Biology subjects

LI, G.

Publications and source records attributed to LI, G..

4 recordsLinked to original sources

bioRxiv2025

Contrasting genomic routes to domestication in Occidental and Oriental pears

The domestication of perennial fruit trees remains poorly understood compared with annual crops, which were shaped by strong bottlenecks and elevated genetic load. Pears (Pyrus spp.) provide an ideal model for exploring how long-lived, outcrossing crops evolved under human selection. Here, we combined high-coverage whole-genome resequencing of 396 wild and cultivated accessions from Occidental and Oriental pears with analyses of nucleotide and transposable element (TE) polymorphisms to reconstruct the demographic and adaptive history of pear domestication. Demographic inferences revealed weak or no domestication bottlenecks and extensive gene flow between wild and cultivated populations. In the Occidental lineage, dessert and perry P. communis cultivars underwent independent domestications from the same wild progenitor, P. pyraster, with divergent selection linked to fruit use. In the Oriental lineage, regionally independent domestications gave rise to Chinese and Japanese P. pyrifolia cultivars, shaped by both environmental adaptation and human selection. Selection scans identified lineage- and use-specific targets related to fruit texture, metabolism, and immunity. Contrary to the classical "cost of domestication" hypothesis, cultivated pears carried fewer deleterious variants than their wild relatives, suggesting efficient purging through selection and introgression. TE insertions mirrored population structure and occasionally occurred near selected genes, indicating a limited but detectable adaptive role. Together, our findings go beyond confirming the dual origins of Pyrus domestication to reveal contrasting demographic and adaptive pathways in Occidental and Oriental pears, illustrating independent adaptive trajectories in perennial crops, where long lifespan, self-incompatibility, and recurrent introgression shape distinctive genomic outcomes.

genomics↗

bioRxiv2023

Structural circuit plasticity by a neurotrophin with a Toll modifies dopamine-dependent behaviour

Experience shapes the brain, as neural circuits can be modified by neural stimulation or the lack of it. The molecular mechanisms underlying structural circuit plasticity and how plasticity modifies behaviour, are poorly understood. Subjective experience requires dopamine, a neuromodulator that assigns a value to stimuli, and it also controls behaviour, including locomotion, learning and memory. In Drosophila, Toll receptors are ideally placed to translate experience into structural brain change. Toll-6 is expressed in dopaminergic neurons (DANs), raising the intriguing possibility that Toll-6 could regulate structural plasticity in dopaminergic circuits. Drosophila neurotrophin-2 (DNT-2) is the ligand for Toll-6 and Kek-6, but whether it is required for circuit structural plasticity was unknown. Here, we show that DNT-2 expressing neurons connect with DANs, and they modulate each other. Loss of function for DNT-2 or its receptors Toll-6 and kinase-less Trk-like kek-6 caused DAN and synapse loss, impaired dendrite growth and connectivity, decreased synaptic sites and caused locomotion deficits. By contrast, over-expressed DNT-2 increased DAN cell number, dendrite complexity and promoted synaptogenesis. Neuronal activity modified DNT-2, it increased synaptogenesis in DNT-2-positive neurons and DANs, and over-expression of DNT-2 did too. Altering the levels of DNT-2 or Toll-6 also modified dopamine-dependent behaviours, including locomotion and long-term memory. To conclude, a feedback loop involving dopamine and DNT-2 sculpted the circuits engaged, and DNT-2 with Toll-6 and Kek-6 induced structural plasticity in this circuit modifying brain function and behaviour.

neuroscience↗

bioRxiv2020

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation (SV) events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. In the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. We demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence, and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. Availabilitygithub.com/CMUSchwartzLab/FISH_deconvolution Contactrussells@andrew.cmu.edu

bioinformatics↗

bioRxiv2018

Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

The availability of a large volume of chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets for various transcription factors (TF) has provided an unprecedented opportunity to identify all functional TF binding motifs clustered in the enhancers in genomes. However, the progress has been largely hindered by the lack of a highly efficient and accurate tool that is fast enough to find not only the target motifs, but also cooperative motifs contained in very large ChIP-seq datasets with a binding peak length of typical enhancers ([~] 1,000 bp). To circumvent this hurdle, we herein present an ultra-fast and highly accurate motif-finding algorithm, ProSampler, with automatic motif length detection. ProSampler first identifies significant k-mers in the dataset and combines highly similar significant k-mers to form preliminary motifs. ProSampler then merges preliminary motifs with subtle similarity using a novel graph-based Gibbs sampler to find core motifs. Finally, ProSampler extends the core motifs by applying a two-proportion z-test to the flanking positions to identify motifs longer than k. As the number of preliminary motifs is much smaller than that of k-mers in a dataset, we greatly reduce the search space of the Gibbs sampler compared with conventional ones. By storing flanking sequences in a hash table, we avoid extensive IO and the necessity of examining all lengths of motifs in an interval. When evaluated on both synthetic and real ChIP-seq datasets, ProSampler runs orders of magnitude faster than the fastest existing tools while more accurately discovering primary motifs as well as cooperative motifs than do the best existing tools. Using ProSampler, we revealed previously unknown complex motif occurrence patterns in large ChIP-seq datasets, thereby providing insights into the mechanisms of cooperative TF binding for gene transcriptional regulation. Therefore, by allowing fast and accurate mining of the entire ChIP-seq datasets, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

bioinformatics↗