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Biology subjects

Yu, R.

Publications and source records attributed to Yu, R..

At least 73 records · Page 4Linked to original sources

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition (DDA) data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides, and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

bioinformatics↗

Modular Pooled Discovery of Synthetic Knockin Sequences to Program Durable Cell Therapies

Chronic stimulation can cause T cell dysfunction and limit efficacy of cellular immunotherapies. CRISPR screens have nominated gene targets for engineered T cells, but improved methods are required to compare large numbers of synthetic knockin sequences to reprogram cell functions. Here, we developed Modular Pooled Knockin Screening (ModPoKI), an adaptable platform for modular construction of DNA knockin libraries using barcoded multicistronic adaptors. We built two ModPoKI libraries of 100 transcription factors (TFs) and 129 natural and synthetic surface receptors. Over 20 ModPoKI screens across human TCR and CAR T cells in diverse conditions identified a transcription factor AP4 (TFAP4) construct to enhance long-term T cell fitness and anti-cancer function in vitro and in vivo. ModPoKIs modularity allowed us to generate a [~]10,000-member library of TF combinations. Non-viral knockin of a combined BATF-TFAP4 polycistronic construct further enhanced function in vivo. ModPoKI facilitates discovery of complex gene constructs to program cellular functions. HighlightsO_LIModular pooled knockins of hundreds of TF and surface receptor constructs combined with different antigen receptors C_LIO_LIChronic stimulation screens discover programs to improve T cell persistence C_LIO_LICombinatorial knockin screens with [~]10,000 transcription factor combinations C_LIO_LIBATF-TFAP4 dual knockin construct improves CAR T cell function in vitro and in vivo C_LI

immunology↗

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

Repulsive Sema3E-Plexin-D1 signaling coordinates both axonal extension and steering via activating an autoregulatory factor, Mtss1

Axon guidance molecules are critical for neuronal pathfinding because they regulate directionality and growth pace during nervous system development. However, the molecular mechanisms coordinating proper axonal extension and turning are poorly understood. Here, Metastasis Suppressor 1 (Mtss1), a membrane protrusion protein, ensured axonal extension while sensitizing axons to the Semaphorin 3E (Sema3E)-Plexin-D1 repulsive cue. Sema3E-Plexin-D1 signaling enhanced Mtss1 expression in projecting striatonigral neurons. Mtss1 localized to the neurite axonal side and regulated neurite outgrowth in cultured neurons. Mtss1 also aided Plexin-D1 trafficking to the growth cone, where it signaled a repulsive cue to Sema3E. Mtss1 ablation reduced neurite extension and growth cone collapse in cultured neurons. Mtss1-knockout mice exhibited fewer striatonigral projections and irregular axonal routes and these defects were recapitulated in Plxnd1- or Sema3e-knockout mice. These findings demonstrate that repulsive axon guidance activates an exquisite autoregulatory program coordinating both axonal extension and steering during neuronal pathfinding.

developmental biology↗

Molecular Control of Circuit Plasticity and the Permanence of Imprinted Odor Memory

Behavioral imprinting is a distinct form of learning that has a lifelong impact on social interactions and affectional behaviors1-4. Unlike other forms of memory, imprinting does not require conspicuous association of stimuli; exposure per se appears sufficient to induce memories that neither undergo extinction nor are altered by experience later in life. The site of storage of imprinted memory and the mechanisms that control its formation and permanence are unknown. Here we uncover a molecular mechanism that controls olfactory imprinting, which underlies behaviors including kin and nest recognition, maternal attachment, and homing5-10. We show that odor exposure during the perinatal period converts an innately aversive odor into a homing signal. The behavioral change is associated with odor-induced changes in the projection patterns of olfactory sensory neuron (OSN) expressing the cognate receptors for the exposed odor. We show that the Wnt signaling receptor Frizzled1 (Fzd1) acts as a master regulator of the critical period of OSN development and is responsible for closing the critical period to prevent further changes in the neural circuit. In Fzd1 knockout mice axon projection patterns are continually modified by sensory experience. As Fzd1 knockout abolishes the developmental critical period, it also abolishes odor imprinting. Specific knockout of Fzd1 in the OSNs have the same effect. Mechanistically, Fzd1 controls the critical period through an autoregulated shutdown and by controlling an activity-driven regulon in the OSNs. The transient expression and the subsequent downregulation of Fzd1 leads to the irreversible closure of the critical period to lock in circuits established during the critical period. The evidence suggests that imprinted odor memory is stored in the patterns of connectivity at the first synapse in the olfactory bulb. Early odor experience induces changes in the OSN projection to alter connectivity with innate circuits to establish a life-long memory.

neuroscience↗

Impaired Lef1 activation accelerates iPSC-derived keratinocytes differentiation in Progeria

Hutchinson-Gilford progeria syndrome (HGPS) is a detrimental premature aging disease caused by a point mutation in the human LMNA gene. This mutation results in the abnormal accumulation of a truncated pre-lamin A protein called progerin. Among the drastically accelerated signs of aging in HGPS patients, severe skin phenotypes such as alopecia and sclerotic skins always develop with the disease progression. Here, we study the HGPS molecular mechanisms focusing on early skin development by differentiating patient-derived induced pluripotent stem cells (iPSCs) to a keratinocyte lineage. Interestingly, HGPS iPSCs showed an accelerated commitment to the keratinocyte lineage than the normal control. To study potential signaling pathways that accelerated skin development in HGPS, we investigated the WNT pathway components during HGPS iPSCs-keratinocytes induction. Surprisingly, despite the unaffected {beta}-catenin activity, the expression of a critical WNT transcription factor LEF1 was diminished from an early stage in HGPS iPSCs-keratinocytes differentiation. Chromatin immunoprecipitation (ChIP) experiment further revealed strong bindings of LEF1 to the early-stage epithelial developmental markers K8 and K18 and that the LEF1 silencing by siRNA down-regulates the K8/K18 transcription. During the iPSCs-keratinocytes differentiation, correction of HGPS mutation by Adenine base editing (ABE), while in a partial level, rescued the phenotypes for accelerated keratinocyte lineage-commitment. ABE also reduced the cell death in HGPS iPSCs-derived keratinocytes. These findings brought new insight into the molecular basis and therapeutic application for the skin abnormalities in HGPS.

developmental biology↗

Multiplexed imaging mass cytometry reveals distincttumor-immune microenvironments linked to immunotherapyresponses in melanoma

Single-cell technologies have enabled extensive analysis of complex immune composition, phenotype and interactions within tumor, which is crucial in understanding the mechanisms behind cancer progression and treatment resistance. Unfortunately, the knowledge on cell phenotypes and their spatial interactions at present has only limited utilization in guiding pathological stratification on patients based on their immune microenvironments for better clinical decisions. Here we used imaging mass cytometry (IMC) to simultaneously quantify 35 proteins in a spatially resolved manner on tumor tissues from melanoma patients receiving anti-programmed cell death-1 (anti-PD-1) therapy. Unbiased single-cell analysis revealed highly dynamic tumor-immune microenvironments that are characterized with variable tumor and immune cell phenotypes and their organizations across and within melanomas, and identified distinct archetypes of melanoma microenvironments that are associated with benefit from anti-PD-1 therapy based on high-dimensional multicellular features. Our results demonstrate the utility of multiplex proteomic imaging technologies in studying complex molecular events in a spatially resolved manner for the development of new strategies for patient stratification and treatment outcome prediction.

immunology↗

Non-random organization of flux control mechanisms in yeast central metabolic pathways

Metabolic flux can be regulated by a variety of different mechanisms, but the organization of these mechanisms within the metabolic network has remained unknown. Here we test the hypothesis that flux control mechanisms are not distributed randomly in the metabolic network, but rather organized according to pathway. Combining proteomics, phosphoproteomics, and metabolic modeling, we report the largest collection of flux-enzyme-phosphoenzyme relationships to date in Saccharomyces cerevisiae. In support of the hypothesis, we show that (i) amino acid metabolic pathways are predominantly regulated by enzyme abundance stemming from transcriptional regulation; (ii) upper glycolysis and associated pathways, by inactivating enzyme phosphorylation; (iii) lower glycolysis and associated pathways, by activating enzyme phosphorylation; and (iv) glycolipid/glycophospholipid pathways, by a combination of enzyme phosphorylation and metabolic compartmentalization. We delineate the evolutionary history for the observed organization of flux control mechanisms in yeast central metabolic pathways, furthering our understanding of the regulation of metabolism and its evolution.

systems biology↗

Yn-situ: a robust single RNA molecule in situ detection method

We describe a cost-effective, highly sensitive, and quantitative method for in situ detection of single RNA molecules in tissue sections. This method, dubbed Yn situ, standing for Y-branched probe in situ hybridization, uses a single-strand DNA preamplifier with multiple initiation sites that trigger hybridization chain reaction (HCR) to detect polynucleotide. We characterized the performance of this method and compared it to other approaches in the postnatal mouse olfactory epithelia. We find that the Yn situ method, in conjunction with an improved fixation step, is sensitive enough to allow detection of single molecules using a single pair of probes targeting a short nucleotide sequence. A set of 5-probes can produce quantitative results with smaller puncta and higher signal-to-noise ratio than the 20-probe sets commonly required for HCR and RNA-Scope. We show that the high sensitivity and wide dynamic range allow quantification of genes expressed at different levels in the olfactory sensory neurons. We describe key steps of this method to enable broad utility by individual laboratories.

bioengineering↗

Serine/arginine-rich splicing factor 7 plays oncogenic roles through specific regulation of m6A RNA modification

Serine/Arginine-Rich Splicing Factor 7 (SRSF7), which is previously recognized as a splicing factor, has been revealed to play oncogenic roles in multiple cancers. However, the mechanisms underlying its oncogenic roles have not been well addressed. Here, based on N6-methyladenosine (m6A) co-methylation network analysis across diverse cell lines, we found SRSF7 positively correlated with glioblastoma cell-specific m6A methylation. We then proved SRSF7 is a novel m6A regulator that specifically facilitates the m6A methylation near its binding sites on the mRNAs involved in cell proliferation and migration through recruiting methyltransferase complex. Moreover, SRSF7 promotes the proliferation and migration of glioblastoma cells largely dependent on the m6A methyltransferase. The two single-nucleotide m6A sites on PBK are regulated by SRSF7 and partially mediate the effects of SRSF7 on glioblastoma cells through recognition by IGF2BP2. Together, our discovery revealed a novel role of SRSF7 in regulating m6A and timely confirmed the existence and functional importance of RNA binding protein (RBP) mediated specific regulation of m6A.

molecular biology↗

IMCellXMBD: A statistical approach for robust cell identification and quantification from imaging mass cytometry images

Imaging Mass Cytometry (IMC) has become a useful tool in biomedical research due to its capability to measure over 100 markers simultaneously. Unfortunately, some protein channels in IMC images can be very noisy, which may significantly affect the phenotyping results without proper data processing. We developed IMCellXMBD1, a highly effective and generalizable cell identification and quantification method for IMC images. IMCell performs denoising by subtracting an estimated background noise value from pixel values for each individual protein channel, identifies positive cells from negative cells by comparing the distribution between segmented cells and decoy cells, and normalize the protein expression levels of the identified positive cells for downstream data analysis. Experimental results demonstrate that our method significantly improves the reliability of cell phenotyping which is essential for using IMC in biomedical studies.

bioinformatics↗

Anaerobic biodegradation of chloroform and dichloromethane with a Dehalobacter enrichment culture

Chloroform (CF) and dichloromethane (DCM) are among the more commonly identified chlorinated aliphatic compounds found in contaminated soil and groundwater. Complete dechlorination of CF has been reported under anaerobic conditions by microbes that respire CF to DCM and others that biodegrade DCM. The objectives of this study were to ascertain if a commercially available bioaugmentation enrichment culture (KB-1(R) Plus) uses an oxidative or fermentative pathway for biodegradation of DCM; and to determine if the products from DCM biodegradation can support organohalide respiration of CF to DCM in the absence of an exogenous electron donor. In various treatments with the KB-1(R) Plus culture to which 14C-CF was added, the predominant product was 14CO2, indicating that oxidation is the predominant pathway for DCM. Recovery of 14C-DCM when biodegradation was still in progress confirmed that CF first undergoes reductive dechlorination to DCM. 14C-labeled organic acids, including acetate and propionate, were also recovered, suggesting that synthesis of organic acids provides a sink for the electron equivalents from oxidation of DCM. When the culture was washed to remove organic acids from prior additions of exogenous electron donor and only CF and DCM were added, the culture completely dechlorinated CF. The total amount of DCM added was not sufficient to provide the electron equivalents needed to reduce CF to DCM. Thus, the additional reducing power came via the DCM generated from CF reduction. Nevertheless, the rate of CF consumption was considerably slower in comparison to treatments that received an exogenous electron donor. IMPORTANCEChloroform (CF) and dichloromethane (DCM) are among the more commonly identified chlorinated aliphatic compounds found in contaminated soil and groundwater. One way to address this problem is to add microbes to the subsurface that can biodegrade these compounds. While microbes are known that can accomplish this task, less is known about the pathways used under anaerobic conditions. Some use an oxidative pathway, resulting mainly in carbon dioxide. Others use a fermentative pathway, resulting in formation of organic acids. In this study, a commercially available bioaugmentation enrichment culture (KB-1(R) Plus) was evaluated using carbon-14 labelled chloroform. The main product formed was carbon dioxide, indicating the use of an oxidative pathway. The reducing power gained from oxidation was shown to support reductive dechlorination of CF to DCM. The results demonstrate the potential to achieve full dechlorination of CF and DCM to nonhazardous products that are difficult to identify in the field.

microbiology↗

Amino acid and nucleotide metabolism shape the selection of trophic levels in animals

What an animal eats determines its trophic level (TL) in the food web. The diet of high-TL animals is thought to contain more energy because it contains higher levels of lipids. This however has not been systematically examined in the context of comprehensive metabolic networks of different animals. Here, we reconstruct species-specific genome-scale metabolic models (GEMs) of 32 animals, and calculate the maximum ATP production per unit of food for each animal. Surprisingly, we find that ATP production is closely associated with metabolic flux through central carbon metabolism and amino acid metabolism, while correlation with lipid metabolism is low. Further, metabolism of specific amino acids and nucleotides underlie maximum ATP production from food. Our analyses indicate that amino acid and nucleotide metabolism, rather than lipid metabolism, are major contributors to the selection of animal trophic levels, demonstrating that species-level metabolic flux plays key roles in trophic interactions and evolution.

ecology↗

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

bioinformatics↗

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis.

bioinformatics↗

PDX1 directs a core developmentally and evolutionarily conserved gene program in the pancreatic islet

Pancreatic and duodenal homeobox 1 (PDX1) is crucial for pancreas organogenesis, yet the dynamic changes in PDX1 targets in mouse or human pancreas development have not been examined. We integrated the PDX1 cistrome with cell lineage-specific gene expression in both mouse and human developing pancreas. We identified a core set of developmentally and evolutionarily conserved PDX1 bound genes that reveal the broad multifaceted role of PDX1 in pancreas development. Despite the well-known, dramatic changes in PDX1 function and expression, we showed that PDX1 binding is largely stable from embryonic pancreas to adult islet. This may point towards a dual role of PDX1, activating or repressing the expression of its targets at different ages, dependent on other functionally-congruent or directly-interacting partners. Our work also suggests that PDX1 functions not only in initiating pancreas differentiation, but also as a potential keepsake of the progenitor program in the adult beta cells.

developmental biology↗

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

In addition to specific regulatory circuits, gene expression is also regulated by global physiological cues such as the cell growth rate and metabolic parameters. Here we examine these global control mechanisms by analyzing an orthogonal multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and intracellular amino acids in 22 steady-state yeast cultures. Our model indicates that transcript and protein abundance are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but are independently controlled by metabolic parameters relating to amino acid and nucleotide availability. Genes in central carbon metabolism, however, are regulated independently of these global physiological cues. Our findings can be used to augment gene expression profiling analyses in the distantly related yeast Schizosaccharomyces pombe and a human cancer cell model. Our results provide a framework to analyze gene expression profiles to gain novel biological insights, a key goal of systems biology.

systems biology↗

Circadian benefits of exercise training reflect metabolic and structural flexibility

Structured AbstractO_ST_ABSObjectiveC_ST_ABSExercise is a critical component of a healthy lifestyle and a key strategy for the prevention and management of metabolic disease. Identifying molecular mechanisms underlying adaptation in response to chronic physical activity is of critical interest in metabolic physiology. Circadian rhythms broadly modulate metabolism, including muscle substrate utilization and exercise capacity. Here, we define the molecular and physiological changes induced across the daily cycle by voluntary low intensity daily exercise. MethodsWildtype c57BL6/J male and female mice were housed with or without access to a running wheel for six weeks. Maximum running speed was measured at four different zeitgeber times (ZTs, hours after lights on) using either electrical or manual stimulation to motivate continued running on a motorized treadmill. RNA isolated from plantaris muscles at six ZTs was sequenced to establish the impact of daily activity on genome-wide transcription. Patterns of gene expression were analyzed using Gene Set Enrichment Analysis (GSEA) and Detection of Differential Rhythmicity (DODR). Blood glucose, lactate, and ketones, and muscle and liver glycogen were measured before and after exercise. ResultsWe demonstrate that the use of mild electrical shocks to motivate running negatively impacts maximum running speed and describe a manual method to motivate running in rodent exercise studies. Using this method, we show that time of day influences the increase in exercise capacity afforded by six weeks of voluntary wheel running: when maximum running speed is measured at the beginning of the nighttime active period in mice, there is no measurable benefit from a history of daily voluntary running, while maximum increase in performance occurs at the end of the night. We show that daily voluntary exercise dramatically remodels the muscle circadian transcriptome. Finally, we describe daily rhythms in carbohydrate metabolism associated with the timedependent response to moderate daily exercise. ConclusionsCollectively, these data indicate that chronic nighttime physical activity dramatically remodels daily rhythms of muscle gene expression, which in turn support daily fluctuations in exercise performance.

physiology↗