Biology subjects

Keller, A.

Publications and source records attributed to Keller, A..

132 records · Page 8Linked to original sources

bioRxiv2018

Circulating small non-coding RNAs associated with age, sex, smoking, body mass and physical activity

Non-coding RNAs (ncRNA) are regulators of cell functions and circulating ncRNAs from the majority of RNA classes, such as miRNA, tRNA, piRNAs, lncRNA, snoRNA, snRNA and miscRNAs, are potential non-invasive biomarkers. Understanding how non-disease traits influence ncRNA expression is essential for assessing their biomarker potential.\n\nWe studied associations of common traits (sex, age, smoking, body mass, physical activity, and technical factors such as sample storage and processing) with serum ncRNAs. We used RNAseq data from 526 donors from the Janus Serum Bank and traits from health examination surveys. We identified associations between all RNA classes and traits. Ageing showed the strongest association with ncRNA expression, both in terms of statistical significance and number of RNAs, regardless of RNA class. Serum processing modifications and storage times significantly altered expression levels of a number of ncRNAs. Interestingly, smoking cessation generally restored RNA expression to non-smoking levels, although for some isomiRs, mRNA fragments and tRNAs smoking-related expression levels persisted.\n\nOur results show that common traits influence circulating ncRNA expression. Therefore it is clear that ncRNA biomarker analyses should be adjusted for age and sex. In addition, for specific ncRNAs identified in our study, analyses should also be adjusted for body mass, smoking, physical activity and serum processing and storage.

molecular biology↗

bioRxiv2017

Genetic variation across the human olfactory receptor repertoire alters odor perception

The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. Genetic variation in a single OR frequently associated with odorant perception, and we validated 10 cases in which in vitro OR function correlated with in vivo odorant perception using a functional assay. This more than doubles the published examples of this phenomenon. For eight of these 10 cases, reduced receptor function associated with reduced intensity perception. In addition, we used participant genotypes to quantify genetic ancestry and found that, in combination with single OR genotype, age and gender, we can explain between 10 and 20% of the perceptual variation in 15 olfactory phenotypes, highlighting the importance of single OR genotype, ancestry, and demographic factors in variation of olfactory perception.

genetics↗

bioRxiv2017

FENNEC - Functional Exploration of Natural Networks and Ecological Communities

O_LISpecies composition assessment of ecological communities and networks is an important aspect of biodiversity research. Yet often ecological traits of organisms in a community are more informative than scientific names only. Furthermore, other properties like threat status, invasiveness, or human usage are relevant for many studies, but cannot be evaluated from taxonomy alone. Despite public databases collecting such information, it is still a tedious manual task to enrich community analyses with such, especially for large-scaled data.\nC_LIO_LIThus we aimed to develop a public and free tool that eases bulk trait mapping of community data in a web browser, implemented with current standard web and database technologies.\nC_LIO_LIHere we present the FO_SCPLOWENNECC_SCPLOW, a workbench that eases the process of mapping publicly available trait data to the users communities in an automated process. Usage is either by a local self-hosted or a public instance (https://fennec.molecular.eco) covering exemplary traits. Alongside the software we also provide usage and hosting documentation as well as online tutorials.\nC_LIO_LIThe FO_SCPLOWENNECC_SCPLOW aims to motivate public trait data submission and its reuse in meta-analyses. Further, it is an open-source development project with the code freely available to use and open for community contributions (https://github.com/molbiodiv/fennec).\nC_LI

ecology↗

bioRxiv2017

A comprehensive profile of circulating RNAs in human serum

Non-coding RNA (ncRNA) molecules have fundamental roles in cells and many are also stable in body fluids as extracellular RNAs. In this study, we used RNA sequencing (RNA-seq) to investigate the profile of small non-coding RNA (sncRNA) in human serum. We analyzed 10 billion lllumina reads from 477 serum samples, included in the Norwegian population-based Janus Serum Bank (JSB). We found that the core serum RNA repertoire includes 258 micro RNAs (miRNA), 441 piwi-interacting RNAs (piRNA), 411 transfer RNAs (tRNA), 24 small nucleolar RNAs (snoRNA), 125 small nuclear RNAs (snRNA) and 123 miscellaneous RNAs (misc-RNA). We also investigated biological and technical variation in expression, and the results suggest that many RNA molecules identified in serum contain signs of biological variation. They are therefore unlikely to be random degradation by-products. In addition, the presence of specific fragments of tRNA, snoRNA, Vault RNA and Y_RNA indicates protection from degradation. Our results suggest that many circulating RNAs in serum can be potential biomarkers.

bioinformatics↗

bioRxiv2017

SMELL-S and SMELL-R: olfactory tests not influenced by odor-specific insensitivity or prior olfactory experience

Smell dysfunction is a common and underdiagnosed medical condition that can have serious consequences. It is also an early biomarker of Alzheimers disease that precedes detectable memory loss. Clinical tests that evaluate the sense of smell face two major challenges. First, human sensitivity to individual odorants varies significantly, leading to potential misdiagnosis of people with an otherwise normal sense of smell but insensitivity to the test odorant. Second, prior familiarity with odor stimuli can bias smell test performance. We have developed new non- semantic tests for olfactory sensitivity (SMELL-S) and olfactory resolution (SMELL-R) that overcome these challenges by using mixtures of odorants that have unfamiliar smells. The tests can be self-administered with minimal training and showed high test-retest reliability. Because SMELL-S uses odor mixtures rather than a single molecule, odor-specific insensitivity is averaged out. Indeed, SMELL-S accurately distinguished people with normal and dysfunctional smell. SMELL-R is a discrimination test in which the difference between two stimulus mixtures can be altered stepwise. This is an advance over current discrimination tests, which ask subjects to discriminate monomolecular odorants whose difference cannot be objectively calculated. SMELL-R showed significantly less bias in scores between North American and Taiwanese subjects than conventional semantically-based smell tests that need to be adapted and translated to different populations. We predict that SMELL-S and SMELL-R will be broadly effective in diagnosing smell dysfunction, including that associated with the earliest signs of memory loss in Alzheimers disease.\n\nSignificance statementCurrently available smell testing methods can misdiagnose subjects with lack of prior experience or insensitivity to the odorants used in the test. This introduces a source of bias into clinical tests aimed at detecting patients with olfactory dysfunction. We have developed smell tests that use mixtures of 30 molecules that average out the variability in sensitivity to individual molecules. Because these mixtures have unfamiliar odors, and the tests are non-semantic, their use eliminates differences in test performance due to the familiarity with the smells or the words used to describe them. The SMELL-S and SMELL-R tests facilitate smell testing of diverse populations, without the need to adapt the test stimuli.

neuroscience↗

bioRxiv2016

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience↗