Biology subjects

Song, S.

Publications and source records attributed to Song, S..

7 recordsLinked to original sources

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2018

Bats adjust temporal features of echolocation calls but not those of communication calls in response to traffic noise

Summary statementThis study reveals the impact of anthropogenic noise on spectrally distinct vocalizations and the limitations of the acoustic masking hypothesis to explain the vocal response of bats to chronic noise.\n\nAbstractThe acoustic masking hypothesis states that auditory masking may occur if the target sound and interfering sounds overlap spectrally, and it suggests that animals exposed to noise will modify their acoustic signals to increase signal detectability. However, it is unclear if animals will put more effort into changing their signals that spectrally overlap more with the interfering sounds than when the signals overlap less. We examined the dynamic changes in the temporal features of echolocation and communication vocalizations of the Asian particolored bat (Vespertilio sinensis) when exposed to traffic noise. We hypothesized that traffic noise has a greater impact on communication vocalizations than on echolocation vocalizations and predicted that communication vocalization change would be greater than echolocation. The bats started to adjust echolocation vocalizations on the fourth day of noise exposure, including an increased number of call sequences, decreased number of calls, and vocal rate within a call sequence. However, there was little change in the duration of the call sequence. In contrast, these communication vocalization features were not significantly adjusted under noise conditions. These findings suggest that the degree of spectral overlap between noise and animal acoustic signals does not predict the level of temporal vocal response to the noise.

animal behavior and cognition↗

bioRxiv2018

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.\n\nMethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.\n\nResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.\n\nConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.

bioinformatics↗

bioRxiv2018

Emergence of V1 connectivity pattern and Hebbian rule in a performance-optimized artificial neural network

The connectivity pattern and function of the recurrent connections in the primary visual cortex (V1) have been studied for a long time. But the underlying mechanism remains elusive. We hypothesize that the recurrent connectivity is a result of performance optimization in recognizing images. To test this idea, we added recurrent connections within the first convolutional layer in a standard convolutional neural network, mimicking the recurrent connections in the V1, then trained the network for image classification using the back-propagation algorithm. We found that the trained connectivity pattern was similar to those discovered in biological experiments. According to their connectivity, the neurons were categorized into simple and complex neurons. The recurrent synaptic weight between two simple neurons is determined by the inner product of their receptive fields, which is consistent with the Hebbian rule. Functionally, the recurrent connections linearly amplify the feedforward inputs to simple neurons and determine the properties of complex neurons. The agreement between the model results and biological findings suggests that it is possible to use deep learning to further our understanding of the connectome.

neuroscience↗

bioRxiv2018

Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent Giant Cell Arteritis

BackgroundGiant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies. GCA is a heterogenous disease, symptoms and signs are variable thereby making it challenging to diagnose and often delaying diagnosis. A temporal artery biopsy is the gold standard to test for GCA, and there are currently no specific biochemical markers to categorize or aid diagnosis of the disease. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA.\n\nMethods and FindingsWe recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital (RVEEH), Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2-3 weeks, 6-8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting (MACS). RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls. Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, we identified two transcripts that remained differentially expressed after 12 months, namely SGTB, associated with neuronal apoptosis, and FCGR3A, which has been found in association with Takayasu arteritis (TA), another large vessel vasculitis. We detected genes that correlate with both symptoms and biochemical markers used in the acute setting for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes: a history of Polymyalgia Rheumatica, both visual disturbance and raised neutrophils at the time of presentation, bilateral blindness and death within 12 months. Altered IL32 gene expression could provide risk evaluation of GCA diagnosis at the time of presentation and give an indication of prognosis, which may influence management.\n\nConclusionsThis is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. These data significantly enhance the current knowledge of relevant biomarkers, their association with clinical prognostic markers, as well as potential candidates for detecting disease activity in whole blood samples. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide us in initiating appropriate patient management.

genomics↗

bioRxiv2017

Gene markers for exon capture and phylogenomics in ray-finned fishes

Gene capture coupled with the next generation sequencing has become one of the favorable methods in subsampling genomes for phylogenomic studies. Many target gene markers have been developed in plants, sharks, frogs, reptiles and others, but few have been reported in the ray-finned fishes. Here, we identified a suite of \"single-copy\" protein coding sequence (CDS) markers through comparing eight fish genomes, and tested them empirically in 83 species (33 families and 11 orders) of ray-finned fishes. Sorting through the markers according to their completeness and phylogenetic decisiveness in taxa tested resulted in a selection of 4,434 markers, which were proven to be useful in reconstructing phylogenies of the ray-finned fishes at different taxonomic level. We also proposed a strategy of refining baits (probes) design a posteriori based on empirical data. The markers that we have developed may fill a gap in the tool kit of phylogenomic study in vertebrates.

evolutionary biology↗

bioRxiv2017

Multilocus DNA barcoding - Species Identification with Multilocus Data

Species identification using DNA sequences, known as DNA barcoding has been widely used in many applied fields. Current barcoding methods are usually based on a single mitochondrial locus, such as cytochrome c oxidase subunit I (COI). This type of barcoding is not always effective when applied to species separated by short divergence times or that contain introgressed genes from closely related species. Herein we introduce a more effective multi-locus barcoding framework that is based on gene capture and \"next-generation\" sequencing and provide both empirical and simulation tests of its efficacy. We examine genetic distinctness in two pairs of fishes that are sister-species: Siniperca chuatsi vs. S. kneri and Sicydium altum vs. S. adelum, where the COI barcoding approach failed species identification in both cases. Results revealed that distinctness between S. chuatsi and S. kneri increased as more independent loci were added. By contrast S. altum and S. adelum could not be distinguished even with all loci. Analyses of population structure and gene flow suggested that the two species of Siniperca diverged from each other a long time ago but have unidirectional gene flow, whereas the two species of Sicydium are not separated from each other and have high bidirectional gene flow. Simulations demonstrate that under limited gene flow (< 0.00001 per gene per generation) and enough separation time (> 100000 generation), we can correctly identify species using more than 90 loci. Finally, we selected 500 independent nuclear markers for ray-finned fishes and designed a three-step pipeline for multilocus DNA barcoding.

evolutionary biology↗