Biology subjects

Johnson, K.

Publications and source records attributed to Johnson, K..

6 recordsLinked to original sources

bioRxiv2018

Speaker-normalized vowel representations in the human auditory cortex

Humans identify speech sounds, the fundamental building blocks of spoken language, using the same cues, or acoustic dimensions, as those that differentiate the voices of different speakers. The correct interpretation of speech cues is hence uncertain, and requires normalizing to the specific speaker. Here we assess how the human brain uses speaker-related contextual information to constrain the processing of speech cues. Using high-density electrocorticography, we recorded local neural activity from the cortical surface of participants who were engaged in a speech sound identification task. The speech sounds were preceded by speech from different speakers whose voices differed along the same acoustic dimension that differentiated the target speech sounds (the first formant; the lowest resonance frequency of the vocal tract). We found that the same acoustic speech sound tokens were perceived differently, and evoked different neural responses in auditory cortex, when they were heard in the context of different speakers. Such normalization involved the rescaling of acoustic-phonetic representations of speech, demonstrating a form of recoding before the signal is mapped onto phonemes or higher level linguistic units. This process is the result of auditory cortex sensitivity to the contrast between the dominant frequencies in speech sounds and those in their just preceding context. These findings provide important insights into the mechanistic implementation of normalization in human listeners. Moreover, they provide the first direct evidence of speaker-normalized speech sound representations in human parabelt auditory cortex, highlighting its critical role in resolving variability in sensory signals.

neuroscience↗

bioRxiv2018

Wolbachia-infected Drosophila prefer cooler temperatures

O_LITemperature plays a fundamental role in the dynamics of host-pathogen interactions.\nC_LIO_LIWolbachia is an endosymbiotic bacteria that infects about 40% of arthropod species, which can affect host behaviour and reproduction. Yet, the effect of Wolbachia on host thermoregulatory behaviour is largely unknown, despite its use in disease vector control programs in thermally variable environments.\nC_LIO_LIHere, we used a thermal gradient to test whether Drosophila melanogaster infected with Wolbachia strain wMelCS exhibit different temperature preferences (Tp) to uninfected flies.\nC_LIO_LIWe found that Wolbachia-infected flies preferred a cooler mean temperature (Tp = 25.06{+/-}0.25{degrees}C) than uninfected flies (Tp = 25.78{+/-}0.24{degrees}C).\nC_LIO_LIThis finding suggests that Wolbachia-infected hosts might seek out cooler microclimates to reduce exposure to and lessen the consequences of high temperatures. This finding has generated hypotheses that will be fruitful in areas of research for exploring the mechanisms by which the change in Tp occurs in this complex and significant host-pathogen-environment interaction.\nC_LI

pathology↗

bioRxiv2018

Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

Drug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naive Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

bioinformatics↗

bioRxiv2017

B-1a cells acquire their unique characteristics by bypassing the pre-BCR selection stage

B-1a cells are long-lived, self-renewing innate like B cells that predominantly inhabit the peritoneal and pleural cavities. In contrast to conventional B-2 cells they have a receptor repertoire that is biased towards bacterial and self-antigens, promoting a rapid response to infection and clearing of apoptotic cells. Although B-1a cells are known to primarily originate from fetal tissues the mechanisms by which they arise has been a topic of debate for many years. Here we show that in the fetal liver (FL) versus bone marrow (BM) environment, reduced IL-7R/STAT5 levels promote immunoglobulin kappa (Igk) recombination at the early pro-B cell stage. As a result, B cells can directly generate a mature B cell receptor (BCR) and bypass the requirement for a pre-BCR and pairing with surrogate light chain (SLC). This alternate pathway of development enables the production of B cells with self reactive, skewed specificity receptors that are peculiar to the B-1a compartment. Together our findings connect seemingly opposing models of B-1a cell development and explain how these cells acquire their unique properties.

immunology↗

bioRxiv2017

Autosomal recessive coding variants explain only a small proportion of undiagnosed developmental disorders in the British Isles

Large exome-sequencing datasets offer an unprecedented opportunity to understand the genetic architecture of rare diseases, informing clinical genetics counseling and optimal study designs for disease gene identification. We analyzed 7,448 exome-sequenced families from the Deciphering Developmental Disorders study, and, for the first time, estimated the causal contribution of recessive coding variation exome-wide. We found that the proportion of cases attributable to recessive coding variants is surprisingly low in patients of European ancestry, at only 3.6%, versus 50% of cases explained by de novo coding mutations. Surprisingly, we found that, even in European probands with affected siblings, recessive coding variants are only likely to explain ~12% of cases. In contrast, they account for 31% of probands with Pakistani ancestry due to elevated autozygosity. We tested every gene for an excess of damaging homozygous or compound heterozygous genotypes and found three genes that passed stringent Bonferroni correction: EIF3F, KDM5B, and THOC6. EIF3F is a novel disease gene, and KDM5B has previously been reported as a dominant disease gene. KDM5B appears to follow a complex mode of inheritance, in which heterozygous loss-of-function variants (LoFs) show incomplete penetrance and biallelic LoFs are fully penetrant. Our results suggest that a large proportion of undiagnosed developmental disorders remain to be explained by other factors, such as noncoding variants and polygenic risk.

genetics↗

bioRxiv2017

Local processing of visual information in neurites of VGluT3-expressing amacrine cells

Synaptic inputs to neurons are distributed across extensive neurite arborizations. To what extent arbors process inputs locally or integrate them globally is, for most neurons, unknown. This question is particularly relevant for amacrine cells, a diverse class of retinal interneurons, which receive input and provide output through the same neurites. Here, we used two-photon Ca2+ imaging to analyze visual processing in VGluT3-expressing amacrine cells (VG3-ACs), an important component of object motion sensitive circuits in the retina. VG3-AC neurites differed in their preferred stimulus contrast (ON vs. OFF); and ON and OFF responses varied in transience and preferred stimulus size. Contrast preference changed predictably with the laminar position of neurites in the inner plexiform layer. Yet, neurites at all depths were strongly activated by local but not by global image motion. Thus, VG3-AC neurites process visual information locally, exhibiting diverse responses to contrast steps, but uniform object motion selectivity.

neuroscience↗