Biology subjects

Li, X. C.

Publications and source records attributed to Li, X. C..

15 recordsLinked to original sources

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗

bioRxiv2026

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by substantial intratumoral heterogeneity. Previous research demonstrates that GBM may have complex cell origins. To elucidate the interplay between brain development and GBM progression, we developed Qombucha (Quadratic prOgraMming Based tUmor deConvolution with cell HierArchy), a computational framework that uses DNA methylation data to infer tumor cell-type composition and profiles of unobserved progenitor cells. Unprecedentedly, Qombucha incorporates a developmental cell hierarchy that models mature brain cell types and their progenitors. Applied to a large TCGA GBM dataset spanning the RTK I, RTK II, and MES TYP subtypes, Qombucha identifies a distinct cell type composition profile for each subtype and recapitulates known biological patterns, including elevated microglia infiltration in MES TYP tumors. It also identifies subtype-specific developmental programs and shows that higher progenitor-cell abundance is associated with poorer survival. Qombucha-imputed cell fractions map methylation profiles of tumor samples to a compact, 11-dimensional latent space; in an independent NCI GBM cohort, this compact representation improves subtype clustering and enables accurate subtype classification, achieving performance comparable to state-of-the-art models based on full methylation profiles with much higher dimensionality. These results suggest that tumor cellular composition captures the core biological axes along which GBM subtypes diverge.

bioinformatics↗

bioRxiv2026

Patient cerebral organoids capture Alzheimers disease proteomic biomarkers and drug targets

Patient iPSC-derived cerebral organoids are a leading human model of Alzheimers disease, yet their proteome has never been benchmarked against human disease. Clinical cohorts now nominate thousands of biomarkers and drug targets across three proteomic platforms, and whether patient organoids capture these candidates is unknown. Here, we profile AD and control cerebral organoids containing neurons, astrocytes, and microglia on the three platforms driving clinical discovery, mass spectrometry, SomaScan, and Olink, in both conditioned media and lysate. Benchmarked against 121 studies and clinical cohorts of over 17,000 plasma, CSF, and cortex samples, patient organoids detect almost every nominated candidate and reproduce the disease-associated change in roughly one in four of the most reproducible. This convergence spans plasma, CSF, and cortex, and extends to synaptic, mitochondrial, and proteostatic biology. We provide the first multi-platform reference proteome of a patient-derived AD model, establishing it as a translationally relevant system for studying AD.

neuroscience↗

bioRxiv2025

Colonization of vineyards by non-Saccharomyces yeast species without evolution of copper and sulfite resistance

Humans have generated ecological and environmental disturbances, such as vineyards, across the globe. Disturbed environments create widespread and repeated selective pressures that can drive colonization and local adaptation in microbial species. We investigated the distribution of fermentative yeast species in vineyards compared to nearby arboreal habitats and measured their resistance to two commonly used vineyard antimicrobials, copper and sulfite. We analyzed 4,101 strains, representing 70 species, collected from grapevine- and oak-associated substrates at 17 vineyard and 20 non-vineyard sites in the USA and Slovenia. Species frequency varied with geography and substrate, but the majority of species commonly present in vineyards were also found in non-vineyard arboreal environments, representing a potential source for vineyard colonization and exploitation of sugar from grapes. Species varied in both copper and sulfite resistance, but only Saccharomyces cerevisiae showed elevated resistance in vineyard compared to non-vineyard samples. Our results indicate that S. cerevisiae has uniquely taken advantage of vineyard environments through adaptations that appear either unnecessary or inaccessible to other yeast species present in vineyards.

evolutionary biology↗

bioRxiv2025

Transferring functional chromosomes between intergeneric yeast generates monochromosomal hybrids with improved phenotypes and transcriptional responses

Interspecific genetic exchanges caused by natural hybridization or horizontal gene transfer can lead to enhanced phenotypes, which are often of interest for industrial applications and evolutionary research. However, transferring genetic materials between distantly related species, such as intergeneric yeasts, presents technical challenges. In this study, we established a method to transfer individual chromosomes from Saccharomyces cerevisiae (Sc) into Kluyveromyces marxianus (Km), an emerging model for bioproduction. The Sc chromosome of interest was circularized, genetically modified to carry Km centromeres and replication origins, and transformed into Km via protoplast transformation. Using this method, we generated two synthetic strains, each containing a full set of Km chromosomes and either Sc chromosome I or III. The Sc chromosomes exhibited normal replication, segregation, and active transcription after the transfer. The synthetic strains displayed enhanced phenotypes in flocculation and salt tolerance, which were found to be caused by transgressive expression of FLO9 and SPS22 on the transferred Sc chromosomes, respectively. Transcriptomic analysis revealed that transgressive expression was prevalent among the transferred Sc genes, suggesting evolution of lineage-specific cis- and trans-regulatory interactions across a long evolutionary timescale. Our strategy has potential applications in optimizing cell factories, constructing synthetic genomes, and advancing evolutionary research.

synthetic biology↗

bioRxiv2024

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step towards tailoring healthcare interventions. However, the critical aspect of equitably selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to bridge the gap between molecular feature selection and algorithmic fairness, ensuring a fair selection of molecular features from all patient samples in a cohort. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of colorectal cancer patients subjected to low read coverage single-cell methylation sequencing. Our results demonstrate that FALAFL can rapidly and robustly determine the optimal set of CpG sites, which are each well covered by cells across the vast majority of the patients, while ensuring that in each patient a high proportion of these sites have good read coverage. An analysis of the FALAFL-selected sites reveals that their tumor lineage-informativeness exhibits a strong correlation across a spectrum of diverse patient profiles. Furthermore, these universally lineage-informative sites are highly enriched in the inter CpG island regions. FALAFL integrates equity considerations into the molecular feature selection from single-cell sequencing data obtained from a patient cohort. We hope that it will help propel equitable healthcare data science practices and contribute to the advancement of our understanding of complex diseases.

bioinformatics↗

bioRxiv2024

Pervasive sublethal effects of agrochemicals as contributing factors to insect decline

Insect biomass is declining across the globe at an alarming rate. Climate change and the widespread use of pesticides have been hypothesized as two underlying drivers. However, the lack of systematic experimental studies across chemicals and species limits our causal understanding of this problem. Here, we employed a chemical library encompassing 1024 different molecules--including insecticides, herbicides, fungicides, and plant growth inhibitors --to investigate how insect populations are affected by varying concentrations of pesticides, focusing on sublethal doses. Using a controlled laboratory pipeline for Drosophila melanogaster, we found that 57% of these chemicals affect the behavior of larvae at sublethal concentrations, and an even higher proportion compromises long-term survivability after acute exposure. Consistent with these results, we observed that exposure to chemicals at doses orders of magnitude below lethality induced widespread phosphorylation changes across the larval proteome. The effects of agrochemicals were amplified when the ambient temperature was increased by four degrees. We also tested the synergistic effects of multiple chemicals at doses found widely in nature and observed fitness-reducing changes in larval developmental time, behavior, and reproduction. Finally, we expanded our investigation to additional fly species, mosquitos, and butterflies and detected similar behavioral alterations triggered by pesticides at sublethal concentrations. Our results provide experimental evidence that strongly suggests sublethal doses of agrochemicals coupled with changes in environmental temperatures are contributing to the global decline in insect populations. We anticipate that our assays can contribute to improving chemical safety assessment, better protect the environment, secure food supplies, and safeguard animal and human health, as well as understand our rapidly changing world.

ecology↗

bioRxiv2022

Mutational scans reveal differential evolvability of Drosophila promoters and enhancers

Rapid enhancer and slow promoter evolution have been demonstrated through comparative genomics. However, it is not clear how this information is encoded genetically and if this can be used to place evolution in a predictive context. Part of the challenge is that our understanding of the potential for regulatory evolution is biased primarily toward natural variation or limited experimental perturbations. Here, to explore the evolutionary capacity of promoter variation, we surveyed an unbiased mutation library for three promoters in Drosophila melanogaster. We found that mutations in promoters had limited to no effect on spatial patterns of gene expression. Compared to developmental enhancers, promoters are more robust to mutations and have more access to mutations that can increase gene expression, suggesting that their low activity might be a result of selection. Consistent with these observations, increasing the promoter activity at the endogenous locus of shavenbaby led to increased transcription yet limited phenotypic changes. Taken together, developmental promoters may encode robust transcriptional outputs allowing evolvability through the integration of diverse developmental enhancers. Quote"Regulators, mount up [at transcriptional promoters]." - Warren G & Nate Dogg, 1994

evolutionary biology↗

bioRxiv2022

Laboratory evolution of flies to morphogen dosage via rapid maternal changes reveals predictable outcomes

Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges at a systems level remains limited. Here, we have examined the phenotypic consequences of disturbing the classic bicoid network in Drosophila, which is essential for anterior-posterior patterning in the early embryo. This network can be synthetically perturbed by increasing the dosage of bicoid, which causes a posterior shift of the networks regulatory outputs and a decrease in fitness. To directly monitor network changes across populations and time with extra copies of bicoid, we performed genome-wide EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level. In the future, such synthetic evolution approaches using animal models could provide a generalizable platform for studying the propagation of genetic perturbations across the many layers of complex multicellular systems.

developmental biology↗

bioRxiv2021

Epigenomic tumor evolution modeling with single-cell methylation data profiling

Recent studies exploring the impact of methylation in tumor evolution suggest that while the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Since changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumors single-cell methylation lineage tree and jointly identifying lineage-informative CpG sites which harbor changes in methylation status that are retained along the lineage. We apply Sgootr on the single-cell bisulfite-treated whole genome sequencing data of multiregionally-sampled tumor cells from 9 metastatic colorectal cancer patients made available by Bian et al., as well as multiregionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient made available by Chaligne et al.. We demonstrate that the tumor lineages constructed reveal a simple model underlying colorectal tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and more in concordance with the sequential-progression model of tumor evolution, in time a fraction of that used in prior studies. Interestingly, lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to CGIs, which have been the main regions of interest in genomic methylation-related analyses. Sgootr is implemented as a Snakemake workflow, available at https://github.com/algo-cancer/Sgootr. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/436475v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1d3920aorg.highwire.dtl.DTLVardef@11905d4org.highwire.dtl.DTLVardef@1cda90aorg.highwire.dtl.DTLVardef@114ea18_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

MotivationRecent advances in single cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program (ILP), or a constraint satisfaction program (CSP), which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain (MCMC) or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. ResultsWe introduce PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set. PhISCS-BnB not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a large melanoma data set derived from the sub-lineages of a cell line involving 24 clones with 3574 mutations, which returned the optimal tumor phylogeny in less than 2 hours. The resulting phylogeny also agrees with bulk exome sequencing data obtained from in vivo tumors growing out from the same cell line. Availabilityhttps://github.com/algo-cancer/PhISCS-BnB

bioinformatics↗

bioRxiv2019

Multiple changes underlie allelic divergence of CUP2 between Saccharomyces species.

Under the model of micromutationism, phenotypic divergence between species is caused by accumulation of many small-effect changes. While mapping the causal changes to single nucleotide resolution could be difficult for diverged species, genetic dissection via chimeric constructs allows us to evaluate whether a large-effect gene is composed of many small-effect nucleotide changes. In a previously described non-complementation screen, we found allele difference of CUP2, a copper-binding transcription factor, underlie divergence in copper resistance between Saccharomyces cerevisiae and S. uvarum. Here, we tested whether the allele effect of CUP2 was caused by multiple nucleotide changes. By analyzing chimeric constructs containing four separate regions in the CUP2 gene, including its distal promoter, proximal promoter, DNA binding domain and transcriptional activation domain, we found that all four regions of the S. cerevisiae allele conferred copper resistance, with the proximal promoter showing the largest effect, and that both additive and epistatic effects are likely involved. These findings support a model of multiple changes underlying evolution and suggest an important role of both protein coding and cis-regulatory changes in evolution.

evolutionary biology↗

bioRxiv2018

Mitochondria-encoded genes contribute to the evolution of heat and cold tolerance among Saccharomyces species

Over time, species evolve substantial phenotype differences. Yet, genetic analysis of these traits is limited by reproductive barriers to those phenotypes that distinguish closely related species. Here, we conduct a genome-wide non-complementation screen to identify genes that contribute to a major difference in thermal growth profile between two Saccharomyces species. S. cerevisiae is capable of growing at temperatures exceeding 40{degrees}C, whereas S. uvarum cannot grow above 33{degrees}C but outperforms S. cerevisiae at 4{degrees}C. The screen revealed only a single nuclear-encoded gene with a modest contribution to heat tolerance, but a large effect of the species mitochondrial DNA (mitotype). Furthermore, we found that, while the S. cerevisiae mitotype confers heat tolerance, the S. uvarum mitotype confers cold tolerance. Recombinant mitotypes indicate multiple genes contribute to thermal divergence. Mitochondrial allele replacements showed that divergence in the coding sequence of COX1 has a moderate effect on both heat and cold tolerance, but it does not explain the entire difference between the two mitochondrial genomes. Our results highlight a polygenic architecture for interspecific phenotypic divergence and point to the mitochondrial genome as an evolutionary hotspot for not only reproductive incompatibilities, but also thermal divergence in yeast.

evolutionary biology↗

bioRxiv2018

Mitochondrial DNA and temperature tolerance in lager yeasts

A growing body of research suggests that the mitochondrial genome (mtDNA) is important for temperature adaptation. In the yeast genus Saccharomyces, species have diverged in temperature tolerance, driving their use in high or low temperature fermentations. Here we experimentally test the role of mtDNA in temperature tolerance in synthetic and industrial hybrids (Saccharomyces cerevisiae x Saccharomyces eubayanus, or Saccharomyces pastorianus), which cold-brew lager beer. We find that the relative temperature tolerances of hybrids correspond to the parent donating mtDNA, allowing us to modulate lager strain temperature preferences. The strong influence of mitotype on the temperature tolerance of otherwise identical hybrid strains provides support for the mitochondrial climactic adaptation hypothesis in yeasts and demonstrates how mitotype has influenced the world's most commonly fermented beverage.\n\nOne Sentence SummaryMitochondrial genome origin affects the temperature tolerance of synthetic and industrial lager-brewing yeast hybrids.

genetics↗

bioRxiv2016

Directional evolution in temperature-responsive gene expression in yeast

Gene regulation is a ubiquitous mechanism by which organisms respond to their environment. While organisms are often found to be adapted to the environments they experience, the role of gene regulation in environmental adaptation is not often known. In this study, we examine divergence in cis-regulatory effects between two Saccharomyces species, S. cerevisiae and S. uvarum, that have substantially diverged in their thermal growth profile. We measured allele specific expression (ASE) in the species hybrid at three temperatures, the highest of which is lethal to S. uvarum but not the hybrid or S. cerevisiae. We find that S. uvarum alleles can be expressed at the same level as S. cerevisiae alleles at high temperature and most cis-acting differences in gene expression are not dependent on temperature. While a small set of 136 genes show temperature-dependent ASE, we find no indication that signatures of directional cis-regulatory evolution are associated with temperature. Within promoter regions we find binding sites enriched upstream of temperature responsive genes, but only weak correlations between binding site and expression divergence. Our results indicate that temperature divergence between S. cerevisiae and S. uvarum has not caused widespread divergence in cis-regulatory activity, but point to a small subset of genes where the species alleles show differences in magnitude or opposite responses to temperature. The difficulty of explaining divergence in cis-regulatory sequences with models of transcription factor binding sites and nucleosome positioning highlights the importance of identifying mutations that underlie cis-regulatory divergence between species.

genomics↗