Human and chimpanzee genomes are almost identical, yet humans express higher brain capabilities. Deciphering the basis for this superiority is a long sought-after challenge. Adenosine-to-inosine (A-to-I) RNA editing is a widespread modification of the transcriptome. The editing level in humans is significantly higher compared with nonprimates, due to exceptional editing within the primate-specific Alu sequences, but the global editing level of nonhuman primates has not been studied so far. Here we report the sequencing of transcribed Alu sequences in humans, chimpanzees, and rhesus monkeys. We found that, on average, the editing level in the transcripts analyzed is higher in human brain compared with nonhuman primates, even where the genomic Alu structure is unmodified. Correlated editing is observed for pairs and triplets of specific adenosines along the Alu sequences. Moreover, new editable species-specific Alu insertions, subsequent to the humanchimpanzee split, are significantly enriched in genes related to neuronal functions and neurological diseases. The enhanced editing level in the human brain and the association with neuronal functions both hint at the possible contribution of A-to-I editing to the development of higher brain function. We show here that combinatorial editing is the most significant contributor to the transcriptome repertoire and suggest that Alu editing adapted by natural selection may therefore serve as an alternate information mechanism based on the binary A/I code.

A-To-I RNA editing is common to all eukaryotes, associated with various neurological functions. Recently, A-to-I editing was found to occur abundantly in the human transcriptome. Here we show that the frequency of A-to-I editing in humans is at least an order of magnitude higher as that of mouse, rat, chicken or fly. The extraordinary frequency of RNA editing in human is explained by the dominance of the primatespecific Alu element in the human transcriptome, which increases the number of double-stranded RNA substrates. 2 2 A-to-I RNA editing is the site-specific modification of adenosine to inosine in stemloop structures within precursor messenger RNAs, catalyzed by members of the doublestranded-RNA (dsRNA) specific ADAR (adenosine deaminase acting on RNA)family [1]. ADAR-mediated RNA editing is essential for the development and normal life of both invertebrates and vertebrates [2][3][4][5]. Altered editing patterns were associated with inflammation[6], epilepsy [7], depression [8], amyotrophic lateral sclerosis (ALS)[9] and malignant gliomas [10]. In a few known examples, editing changes an aminoacid in the translated protein, resulting in a change in its function. However, it was suggested that this might not the primary role of editing by ADARs[4], as most documented editing events occur within UTRs and intronic regions [11]. These editing events may affect splicing, RNA localization, RNA stability and translation [12], but full understanding of the purpose of editing in these regions is yet elusive.Using a combination of bioinformatics to search for potential stem loop structures in transcripts combined with differences between EST and genomic sequences we have recently reported the identification of abundant A-to-I editing in human, affecting more than 1600 different genes [13]. Most of these editing sites reside in Alu elements within UTR regions [13,14]. Alu elements are short interspersed elements (SINEs), typically 300 nucleotides long, which account for >10% of the human genome. Despite being considered genetically functionless, Alu elements have been suggested to have broad evolutionary impacts [15,16]. They are found in all primates but in no other organism [17,18]. Therefore, they were suggested to play a role in the evolution of primates [19,20]. However, the nature of this role is still under debate. The question thus arises whether the abundance of A-to-I editing sites in humans is related to some special 3 3 characteristics of the Alu repeat, and thus unique to primates, or whether similar editing patterns could also be observed in other organisms with a different, yet similar, composition of SINEs. Comparative search for RNA editing sitesIn this study, we have searched for A-to-I editing sites in human, mouse (Mus musculus), rat (Rattus norvegicus ), chicken (Gallus gallus) and fly (Drosophila melanogaster). We have found that the frequency of predicted A-to-I RNA editing in human is at least an order of magnitude higher than in other organisms. For this purpose, we used the algorithm ...

Objective The use of risk prediction models grows as electronic medical records become widely available. Here, we develop and validate a model to identify individuals at increased risk for colorectal cancer (CRC) by analyzing blood counts, age, and sex, then determine the model’s value when used to supplement conventional screening.Materials and Methods Primary care data were collected from a cohort of 606 403 Israelis (of whom 3135 were diagnosed with CRC) and a case control UK dataset of 5061 CRC cases and 25 613 controls. The model was developed on 80% of the Israeli dataset and validated using the remaining Israeli and UK datasets. Performance was evaluated according to the area under the curve, specificity, and odds ratio at several working points.Results Using blood counts obtained 3–6 months before diagnosis, the area under the curve for detecting CRC was 0.82 ± 0.01 for the Israeli validation set. The specificity was 88 ± 2% in the Israeli validation set and 94 ± 1% in the UK dataset. Detecting 50% of CRC cases, the odds ratio was 26 ± 5 and 40 ± 6, respectively, for a false-positive rate of 0.5%. Specificity for 50% detection was 87 ± 2% a year before diagnosis and 85 ± 2% for localized cancers. When used in addition to the fecal occult blood test, our model enabled more than a 2-fold increase in CRC detection.Discussion Comparable results in 2 unrelated populations suggest that the model should generally apply to the detection of CRC in other groups. The model’s performance is superior to current iron deficiency anemia management guidelines, and may help physicians to identify individuals requiring additional clinical evaluation.Conclusions Our model may help to detect CRC earlier in clinical practice.

Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at C1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a ''calendar year'' based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag Ò identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

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