Biology subjects

Zhang, H.

Publications and source records attributed to Zhang, H..

At least 469 records · Page 26Linked to original sources

bioRxiv2025

Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors

Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIrEcel1 and ZImPde11a), respectively. The GABAergic projection from ZIrEcel1 to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZImPde11a to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.

neuroscience↗

bioRxiv2025

Elucidation and de novo Reconstitution of Glyceollins Biosynthesis

Glyceollins are phytoalexins produced by soybeans in response to stressors such as pathogen invasion, injury, and environmental challenges. In addition to their antibacterial and antifungal activities, these compounds have attracted significant attention for their potential anticancer, anti-inflammatory, and antioxidant properties. However, their limited accessibility--due to the challenges and high costs associated with organic synthesis or purification from treated soybean seedlings--has hindered further physiological and biochemical studies. Moreover, the incomplete understanding of glyceollin biosynthesis, particularly the final cyclization steps, remains a major barrier to elucidating the physiological functions of glyceollins and achieving sustainable production through synthetic biology. In this study, we identified previously uncharacterized genes encoding two reductases for 7,2,4-trihydroxyisoflavanol (THI) biosynthesis and five P450 enzymes responsible for the final oxidative cyclization in glyceollin I, II, and III biosynthesis, thereby completing the entire glyceollin biosynthetic pathway. By reconstructing the pathway de novo through synthetic biology, we achieved the successful production of glyceollins from simple carbon sources in bakers yeast. This work advances the understanding of glyceollin biosynthesis in soybean, facilitates sustainable production in microbial hosts and offers new opportunities for their application in agriculture and biology.

biochemistry↗

bioRxiv2025

Disrupted spatial and contextual memory encoding in the hippocampus of 5xFAD mice precedes cognitive impairment

Alzheimers disease (AD) is characterized by progressive memory decline, yet how hippocampal representations change at early AD stages remains largely unknown. In this study, we utilized a real-world head-fixed spatial alternation task combined with in vivo two-photon imaging to investigate hippocampal CA1 representations in 5xFAD mice. While 7-9 month old 5xFAD mice showed significant deficits in task learning, 2-4 month old mice performed normally, enabling assessment of hippocampal coding prior to behavioral decline. At this early stage, CA1 neurons exhibited intact global spatial encoding but demonstrated impaired task-related place cell representations. Contextual representations revealed by trajectory-dependent coding were weakened at both the population and single cell levels, accompanied by a notable shift from predominantly retrospective to more prospective encoding during behavioral choices. During task learning, longitudinally unstable place cells showed greater impairments in spatial coding, whereas stable place cells remained largely preserved in early-stage AD mice. Additionally, chemogenetic activation of basal forebrain cholinergic neurons increased spatial information and within-session stability, while promoting trajectory-dependent coding in these early-stage 5xFAD mice. Together, these findings reveal selective early impairments in hippocampal task-related spatial and contextual representations in AD that precede overt deficits in spatial alternation performance and remains amenable to cholinergic modulation.

neuroscience↗

bioRxiv2025

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

bioRxiv2025

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present Worm Perturb-Seq (WPS), which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking pairwise modularity where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

genomics↗

bioRxiv2025

A Sensitive Soma-localized Red Fluorescent Calcium Indicator for Multi-Modality Imaging of Neuronal Populations In Vivo

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end-users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak {Delta}F/F0 per single AP compared to widely used jRGECO1a in neurons in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to four-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortex in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

neuroscience↗

bioRxiv2025

Intense oocyte competition builds the female reproductive reserve in mice

During the process of ovariogenesis, more than two-thirds of germ cells are sacrificed to enhance the quality of the remaining oocytes. But the detail process of selection is not completely understood in mammals. Here, by creating a high resolution 4- dimensional ovariogenesis imaging system, we recorded the progress of oocyte fate determination in live mouse ovaries, and discovered an oocyte competition mechanism that determines the survival of oocytes. We found that dominant oocytes catch and absorb the cell debris from sacrificed oocytes to enrich their cytoplasm for survival. Single-cell sequencing demonstrated that this competition follows a classic autophagy- driven model. Blocking competition by suppressing autophagy resulted in an enlarged pool of survived oocytes. However, these oocytes lacking competition were unable to fully develop and contribute to fertility. Our study suggests that mammals have evolved a unique intensive competition system to enhance oocyte quality, necessary to support their long reproductive lifespan.

developmental biology↗

bioRxiv2025

ScaleSC: A superfast and scalable single cell RNA-seq data analysis pipeline powered by GPU.

The rise of large-scale single-cell RNA-seq data has introduced challenges in data processing due to its slow speed. Leveraging advancements in GPU computing ecosystems, such as CuPy, and building on Scanpy and rapids-singlecell package, we developed ScaleSC, a GPU-accelerated solution for single-cell data processing. ScaleSC delivers over a 20x speedup through GPU computing and significantly improves scalability, handling datasets of 10-40 million cells with over 1000 batches by overcoming the memory bottleneck on a single A100 card- far surpassing rapids-singlecells capacity of processing only 1 million cells without multi-GPU support. We also resolved discrepancies between GPU and CPU algorithm implementations to ensure consistency. In addition to core optimizations, we developed new advanced tools for marker gene identification, cluster merging, and more, with GPU-optimized implementations seamlessly integrated. Designed for ease of use, the ScaleSC package is compatible with Scanpy workflows, requiring minimal adaptation from users. The ScaleSC package (https://github.com/interactivereport/ScaleSC) promises significant benefits for the single-cell RNA-seq computational community.

bioinformatics↗

bioRxiv2025

DeepSynBa: Actionable Drug Combination Prediction with Complete Dose-Response Profiles

Many cancer monotherapies demonstrate limited clinical efficacy, making combination therapies a relevant treatment strategy. The extensive number of potential drug combinations and context-specific response profiles complicates the prediction of drug combination responses. Existing computational models are typically trained to predict a single aggregated synergy score, which summarises drug responses across different dosage combinations, such as Bliss or Loewe scores. This oversimplification of the drug-response surface leads to high prediction uncertainty and limited actionability, as these models fail to distinguish between potency and efficacy. We introduce DeepSynBa, an actionable model that predicts the complete dose-response matrix of drug pairs instead of relying on an aggregated synergy score. This is achieved by predicting parameters describing the response surface as an intermediate layer in the model. Evaluated on the NCI-ALMANAC and the ONeil datasets, DeepSynBa outperforms the state-of-the-art methods in the dose-response matrix prediction task across most evaluation scenarios, including testing on novel drug combinations, cell lines, and drugs, across nine different tissue types. We also show that DeepSynBa yields reliable synergy score predictions. More importantly, DeepSynBa can predict drug combination responses across different dosages for untested combinations. The intermediate dose-response parameter layer enables the separation of efficacy from potency, informing the selection of dosage ranges that optimise efficacy while limiting off-target toxicity in experimental screens. The predictive capability and the downstream actionability make DeepSynBa a powerful tool for advancing drug combination research beyond the limitations of the current approaches. The code and the dataset for DeepSynBa are available at https://github.com/hikuru/DeepSynBa.

bioinformatics↗

bioRxiv2025

Unraveling and overcoming the ammonia toxicity in methanotrophs for sustainable biomanufacturing and methane removal

Replacement of nitrate with ammonium at large scale cultivation of methanotrophs can improve the economic feasibility of these bacteria in methane-based biomanufacturing and methane removal. However, ammonia toxicity and N2O emission impede this option. The mechanism of ammonia oxidation in methanotrophs remains elusive, limiting the effort to detoxify ammonia via genetic engineering. Using an industrially promising methanotroph as a model, we identified a porin PorA that facilitated ammonium uptake. Inactivation of PorA remarkably relieved ammonia toxicity and reduced N2O production. Meanwhile, we demonstrated that haoA, cytL and hcp contributed to ammonia detoxification and cytL was involved in the conversion of NH2OH to N2O. A mutant strain with increased ammonium-utilizing ability and decreased N2O emission was constructed. High growth rate and cell biomass were achieved in fed-batch fermentation with this strain using ammonium. These results deepen our understanding of ammonia oxidation in methanotrophs and promote their applications in biomanufacturing and methane removal. IMPORTANCEM. buryatense 5GB1C is becoming a methanotrophic model for both fundamental research and practical applications. Here, the genetic determinants of ammonia toxicity were identified and a porin gene that facilitated ammonium uptake was described for the first time, providing new understanding regarding the mechanism of nitrification in methanotrophs. Moreover, this study dramatically reduced ammonia toxicity and N2O production in this strain and enabled the use of ammonium as nitrogen source in fermentation, improving the sustainability of this strain in practical applications.

microbiology↗

bioRxiv2025

Targeting Runx1 protects against heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is a public health problem and an elusive illness for which there are few treatment options. HFpEF is a systemic condition with a broad phenotype including diastolic dysfunction, pulmonary oedema, exercise intolerance, and left ventricular (LV) hypertrophy, collectively resulting in enhanced morbidity and mortality. Master-regulator transcription factor RUNX1 has recently been identified as a mediator of pathological changes in many cardiac diseases, however its role in HFpEF was unknown. Here we show that inhibition of Runx1 limits adverse cardiac remodelling in a clinically relevant mouse model of HFpEF. Cardiomyocyte-specific tamoxifen-inducible Runx1-deficient mice with HFpEF are protected, with preservation of diastolic function, and attenuation of pulmonary oedema, exercise intolerance, and hypertrophy. Furthermore, targeting Runx1 in HFpEF by using gene transfer or small molecule inhibitors improves diastolic function, both in female and male mice. Overall, our research enhances our understanding of RUNX1 in cardiac disease and demonstrates a novel translational target for the treatment of HFpEF. Keywords: Heart failure with preserved ejection fraction, metabolic heart failure, diastolic dysfunction, hypertrophy, pulmonary oedema, exercise intolerance CLINICAL PERSPECTIVEHeart failure (HF) is a leading cause of death world-wide and traditionally divided into different subtypes according to cardiac ejection fraction (EF). In contrast to HF with reduced EF (HFrEF), there are limited treatment options for HF with preserved EF which is of considerable concern given that HFpEF is projected to become the dominant HF subtype in the future 1. RUNX1 has been demonstrated to play an important role in the development of many cardiac and non-cardiac diseases. As a result, the potential for RUNX1 inhibitors as therapeutic agents across various conditions has become increasingly evident. In this study we established the therapeutic potential of targeting RUNX1 in the context of HFpEF. Targeting RUNX1 in cardiomyocytes markedly attenuates the development of the HFpEF phenotype and therefore this novel translational therapeutic target has great potential to address one of the biggest challenges in cardiac research.

physiology↗

bioRxiv2025

Detection of Diffuse to Focal Myocardial Fibrosis by Cardiovascular Magnetic Resonance Against Histology in Mini-Swine

BackgroundMyocardial fibrosis predicts adverse outcomes in myocardial infarction (MI) and other cardiovascular conditions. Cardiovascular magnetic resonance (CMR) can non-invasively detect focal and diffuse myocardial fibrosis, but comprehensive validation against histology remains scarce. AimsTo assess the comparative diagnostic performance of CMR methods in detecting myocardial fibrosis, using a mini-swine model and histology as gold standard. MethodsEighteen mini-swine (16 MI; 2 healthy) underwent CMR cine, LGE, T1- and ECV-mapping. Two commonly-used T1-mapping methods - MOLLI 5(3)3 and ShMOLLI 5(1)1(1)1 - were included. Pathological sections were categorized as infarcted, peri-infarct, remote and healthy myocardium based on triphenyl tetrazolium chloride staining. Fibrotic burden was quantified by collagen volume fraction (CVF) into severe (CVF[≥]30%), moderate (CVF=10-25%), and mild (CVF=3-14%). The relationships between LGE, T1, ECV and CVF, and diagnostic performance using area-under-the-curve (AUC), were analyzed. ResultsFor detecting severe fibrosis, LGE, T1 and ECV all had excellent diagnostic performance (AUC: LGE=0.93, ECVShMOLLI=0.96, T1ShMOLLI=0.91, T1MOLLI=0.93, ECVMOLLI=0.88). ECVShMOLLI showed significantly better discriminatory accuracy than ECVMOLLI in detecting severe fibrosis and MI (both p<0.05), with the highest correlation to CVF (ECVShMOLLI r=0.86, ECVMOLLI r=0.82, T1ShMOLLI r=0.77, T1MOLLI r=0.77, semi-quantitative LGE r=0.75). Only T1ShMOLLI and ECVShMOLLI, but not LGE or T1MOLLI/ECVMOLLI, differentiated remote myocardium with mild fibrosis (CVF=8.23%) from healthy myocardium (CVF=2.01%). ConclusionsCMR can detect severe to mild myocardial fibrosis as validated against histology. For low-grade fibrosis, T1-mapping significantly outperformed LGE. Choice of CMR methodologies matters for myocardial fibrosis detection, which has importance for clinical trial design.

pathology↗

bioRxiv2025

Telomeric SUMO level influences the choices of APB formation pathways and ALT efficiency

Many cancers use an alternative lengthening of telomeres (ALT) pathway for telomere maintenance. ALT telomeric DNA synthesis occurs in ALT telomere-associated PML bodies (APBs). However, the mechanisms by which APBs form are not well understood. Here, we monitored the formation of APBs with time-lapse imaging employing CRISPR knock-in to track the promyelocytic leukemia (PML) protein at endogenous levels. We found APBs form via two pathways: telomeres recruit PML proteins to nucleate PML bodies de novo, or telomeres fuse with preformed PML bodies. Both nucleation and fusion of APBs require interactions between SUMO and SUMO interaction motifs (SIMs). Moreover, APB nucleation is associated with higher levels of SUMOs and SUMO-mediated recruitment of DNA helicase BLM, resulting in more robust telomeric DNA synthesis. Finally, further boosting SUMO levels at telomeres enhances APB nucleation, BLM enrichment, and telomeric DNA synthesis. Thus, high SUMO levels at telomeres promote APB formation via nucleation, resulting in stronger ALT activity.

cell biology↗

bioRxiv2025

Towards a Generative Paradigm for Large-scale Microbiome Analysis by Generative Language Model

Microbiome analysis has traditionally relied on taxonomic abundance tables, which, while effective, often constrain the exploration of deeper contextual relationships. In this study, we present MGM 2.0, a novel framework that applies advanced natural language processing (NLP) techniques to microbiome research. By reimagining microbiome samples as sentences and microbial species as words, MGM 2.0 enabled the extraction of nuanced patterns and relationships. The model demonstrated robust predictive performance in identifying exogenous species colonization (AUROC = 0.86). Additionally, through prompt-guided microbiome data generation, MGM 2.0 produced realistic microbial profiles conditioned on disease labels. The framework further revolutionized donor selection in fecal microbiota transplantation (FMT) by framing it as a sequence-to-sequence prediction task, enabling the prediction of post-transplantation community compositions and the identification of super donors for personalized treatments (average increase in C2R = 0.52). This innovative integration of NLP and microbiome science provides a versatile toolkit for predictive modeling, data generation, and personalized medicine. HighlightsO_LIIntroduced MGM 2.0, a generative language model utilizing sentence-like representation for microbiome analysis and generation. C_LIO_LIDemonstrated that sentence-like representation preserves sample distinctions, enabling accurate microbial sample classification tasks, such as colonization prediction. C_LIO_LIGenerated realistic, disease-specific microbiome profiles using a prompt-guided approach, validated by a novel "Microbiome Turing Test." C_LIO_LIApplied MGM 2.0 to fecal microbiota transplantation (FMT) donor selection, accurately predicting post-transplant community compositions and identifying potential "super donors" for personalized treatment strategies. C_LI

bioinformatics↗

bioRxiv2025

Spacer Extension Reconciles Specificity and Activity in High-Fidelity Cas9 Genome Editing

AbstractEngineering high-fidelity CRISPR enzymes often leads to reduced cleavage activity, creating a significant hurdle in balancing nuclease specificity and efficiency for clinical applications. Here, we demonstrate that extending the spacer to 21 or 22 nucleotides restores the impaired cleavage activity of SuperFi-Cas9, an engineered high-fidelity Cas9 variant with seven mutations at the PAM-distal region. Structural and mutational analyses reveal that the spacer extension strengthens additional interactions at the PAM-distal end, stabilizing the nuclease- sgRNA-DNA complex, which appears to be disturbed due to the seven mutations. This approach not only provides a high-fidelity Cas9 with uncompromised efficiency but also introduces a novel strategy to enhance CRISPR complex stability. Our findings offer a promising avenue for precise and efficient genome editing, crucial for advancing CRISPR technologies toward clinical translation.

genomics↗

bioRxiv2025

Systematic Determination of the Impact of Structural Edits on Accumulation into Mycobacteria

Understanding the factors that influence the accumulation of molecules beyond the mycomembrane of Mycobacterium tuberculosis (Mtb) - the main barrier to accumulation - is essential for developing effective antimycobacterial agents. In this study, we investigated two design principles commonly observed in natural products and mammalian cell-permeable peptides: backbone N-alkylation and macrocyclization. To assess how these structural edits impact molecule accumulation beyond the mycomembrane, we utilized our recently developed Peptidoglycan Accessibility Click-Mediated Assessment (PAC-MAN) assay for live-cell analysis. Our findings provide the first empirical evidence that peptide macrocyclization generally enhances accumulation in mycobacteria, while N-alkylation influences accumulation in a context-dependent manner. We examined these design principles in the context of two peptide antibiotics, tridecaptin A1 and griselimycin, which revealed the roles of N-alkylation and macrocyclization in improving both accumulation and antimicrobial activity against mycobacteria in specific contexts. Together, we present a working model for strategic structural modifications aimed at enhancing the accumulation of molecules past the mycomembrane. More broadly, our results also challenge the prevailing belief in the field that large and hydrophilic molecules, such as peptides, cannot readily traverse the mycomembrane.

microbiology↗

bioRxiv2025

Self-organization of high-dimensional geometry of neural activity in culture

A vast number of neurons exhibit high-dimensional coordination for brain computation, both in processing sensory input and in generating spontaneous activity without external stimuli. Recent advancements in large-scale recordings have revealed that this high-dimensional population activity exhibits a scale-free structure, characterized by power law and distinct spatial patterns in principal components (PCs). However, the mechanisms underlying the formation of this high-dimensional neural coordination remain poorly understood. Specifically, it is unclear whether the characteristic high-dimensional structure of population activity emerges through self-organization or is shaped by the learning of sensory stimuli in animals. To address this question and clearly differentiate between these two possibilities, we investigated large-scale neural activity in dissociated neuronal culture using high-density multi-electrode arrays. Our findings demonstrate that the high-dimensional structure of neural activity self-organizes during network development in the absence of explicit sensory stimuli provided to animals. As the cultures mature, the PC variance exhibits a power-law decay, and the spatial structures of PCs transition from global to localized patterns, driven by the temporal correlations of neural activity. Furthermore, we observed an unexpected co-occurrence between the power-law decay in PCA and neuronal avalanches, suggesting a link between self-organized criticality and high-dimensional activity. To uncover the mechanism behind this co-occurrence, we developed a new theoretical framework and demonstrated that it arises from heavy-tailed synaptic connectivity. By highlighting a developmental origin of the high-dimensional structure of neural activity, these findings deepen our understanding of how coordinated neural computations are achieved in the brain. SignificanceOne of the most intriguing questions in neuroscience is how the extremely high-dimensional yet efficiently orchestrated coordination of neural activity is organized. Central to this question lies the "nature versus nurture" debate: Is the high-dimensional coordination self-organized during development, or is it acquired through learning in animals? We take a novel approach by applying cutting-edge analytical techniques to study high-dimensional neural activity in dissociated neuronal cultures. Our findings reveal that fundamental features characterizing the high-dimensional coordination of neurons, including the scale-free geometry of the neural manifold and the intrinsic spatial structure of neural dynamics, are self-organized without sensory stimuli. Remarkably, we also discover that this high-dimensional geometry emerges concurrently with neuronal avalanches, a hallmark of self-organized criticality in neural circuits.

neuroscience↗

bioRxiv2025

AUTO-SP: automated sample preparation for analyzing proteins and protein modifications

Liquid chromatography (LC) tandem mass spectrometry (MS/MS) is one of the widely used proteomic techniques to study the alterations occurred at protein expression level as well as post-translation modifications (PTMs) of proteins that are relevant to different physiological or pathological statuses. The mass spectrometric analysis of peptides fragmented from proteins (bottom-up proteomics) has emerged as one of the major approaches for proteomics. In this approach, proteins are first cleaved into peptides for mass spectrometric analysis and peptides with PTMs are further enriched followed by the LC-MS/MS analysis. To achieve a reproducible and quantitative proteomic characterization, a well-established protease digestion and PTM peptide enrichment protocol is critical. In this study, we developed an automated sample preparation (AUTO-SP) for analyzing proteins and protein modifications utilizing Patient-Derived Xenograft (PDX) breast cancer tumors (basal-like and luminal subtypes). The protein amount was quantified and proteins were further digested by using AUTO-SP for each PDX sample. Based on the data-independent acquisition (DIA)-MS data, we observed samples of the same breast cancer subtypes were highly correlated (>0.98). Additionally, >14,000 ubiquitinated peptides were identified in the PDX samples when using AUTO-SP for ubiquitin enrichment, while unique pathways were enriched from the basal-like and luminal subtypes. AUTO-SP demonstrated its efficacy to provide reliable and reproducible sample preparation procedure for MS-based proteomic and PTM analyses.

systems biology↗