐TGG‐001 are downregulated in breast cancer and early breast cancer and act as new potential biomarkers for the diagnosis and early diagnosis of breast cancer. As shown in the technology roadmap, due to the low primer specificity and expression levels, some tRFs were ruled out and others were subjected to validation in fresh tissues, including 23 breast cancer tissues and paired paracancerous tissues. After that, tRF‐Gly‐CCC‐046, tRF‐Tyr‐GTA‐010 and tRF‐Pro‐TGG‐001 were observed with significant decrease in breast cancer tissue and selected as the candidates. The three differential tRFs were further verified in expanded tissue (n = 83) and serum cohorts (n = 214). Finally, ROC curves were used to evaluate the diagnostic efficacy of the three tRFs for breast cancer and early breast cancer.

This review will summarise the recent discoveries and current state of research on short noncoding RNAs derived from tRNAs—known as tRNA-derived fragments (tRFs). It will describe the features of the known subtypes of these RNAs; including sequence characteristics, protein interactors, expression characteristics, biogenesis, and similarity to canonical miRNA pathways. Also their role in regulating gene expression; including mediating translational suppression, will be discussed. We also highlight their potential use as biomarkers, functions in gene regulation and links to disease. Finally, this review will speculate as to the origin and rationale for the conservation of this novel class of noncoding RNAs amongst both prokaryotes and eukaryotes.

Transfer RNA‐derived fragments are a group of small noncoding single‐stranded RNA that play essential roles in multiple diseases. However, their biological functions in carcinogenesis are not well understood. In this study, 5′tRF‐Gly was found to have significantly high expression in hepatocellular carcinoma (HCC), and the upregulation of 5′tRF‐Gly was positively correlated with tumor size and tumor metastasis. Overexpression of 5′tRF‐Gly induced increased growth rate and metastasis in HCC cells in vitro and in nude mice, while knockdown showed the opposite effect. Carcinoembryonic antigen‐related cell adhesion molecule 1 (CEACAM1) was confirmed to be a direct target of 5′tRF‐Gly in HCC. In addition, the cytological effect of CEACAM1 knockdown proved to be similar to the overexpression of 5′tRF‐Gly. Moreover, attenuation of CEACAM1 expression rescued the 5′tRF‐Gly‐mediated promoting effects on HCC cells. These data show that 5′tRF‐Gly is a new tumor‐promoting factor and could be a potential diagnostic biomarker or new therapeutic target for HCC. In the present study, the small RNA high‐throughput sequencing results showed that 5’tRF‐Gly was up‐regulated in HCC tissues and was related to poor prognosis. We demonstrated the oncogenic role of 5’tRF‐Gly and its underlying molecular mechanism in HCC. Furthermore, we first verified CEA cell adhesion molecule 1 (CEACAM1) as the direct target of 5’tRF‐Gly and revealed how CEACAM1 inhibits hepatocellular carcinoma growth and metastasis. Our findings not only elucidate the role of 5’tRF‐Gly and CEACAM1 in HCC but also provide new targets for the prognostic evaluation and treatment of HCC.

Small, noncoding RNAs are short untranslated RNA molecules, some of which have been associated with cancer development. Recently we showed that a class of small RNAs generated during the maturation process of tRNAs (tRNA-derived small RNAs, hereafter “tsRNAs”) is dysregulated in cancer. Specifically, we uncovered tsRNA signatures in chronic lymphocytic leukemia and lung cancer and demonstrated that the ts-4521/3676 cluster (now called “ts-101” and “ts-53,” respectively), ts-46, and ts-47 are down-regulated in these malignancies. Furthermore, we showed that tsRNAs are similar to Piwi-interacting RNAs (piRNAs) and demonstrated that ts-101 and ts-53 can associate with PiwiL2, a protein involved in the silencing of transposons. In this study, we extended our investigation on tsRNA signatures to samples collected from patients with colon, breast, or ovarian cancer and cell lines harboring specific oncogenic mutations and representing different stages of cancer progression. We detected tsRNA signatures in all patient samples and determined that tsRNA expression is altered upon oncogene activation and during cancer staging. In addition, we generated a knockedout cell model for ts-101 and ts-46 in HEK-293 cells and found significant differences in gene-expression patterns, with activation of genes involved in cell survival and down-regulation of genes involved in apoptosis and chromatin structure. Finally, we overexpressed ts-46 and ts-47 in two lung cancer cell lines and performed a clonogenic assay to examine their role in cell proliferation. We observed a strong inhibition of colony formation in cells overexpressing these tsRNAs compared with untreated cells, confirming that tsRNAs affect cell growth and survival.

•Longitudinal EEG study in which 4, 7- & 11-month infants listened to nursery rhymes.•We demonstrate cortical speech tracking via delta & theta neural signals (mTRF).•Periodogram (PSD) analysis revealed stimulus related delta and theta PSD peaks.•Delta and theta driven phase amplitude coupling (PAC) was found at all ages.•Gamma frequency amplitudes displayed stronger PAC to low frequency phases than beta. The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding at multiple time scales. Neurophysiological signals are known to track the amplitude envelope of adult-directed speech (ADS), particularly in the theta-band. Acoustic analysis of infant-directed speech (IDS) has revealed significantly greater modulation energy than ADS in an amplitude-modulation (AM) band centred on ∼2 Hz. Accordingly, cortical tracking of IDS by delta-band neural signals may be key to language acquisition. Speech also contains acoustic information within its higher-frequency bands (beta, gamma). Adult EEG and MEG studies reveal an oscillatory hierarchy, whereby low-frequency (delta, theta) neural phase dynamics temporally organize the amplitude of high-frequency signals (phase amplitude coupling, PAC). Whilst consensus is growing around the role of PAC in the matured adult brain, its role in the development of speech processing is unexplored. Here, we examined the presence and maturation of low-frequency (<12 Hz) cortical speech tracking in infants by recording EEG longitudinally from 60 participants when aged 4-, 7- and 11- months as they listened to nursery rhymes. After establishing stimulus-related neural signals in delta and theta, cortical tracking at each age was assessed in the delta, theta and alpha [control] bands using a multivariate temporal response function (mTRF) method. Delta-beta, delta-gamma, theta-beta and theta-gamma phase-amplitude coupling (PAC) was also assessed. Significant delta and theta but not alpha tracking was found. Significant PAC was present at all ages, with both delta and theta -driven coupling observed. [Display omitted]

The strengths and limitations of the major methods developed to measure telomere lengths (TLs) in cells and tissues are presented in this review. These include Q-PCR (Quantitative Polymerase Chain Reaction), TRF (Terminal Restriction Fragment) analysis, a variety of Q-FISH (Quantitative Fluorescence In Situ Hybridization) methods, STELA (Single TElomere Length Analysis) and TeSLA (Telomere Shortest Length Assay). For each method, we will cover information about validation studies, including reproducibility in independent laboratories, accuracy, reliability and sensitivity for measuring not only the average but also the shortest telomeres. There is substantial evidence that it is the shortest telomeres that trigger DNA damage responses leading to replicative senescence in mammals. However, the most commonly used TL measurement methods generally provide information on average or relative TL, but it is the shortest telomeres that leads to telomere dysfunction (identified by TIF, Telomere dysfunction Induced Foci) and limit cell proliferation in the absence of a telomere maintenance mechanism, such as telomerase. As the length of the shortest telomeres is a key biomarker determining cell fate and the onset of senescence, a new technique (TeSLA) that provides quantitative information about all the shortest telomeres will be highlighted. This article is part of the theme issue ‘Understanding diversity in telomere dynamics’.

We examine several of the choices that went into the design of tDRmapper, a recently reported tool for identifying transfer RNA (tRNA) fragments in deep sequencing data, evaluate them in the context of currently available knowledge, and discuss their potential impact on the output that the tool generates.

The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated “decoding” strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response. •CCA is a powerful, easy to use method for multichannel data analysis.•CCA finds an optimal linear model to relate stimulus and brain response.•Multiple speech components map to distinct spectro-spatial signatures.•CCA yields large stimulus-response correlation values.•CCA supports good performance in a classification task.

Background tRFs, 14 to 32 nt long single-stranded RNA derived from mature or precursor tRNAs, are a recently discovered class of small RNA that have been found to be present in diverse organisms at read counts comparable to miRNAs. Currently, there is a debate about their biogenesis and function. Results This is the first meta-analysis of tRFs. Analysis of more than 50 short RNA libraries has revealed that tRFs are precisely generated fragments present in all domains of life (bacteria to humans), and are not produced by the miRNA biogenesis pathway. Human PAR-CLIP data shows a striking preference for tRF-5s and tRF-3s to associate with AGO1, 3 and 4 rather than AGO2, and analysis of positional T to C mutational frequency indicates these tRFs associate with Argonautes in a manner similar to miRNAs. The reverse complements of canonical seed positions in these sequences match cross-link centered regions, suggesting these tRF-5s and tRF-3s interact with RNAs in the cell. Consistent with these results, human AGO1 CLASH data contains thousands of tRF-5 and tRF-3 reads chimeric with mRNAs. Conclusions tRFs are an abundant class of small RNA present in all domains of life whose biogenesis is distinct from miRNAs. In human HEK293 cells tRFs associate with Argonautes 1, 3 and 4 and not Argonaute 2 which is the main effector protein of miRNA function, but otherwise have very similar properties to miRNAs, indicating tRFs may play a major role in RNA silencing.

Cognitive neuroscience, in particular research on speech and language, has seen an increase in the use of linear modeling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits under more naturalistic conditions. However, studying clinical (and often highly heterogeneous) cohorts introduces an added layer of complexity to such modeling procedures, potentially leading to instability of such techniques and, as a result, inconsistent findings. Here, we outline some key methodological considerations for applied research, referring to a hypothetical clinical experiment involving speech processing and worked examples of simulated electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing, stimulus feature extraction, model design, model training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate the implementation of each step in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied research. In doing so, we hope to provide better intuition on these more technical points and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically rich stimuli.