Biology subjects

Huang, E.

Publications and source records attributed to Huang, E..

50 records · Page 3Linked to original sources

bioRxiv2021

Elamipretide (SS-31) Treatment Attenuates Age-Associated Post-Translational Modifications of Heart Proteins

It has been demonstrated that elamipretide (SS-31) rescues age-related functional deficits in the heart but the full set of mechanisms behind this have yet to be determined. We investigated the hypothesis that elamipretide influences post-translational modifications to heart proteins. The S-glutathionylation and phosphorylation proteomes of mouse hearts were analyzed using shotgun proteomics to assess the effects of aging on these post-translational modifications and the ability of the mitochondria-targeted drug elamipretide to reverse age-related changes. Aging led to an increase in oxidation of protein thiols demonstrated by increased S-glutathionylation of cysteine residues on proteins from Old (24 months old at the start of the study) mouse hearts compared to Young (5-6 months old). This shift in the oxidation state of the proteome was almost completely reversed by 8-weeks of treatment with elamipretide. Many of the significant changes that occurred were in proteins involved in mitochondrial or cardiac function. We also found changes in the mouse heart phosphoproteome that were associated with age, some of which were partially restored with elamipretide treatment. Parallel reaction monitoring of a subset of phosphorylation sites revealed that the unmodified peptide reporting for Myot S231 increased with age, but not its phosphorylated form and that both phosphorylated and unphosphorylated forms of the peptide covering cMyBP-C S307 increased, but that elamipretide treatment did not affect these changes. These results suggest that changes to thiol redox state and phosphorylation status are two ways in which age may affect mouse heart function, which can be restored by treatment with elamipretide.

cell biology↗

bioRxiv2021

Immunogenic SARS-CoV2 Epitopes Defined by Mass Spectrometry

SARS-CoV-2 infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID19, the disease caused by SARS-CoV-2, T cell responses are important in mediating recovery and immune-protection. The identification of immunogenic epitopes that can elicit a meaningful T cell response can be elusive. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes; however, our previous studies find that immunodominant SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing SARS-CoV-2. We postulated that immunogenic epitopes for SARS-CoV-2 are best defined by directly analyzing peptides eluted from the peptide-MHC complex and then validating immunogenicity empirically by determining if such peptides can elicit T cells recognizing SARS-CoV-2 antigen-expressing cells. Using a tandem mass spectrometry approach, we identified epitopes of SARS-CoV-2 derived not only from structural but also non-structural genes in regions highly conserved among SARS-CoV-2 strains including recently recognized variants. We report here, for the first time, several novel SARS-CoV-2 epitopes from membrane glycol-protein (MGP) and non-structure protein-13 (NSP13) defined by mass-spectrometric analysis of MHC-eluted peptides, provide empiric evidence for their immunogenicity to induce T cell response. Significance StatementCurrent state of the art uses putative epitope peptides based on in silico prediction algorithms to evaluate the T cell response among COVID-19 patients. However, none of these peptides have been tested for immunogenicity, i.e. the ability to elicit a T cell response capable of recognizing endogenously presented peptide. In this study, we used MHC immune-precipitation, acid elution and tandem mass spectrometry to define the SARS-CoV-2 immunopeptidome for membrane glycol-protein and the non-structural protein. Furthermore, taking advantage of a highly robust endogenous T cell (ETC) workflow, we verify the immunogenicity of these MS-defined peptides by in vitro generation of MGP and NSP13 peptide-specific T cells and confirm T cell recognition of MGP or NSP13 endogenously expressing cell lines.

immunology↗

bioRxiv2021

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulation

Precise Hox gene expression is crucial for embryonic patterning. Intra-Hox transcription factor binding and distal enhancer elements have emerged as the major regulatory modes controlling Hox gene expression. However, quantifying their relative contributions has remained elusive. Here, we introduce synthetic regulatory reconstitution, a novel conceptual framework for studying gene regulation and apply it to the HoxA cluster. We synthesized and delivered variant rat HoxA clusters (130-170 kilobases each) to an ectopic location in the mouse genome. We find that a HoxA cluster lacking distal enhancers recapitulates correct patterns of chromatin remodeling and transcription in response to patterning signals, while distal enhancers are required for full transcriptional output. Synthetic regulatory reconstitution is a generalizable strategy to decipher the regulatory logic of gene expression in complex genomes. One-Sentence SummaryReconstitution of gene regulation using large DNA constructs unravels the regulatory logic of a developmental gene locus.

synthetic biology↗

bioRxiv2021

Molecular diversity and lineage commitment of human interneuron progenitors

Cortical interneurons are indispensable for proper function of neocortical circuits. Changes in interneuron development and function are implicated in human disorders, such as autism spectrum disorder and epilepsy. In order to understand human-specific features of cortical development as well as the origins of neurodevelopmental disorders it is crucial to identify the molecular programs underlying human interneuron development and subtype specification. Recent studies have explored gene expression programs underlying mouse interneuron specification and maturation. We applied single-cell RNA sequencing to samples of second trimester human ganglionic eminence and developing cortex to identify molecularly defined subtypes of human interneuron progenitors and immature interneurons. In addition, we integrated this data from the developing human ganglionic eminences and neocortex with single-nucleus RNA-seq of adult cortical interneurons in order to elucidate dynamic molecular changes associated with commitment of progenitors and immature interneurons to mature interneuron subtypes. By comparing our data with published mouse single-cell genomic data, we discover a number of divergent gene expression programs that distinguish human interneuron progenitors from mouse. Moreover, we find that a number of transcription factors expressed during prenatal development become restricted to adult interneuron subtypes in the human but not the mouse, and these adult interneurons express species- and lineage-specific cell adhesion and synaptic genes. Therefore, our study highlights that despite the similarity of main principles of cortical interneuron development and lineage commitment between mouse and human, human interneuron genesis and subtype specification is guided by species-specific gene programs, contributing to human-specific features of cortical inhibitory interneurons.

neuroscience↗

bioRxiv2021

Gut microbiota features associated with Clostridioides difficile colonization in dairy calves

Diarrheal disease, a major cause of morbidity and mortality in dairy calves, is strongly associated with the health and composition of the gut microbiome. Clostridioides difficile is an opportunistic pathogen that proliferates and can produce enterotoxins when the host experiences gut dysbiosis. However, even asymptomatic colonization with C. difficile can be associated with differing degrees of microbiome disruption in a range of species, including people, swine, and dogs. Little is known about the interaction between C. difficile and the gut microbiome in dairy calves. In this study, we sought to define microbial features associated with C. difficile colonization in pre-weaned dairy calves less than 2 weeks of age. We characterized the fecal microbiota of 80 calves from 23 different farms using 16S rRNA sequencing and compared the microbiota of C. difficile-positive (n=24) and C. difficile-negative calves (n=56). Farm appeared to be the greatest source of variability in the gut microbiota. When controlling for calf age, diet, and farm location, there was no significant difference in Shannon alpha diversity (P= 0.50) or in weighted UniFrac beta diversity (P=0.19) between C. difficile-positive and -negative calves. However, there was a significant difference in beta diversity as assessed using Bray-Curtiss diversity (P=0.0077), and C. difficile-positive calves had significantly increased levels of Ruminococcus (gnavus group) (Adj. P=0.052), Lachnoclostridium (Adj. P=0.060), Butyricicoccus (Adj. P=0.060), and Clostridium sensu stricto 2 compared to C. difficile-negative calves. Additionally, C. difficile-positive calves had fewer microbial co-occurrences than C. difficile-negative calves, indicating reduced bacterial synergies. Thus, while C. difficile colonization alone is not associated with dysbiosis and is therefore unlikely to result in an increased likelihood of diarrhea in dairy calves, it may be associated with a more disrupted microbiota.

microbiology↗

bioRxiv2021

A tandem-repeat dimeric RBD protein-based COVID-19 vaccine ZF2001 protects mice and nonhuman primates

A safe, efficacious and deployable vaccine is urgently needed to control COVID-19 pandemic. We report here the preclinical development of a COVID-19 vaccine candidate, ZF2001, which contains tandem-repeat dimeric receptor-binding domain (RBD) protein with alum-based adjuvant. We assessed vaccine immunogenicity and efficacy in both mice and non-human primates (NHPs). ZF2001 induced high levels of RBD-binding and SARS-CoV-2 neutralizing antibody in both mice and NHPs, and also elicited balanced TH1/TH2 cellular responses in NHPs. Two doses of ZF2001 protected Ad-hACE2-transduced mice against SARS-CoV-2 infection, as detected by reduced viral RNA and relieved lung injuries. In NHPs, vaccination of either 25 g or 50 g ZF2001 prevented infection with SARS-CoV-2 in lung, trachea and bronchi, with milder lung lesions. No evidence of disease enhancement is observed in both models. ZF2001 is being evaluated in the ongoing international multi-center Phase 3 trials (NCT04646590) and has been approved for emergency use in Uzbekistan.

immunology↗

bioRxiv2020

Genetics of white color and iridophoroma in "Lemon Frost" leopard geckos

The squamates (lizards and snakes) are close relatives of birds and mammals, with more than 10,000 described species that display extensive variation in a number of important biological traits, including coloration, venom production, and regeneration. Due to a lack of genomic tools, few genetic studies in squamates have been carried out. The leopard gecko1, Eublepharis macularius, is a popular companion animal, and displays a variety of coloration patterns. We took advantage of a large breeding colony and used linkage analysis, synteny, and homozygosity mapping to investigate a spontaneous semi-dominant mutation, "Lemon Frost", that produces white coloration and causes skin tumors (iridophoroma). We localized the mutation to a single locus which contains a strong candidate gene, SPINT12,3, a tumor suppressor implicated in human skin cutaneous melanoma (SKCM) and over-proliferation of epithelial cells in mice and zebrafish4-16. Our work establishes the leopard gecko as a tractable genetic system and suggests that a tumor suppressor in melanocytes in humans can also suppress tumor development in iridophores in lizards.

genetics↗

bioRxiv2020

A mouse-adapted SARS-CoV-2 model for the evaluation of COVID-19 medical countermeasures

Coronaviruses are prone to emergence into new host species most recently evidenced by SARS-CoV-2, the causative agent of the COVID-19 pandemic. Small animal models that recapitulate SARS-CoV-2 disease are desperately needed to rapidly evaluate medical countermeasures (MCMs). SARS-CoV-2 cannot infect wildtype laboratory mice due to inefficient interactions between the viral spike (S) protein and the murine ortholog of the human receptor, ACE2. We used reverse genetics to remodel the S and mACE2 binding interface resulting in a recombinant virus (SARS-CoV-2 MA) that could utilize mACE2 for entry. SARS-CoV-2 MA replicated in both the upper and lower airways of both young adult and aged BALB/c mice. Importantly, disease was more severe in aged mice, and showed more clinically relevant phenotypes than those seen in hACE2 transgenic mice. We then demonstrated the utility of this model through vaccine challenge studies in immune competent mice with native expression of mACE2. Lastly, we show that clinical candidate interferon (IFN) lambda-1a can potently inhibit SARS-CoV-2 replication in primary human airway epithelial cells in vitro, and both prophylactic and therapeutic administration diminished replication in mice. Our mouse-adapted SARS-CoV-2 model demonstrates age-related disease pathogenesis and supports the clinical use of IFN lambda-1a treatment in human COVID-19 infections.

microbiology↗

bioRxiv2019

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue, but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients.\n\nWe designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide significantly accurate prediction. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.\n\nAUTHOR SUMMARYCancer is among the leading causes of death globally and perdition of the drug response of patients to different treatments based on their clinical and molecular profiles can enable individualized cancer medicine. Machine learning algorithms have the potential to play a significant role in this task; but, these algorithms are designed based the premise that a large number of labeled training samples are available, and these samples are accurate representation of the profiles of real tumors. However, due to ethical and technical reasons, it is not possible to screen humans for many drugs, significantly limiting the size of training data. To overcome this data scarcity problem, machine learning models can be trained using large databases of preclinical samples (e.g. cancer cell line cultures). However, due to the major differences between preclinical samples and real tumors, it is unclear how accurately such preclinical-to-clinical computational models can predict the clinical drug response of cancer patients.\n\nHere, first we systematically evaluate a variety of different linear and nonlinear machine learning algorithms for this particular task using two large databases of preclinical (GDSC) and tumor samples (TCGA). Then, we present a novel method called TG-LASSO that utilizes a new approach for explicitly incorporating the tissue of origin of samples in the prediction task. Our results show that TG-LASSO outperforms all other algorithms and can accurately distinguish resistant and sensitive patients for the majority of the tested drugs. Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity in each cancer type.

bioinformatics↗

bioRxiv2019

Quantitative mitochondrial DNA copy number determination using droplet digital PCR with single cell resolution: a focus on aging and cancer

Mitochondria are involved in a number of diverse cellular functions, including energy production, metabolic regulation, apoptosis, calcium homeostasis, cell proliferation and motility as well as free radical generation. Mitochondrial DNA (mtDNA) is present at hundreds to thousands of copies per cell in a tissue-specific manner. Importantly, mtDNA copy number also varies during aging and disease progression and therefore might be considered as a biomarker that mirrors alterations within the human body. Here we present a new quantitative, highly sensitive droplet digital PCR (ddPCR) method (ddMDM; droplet digital mitochondrial DNA measurement) to measure mtDNA copy number not only from cell populations but also from single cells. Our developed assay can generate data in as little as 3 hours, is optimized for 96-well plates and also allows the direct use of cell lysates without the need for DNA purification or nuclear reference genes. Importantly, we show that ddMDM is able to detect differences between samples whose mtDNA copy number was close enough as to be indistinguishable by other commonly used mtDNA quantitation methods. By utilizing ddMDM, we show quantitative changes in mtDNA content per cell across a wide variety of physiological contexts including cancer progression, cell cycle progression, human T cell activation, and human aging.

molecular biology↗

bioRxiv2019

Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

AbstractO_ST_ABSBackgroundC_ST_ABSHigh grade serous ovarian carcinoma shows marked intra-tumoral heterogeneity which is associated with decreased survival and resistance to platinum-based chemotherapy. Pre-treatment quantification of spatial tumor heterogeneity by multiple tissue sampling is not clinically feasible. Using standard-of-care CT imaging to non-invasively quantify heterogeneity could have high clinical utility and would be highly cost-effective. Texture analysis measures local variations in computed tomography (CT) image intensity. Haralick texture methods are typically used to capture the heterogeneity of entire lesions; however, this neglects the possible presence of texture habitats within the lesion, and the differences between metastatic sites. The primary aim of this study was to develop texture analysis of intra-site and inter-site spatial heterogeneity from standard-of-care CT images and to correlate these measures with clinical and genomic features in patients with HGSOC. Methods and findingsWe analyzed the data from a retrospective cohort of 84 patients with HGSOC consisting of 46 patients from Memorial Sloan Kettering Cancer Center (MSKCC) and 38 non-MSKCC cases selected from The Cancer Imaging Archive (TCIA). Inclusion criteria consisted of FIGO stage II-IV HGSOC, attempted primary cytoreductive surgery, intravenous contrast-enhanced CT of abdomen and pelvis performed prior to surgery and availability of molecular tumor data analysed as per the Cancer Genome Atlas (TCGA) Research Network ovarian cancer project. Manual segmentation and image analysis was performed on 463 metastatic tumor sites from 84 patients. In the MSKCC cohort the median number of tumor sites was 7 (interquartile range 5-9) and 4 (interquartile range 3-4) in the TCIA patients. Sub-regions were produced within each tumor site by grouping voxels with similar Haralick texture using the Kernel K-means method. We derived statistical measures of intra- and inter-site tumor heterogeneity (IISTH) including cluster sites entropy (cSE), cluster sites standard deviation (cluDev) and cluster sites dissimilarity (cluDiss) from sub-regions identified within and between individual tumor sites. Unsupervised clustering was used to group patient IISTH measures into low, medium, high, and ultra-high heterogeneity clusters from each cohort. The IISTH measure cluDiss was an independent predictor of progression-free survival (PFS) in multivariable analysis in both datasets (MSKCC hazard ratio [HR] 1.04, 95% CI 1.01-1.06, P = 0.002; TCIA HR 1.05, 95% CI 1.00-1.10, P = 0.049). Low and medium IISTH clusters were associated with longer PFS in multivariable analysis (MSKCC HR 2.94, 90% CI 1.29-6.70, P = 0.009, TCIA HR 5.94, 95% CI 1.05-33.6, P = 0.044). IISTH measures were robust to differences in the CT imaging systems. Average Haralick textures contrast (TCIA HR 1.08, 95% CI 1.01-1.10, P = 0.019) and homogeneity (TCIA HR 1.09, 95% CI 1.02-1.16, P = 0.008) were associated with PFS in mutivariate analysis only in the TCIA dataset. All other average Haralick textures and total tumor volume were not associated with PFS in either dataset. ConclusionsTexture measures of intra- and inter-site tumor heterogeneity from standard of care CT images are correlated with shorter PFS in HGSOC patients. These quantitative methods are independent of the CT imaging system and can thus be applied in clinical practice. The methodology proposed here enables the non-invasive quantification of intra-tumoral heterogeneity and disease stratification for future experimental medicine studies and clinical trials, particularly in cases where total tumour volume and averaged textures have low predictive power. Author summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LITumor heterogeneity is a feature of many solid malignancies including ovarian cancer. C_LIO_LIRecent genomic research suggests that intra-site tumor heterogeneity (heterogeneity within a single tumor site) and inter-site tumor heterogeneity (heterogeneity between different metastatic sites in the same patient) correlate with clinical outcome in HGSOC. C_LI What did the researchers do and find?O_LIWe developed quantitative and non-invasive image-analysis based measures for predicting outcome in HGSOC patients by combining image-based information from within and between multiple tumor sites. C_LIO_LIUsing datasets from two sources, we demonstrate that these image-based tumor heterogeneity measures predict progression free survival in patients with HGSOC. C_LI What do these findings mean?O_LINon-invasive measures of CT image heterogeneity may predict outcomes in HGSOC patients. C_LIO_LIWider application of these CT image heterogeneity measures could prove useful for stratifying patients to different therapies given that total tumour volume and averaged textures have low predictive power. C_LI

bioinformatics↗

bioRxiv2018

Single-cell in situ transcriptomic map of astrocyte cortical layer diversity

During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering. To determine the molecular architecture of the mammalian cortex, we developed a high content pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.

neuroscience↗

bioRxiv2018

Optical and physical mapping with local finishing enables megabase-scale resolution of agronomically important regions in the wheat genome

BackgroundNumerous scaffold-level sequences for wheat are now being released and, in this context, we report on a strategy for improving the overall assembly to a level comparable to that of the human genome.\n\nResultsUsing chromosome 7A of wheat as a model, sequence-finished megabase scale sections of this chromosome were established by combining a new independent assembly based on a BAC-based physical map, BAC pool paired end sequencing, chromosome arm specific mate-pair sequencing and Bionano optical mapping with the IWGSC RefSeq v1.0 sequence and its underlying raw data. The combined assembly results in 18 super-scaffolds across the chromosome. The value of finished genome regions is demonstrated for two approximately 2.5 Mb regions associated with yield and the grain quality phenotype of fructan carbohydrate grain levels. In addition, the 50 Mb centromere region analysis incorporates cytological data highlighting the importance of non-sequence data in the assembly of this complex genome region.\n\nConclusionsSufficient genome sequence information is shown to be now available for the wheat community to produce sequence-finished releases of each chromosome of the reference genome. The high-level completion identified that an array of seven fructosyl transferase genes underpins grain quality and yield attributes are affected by five f-box-only-protein-ubiquitin ligase domain and four root-specific lipid transfer domain genes. The completed sequence also includes the centromere.

genomics↗

bioRxiv2017

Identification of Pathways Associated with Chemosensitivity through Network Embedding

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype-phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a recent pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines known relationships among genes as encoded in a molecular network along with gene memberships of all pathways to determine a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes with basal expression levels in a panel of cell lines that are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes, compound chemical structure, as well as large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations, compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses about chemosensitivity pathways and help further study the mechanism of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.

pharmacology and toxicology↗