Biology subjects

Lu, X.

Publications and source records attributed to Lu, X..

At least 325 records · Page 18Linked to original sources

bioRxiv2020

Isolation and characterization of SARS-CoV-2 from the first US COVID-19 patient

The etiologic agent of the outbreak of pneumonia in Wuhan China was identified as severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) in January, 2020. The first US patient was diagnosed by the State of Washington and the US Centers for Disease Control and Prevention on January 20, 2020. We isolated virus from nasopharyngeal and oropharyngeal specimens, and characterized the viral sequence, replication properties, and cell culture tropism. We found that the virus replicates to high titer in Vero-CCL81 cells and Vero E6 cells in the absence of trypsin. We also deposited the virus into two virus repositories, making it broadly available to the public health and research communities. We hope that open access to this important reagent will expedite development of medical countermeasures. Article SummaryScientists have isolated virus from the first US COVID-19 patient. The isolation and reagents described here will serve as the US reference strain used in research, drug discovery and vaccine testing.

microbiology↗

bioRxiv2020

Chromosome-Scale Genome Assembly Provides Insights into Speciation of Allotetraploid and Massive Biomass Accumulation of Elephant Grass (Pennisetum purpureum Schum.)

Elephant grass (Pennisetum purpureum Schum., AABB, 2n=4x=28), which is characterized as robust growth and high biomass, and widely distributed in tropical and subtropical areas globally, is an important forage, biofuels and industrial plant. We sequenced its allopolyploid genome and assembled 2.07 Gb (96.88%) into A and B sub-genomes of 14 chromosomes with scaffold N50 of 8.47 Mb. A total of 38,453 and 36,981 genes were annotated in A and B sub-genomes, respectively. A phylogenetic analysis with species in Pennisetum identified that the speciation of the allotetraploid occurred approximately 15 MYA after the divergence between S.italica and P. glaucum. Double whole-genome duplication (WGD) and polyploidization events resulted in large scale gene expansion, especially in the key steps of growth and biomass accumulation. Integrated transcriptome profiling revealed the functional differentiation between sub-genomes; A sub-genome contributed more to plant growth, development and photosynthesis whereas B sub-genome primarily offered functions of effective transportation and resistance to stimulation. The results uncovered enhanced cellulose and lignin biosynthesis pathways with 645 and 666 genes expanded in A and B sub-genomes, respectively. Our findings provided deep insights into the speciation and genetic basis of fast growth and high biomass accumulation in the species. The genetic, genomic, and transcriptomic resources generated in this study will pave the way for further domestication and selection of these economical species and making them more adaptive to industrial utilization.

genomics↗

bioRxiv2020

Genome-wide discovery of lupus genetic risk variant allelic regulatory activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3,073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we constructed a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 revealed 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second, larger class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

genomics↗

bioRxiv2020

Learning to Encode Cellular Responses to Systematic Perturbations with Deep Generative Models

Components of cellular signaling systems are organized as hierarchical networks, and perturbing different components of the system often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology. Here, we investigated the capability of deep generative models (DGMs) for modeling signaling systems and learning representations for transcriptomic profiles derived from cells under diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data. Both models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately depict how cellular signals are encoded. The resulting representations have broad applications in systems biology, such as studying the mechanism-of-action of drugs.

bioinformatics↗

bioRxiv2020

Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map

Do machine learning methods improve standard deconvolution techniques for gene expression data? This paper uses a unique new dataset combined with an open innovation competition to evaluate a wide range of gene-expression deconvolution approaches developed by 294 competitors from 20 countries. The objective of the competition was to separate the expression of individual genes from composite measures of gene pairs. Outcomes were evaluated using direct measurements of single genes from the same samples. Results indicate that the winning algorithm based on random forest regression outperformed the other methods in terms of accuracy and reproducibility. More traditional gaussian-mixture methods performed well and tended to be faster. The best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a useful resource for multiple applications.

genomics↗

bioRxiv2019

Fibroblast growth factor-2 prevents synaptic pathology in minimal hepatic encephalopathy via NRG1/ErbB4 signaling

BackgroundMinimal hepatic encephalopathy (MHE) is implicated in the impairment of memory function. Fibroblast growth factor-2 (FGF2) is involved in modulating synaptic and neuronal formation. MethodsThe aim of this study is to examined the impacts of FGF2 on MHE pathology. Our study addressed whether FGF2 could trigger neuregulin-1 (NRG1) release to ameliorate synaptic impairment in MHE rats and in primary cultured neurons. ResultsThe results showed the decreased FGF2 expression in MHE brains. After treatment with FGF2, secreted neuregulin-1 (NRG1) and ErbB4 were increased, and the interaction of the 2 proteins was enhanced. Additionally, treatment with FGF2 or NRG1 induced synaptic formation, with increase in the activity of synapse and the density of dendritic spine, through Sirt1. NRG1 signaling was prevented by administration of FGF2, which acts through the FGFR1 in MHE rats. Finally, intracerebroventricular injection with FGF2 or NRG1 mitigated the impairment of synaptogenesis. ConclusionThe data suggest that FGF2 may be a promising latent therapeutic reagent for MHE pathogenesis.

neuroscience↗

bioRxiv2019

LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis

BackgroundClinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients. MethodsWe enrolled 1372 subjects, including clinically diagnosed PTB patients, non-TB disease controls and healthy controls, in three cohorts (Screening, Selection and Validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and qRT-PCR in the Screening Cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the Selection Cohort. These models were evaluated by AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. The biological function of lncRNAs was interrogated using ELISA, lactate dehydrogenase release analysis and flow cytometry. ResultsThree differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHR variables (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. ENST00000497872 may regulate inflammatory cytokine production, cell death and apoptosis during MTB infection. ConclusionsLncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.

molecular biology↗

bioRxiv2019

MicroRNA-148a Regulates Low-Density Lipoprotein Metabolism by Repressing the (Pro)renin Receptor

High plasma LDL cholesterol (LDL-c) concentration is a major risk factor for atherosclerosis. Hepatic LDLR regulates LDL metabolism, and thereby plasma LDL-c concentration. Recently, we identified the (pro)renin receptor [(P)RR] as a novel regulator of LDL metabolism, which regulates LDLR degradation and hence its protein abundance and activity. In silicon analysis suggests that the (P)RR is a target of miR-148a. In this study we determined whether miR-148a could regulate LDL metabolism by regulating (P)RR expression in HepG2 and Huh7 cells. We found that miR-148a suppressed (P)RR expression by binding to the 3-untranslated regions (3-UTR) of (P)RR mRNA. Mutating the binding sites for miR-148a in the 3-UTR of (P)RR mRNA abolished the inhibitory effects of miR-148a on (P)RR expression. In line with our recent findings, reduced (P)RR expression resulted in decreased cellular LDL uptake, likely as a consequence of decreased LDLR protein abundance. Overexpressing the (P)RR prevented miR-148a-induced reduction in LDLR abundance and cellular LDL uptake. Our study supports a new concept that miR-148a is a regulator of (P)RR expression. By reducing (P)RR abundance, miR-148a decreases LDLR protein abundance and consequently cellular LDL uptake.

molecular biology↗

bioRxiv2019

Variation in the life history strategy of cells underlies tumor’s functional diversity

Classical r- vs. K-selection theory describes the trade-offs between high reproductive output and competitiveness and guides research in evolutionary ecology1-5. While its impact has waned in the recent past, cancer evolution may rekindle it6-10. Indeed, solid tumors are an ideal theater for r- and K-selection and, hence, a good testing ground for ideas on life-history strategy evolution11,12. In this study, we impose r- or K-selection on HeLa cells to obtain strongly proliferative r cells and highly competitive K cells. RNA-seq analysis indicates that phenotypic trade-offs in r and K cells are associated with distinct patterns of expression of genes involved in the cell cycle, adhesion, apoptosis, and contact inhibition. Both empirical observations and simulations based on an ecological competition model show that the trade-off between cell proliferation and competitiveness can evolve adaptively and rapidly in naive cell lines. It is conceivable that the contrasting selective pressure may operate in a realistic ecological setting of actual tumors. When the r and K cells are mixed in vitro, they exhibit strikingly different spatial and temporal distributions in the resultant cultures. Thanks to this niche separation, the fitness of the entire tumor increases. Our analyses of life-history trade-offs are pertinent to evolutionary ecology as well as cancer biology.

evolutionary biology↗

bioRxiv2019

Marizomib suppresses triple-negative breast cancer via proteasome and oxidative phosphorylation inhibition

Lacking effective targeted therapies, triple-negative breast cancer (TNBCs) is highly aggressive with development of metastasis especially brain, and remains clinically challenging breast cancer subtype to treat. Despite the survival dependency on the proteasome pathway genes, FDA-approved proteasome inhibitors induced minimal clinical response in breast cancer patients due to weak proteasome inhibition. Here, we show that a potent proteasome inhibitor Marizomib (Mzb) inhibits multiple proteasome catalytic activities and induces a better anti-tumor response in TNBC cell lines and patient-derived xenografts alone and in combination with the standard-of-care chemotherapy. Mechanistically, Mzb inhibits oxidative phosphorylation (OXPHOS) via PGC-1 suppression in conjunction with proteasome inhibition in TNBC cells. Mzb reduces lung and brain metastases by reducing the number of circulating tumor cells and the expression of multiple genes involved in the epithelial-to-mesenchymal transition. Furthermore, Mzb-induced OXPHOS inhibition upregulates glycolysis to meet the energetic demands of TNBC cells and, hence, combined inhibition of glycolysis with Mzb exposure leads to a synergistic anti-cancer activity. Collectively, our data provide a strong rationale for a clinical evaluation of Mzb in primary and metastatic TNBC patients.\n\nOne Sentence SummaryMarizomib inhibits primary tumor growth, and also reduces lung and brain metastases in pre-clinical models of triple-negative breast cancer.

cancer biology↗

bioRxiv2019

Deamidation in Moxetumomab Pasudotox Leading to Conformational Change and Immunotoxin Activity Loss

Asparagine deamidation is a common posttranslational modification in which asparagine is converted to aspartic acid or isoaspartic acid. By introducing a negative charge, deamidation could potentially impact the binding interface and biological activities of protein therapeutics. We identified a deamidation variant in moxetumomab pasudotox, an immunotoxin Fv fusion protein drug derived from a 38-kilodalton (kDa) truncated Pseudomonas exotoxin A (PE38) for the treatment of hairy-cell leukemia. Although the deamidation site, Asn-358, was outside of the binding interface, the modification had a significant impact on the biological activity of moxetumomab pasudotox. Surprisingly, the variant eluted earlier than its unmodified form on anion exchange chromatography, which suggests a higher positive charge. Here we describe the characterization of the deamidation variant with differential scanning calorimetry and hydrogen-deuterium exchange mass spectrometry, which revealed that the Asn-358 deamidation caused the conformational changes in the catalytic domain of the PE38 region. These results provide a possible explanation for why the deamidation affected the biological activity of moxetumomab pasudotox and suggest an approach that can be used for process control to ensure product quality and process consistency.\n\nStatement of SignificanceAsparagine deamidation can have a potentially significant impact on protein therapeutics. Previous studies have revealed that deamidation at a single site significantly reduces the biological activity of moxetumomab pasudotox, a recombinant anti-CD22 immunotoxin developed for the treatment of B-cell malignancies. Surprisingly, despite the fact that deamidation introduced a negative charge, the deamidated variant eluted earlier than its unmodified form on anion exchange chromatography. In order to understand these observations, we isolated the deamidated variant using an anion exchange column and characterized it by differential scanning calorimetry and hydrogen-deuterium exchange mass spectrometry. The results revealed the conformational change caused by the deamidation, which explains the diminished biological activity of the variant and its early elution time on anion exchange chromatography.

biophysics↗

bioRxiv2019

Kinetic Inference Resolves Epigenetic Mechanism of Drug Resistance in Melanoma

Drug-induced dedifferentiation towards a drug-tolerant persister state is a common mechanism cancer cells exploit to escape therapies, posing a significant obstacle to sustained therapeutic efficacy. The dynamic coordination of epigenomic and transcriptomic programs at the early-stage of drug exposure, which initiates and orchestrates these reversible dedifferentiation events, remains largely unexplored. Here we employ high-temporal-resolution multi-omics profiling, information-theoretic approaches, and dynamic system modeling to probe these processes in BRAF-mutant melanoma models and patient specimens. We uncover a hysteretic transition trajectory of melanoma cells in response to oncogene inhibition and subsequent release, driven by the sequential operation of two tightly coupled transcriptional waves, which orchestrate genome-scale chromatin state reconfiguration. Modeling of the transcriptional wave interactions predicts NF-{kappa}B/RelA-driven chromatin remodeling as the underlying mechanism of cell-state dedifferentiation, a finding we validate experimentally. Our results identify critical RelA-target genes that are epigenetically modulated to drive this process, establishing a quantitative epigenome gauge to measure cell-state plasticity in melanomas, which supports the potential use of drugs targeting epigenetic machineries to potentiate oncogene inhibition. Extending our investigation to other cancer models, we identify oxidative stress-mediated NF-{kappa}B/RelA activation as a common mechanism driving cellular transitions towards drug-tolerant persister states, revealing a novel and pivotal role for the NF-{kappa}B signaling axis in linking cellular oxidative stress to cancer progression.

cancer biology↗

bioRxiv2019

Genome-wide analysis reveals genetic diversity, linkage disequilibrium, and selection for milk traits in Chinese buffalo breeds

Water buffalo holds the tremendous potential of milk and meat that widespread throughout central and southern China. However, characterization of the population genetics of Chinese buffalo is poorly understood. Using Axiom(R) buffalo genotyping array, we performed the genetic diversity, linkage disequilibrium (LD) pattern and signature of selection in the 176 Chinese buffaloes from thirteen breeds. A total of 35,547 SNPs passed quality control and were used for further analyses. Population genetic analysis revealed a clear separation between the swamp and river types. Ten Chinese indigenous breeds clustered into the swamp group, Murrah and Nili-Ravi breeds were the river group, and the crossbred breed was closer to the river group. Genetic diversity analysis showed that the swamp group had a lower average expected heterozygosities compared to the river group. LD decay distance was much shorter in the swamp group compared with the river group with [Formula] value of approximately 50 Kb. Analysis of runs of homozygosity indicated that extensive remote and recent inbreeding activity was respectively found within swamp and river groups. Moreover, a total of 12 genomic regions under selection were detected between river and swamp groups. Further, 12 QTL regions were found associated with buffalo milk production traits. Some candidate genes within these QTLs were predicted to be involved in the cell structure and function, suggesting that these genes might play vital roles in the buffalo milk performance. Our data contribute to our understanding of the characterization of population genetics in Chinese buffaloes, which in turn may be utilized in buffalo breeding programs.\n\nAuthor SummaryIdentifying the causal genes or markers associated with important economic traits in livestock is critical to increasing the production level on the species. However, current understanding of the genetic basis for milk production traits in buffalo is limited. Here, we confirmed the divergent evolution, distinct population structure, and LD extent among Chinese buffalo breeds. We also identified 12 QTL regions associated with milk production traits in buffaloes using the selective sweeps and haplotype analysis. Further, a total of 7 genes involved in the cell structure and function were predicted within the identified QTLs. These findings suggested that these genes can serve as the candidate genes associated with buffalo milk production, which hold a vital role in the milk trait improvement of dairy buffalo industry.

genetics↗

bioRxiv2019

LncRNA GUARDIN suppresses cellular senescence through a LRP130-PGC1α-FOXO4-p21-dependent signaling axis

The long non-coding RNA GUARDIN functions to protect genome stability. Inhibiting GUARDIN expression can alter cell fate decisions towards senescence or apoptosis, but the underlying molecular signals are unknown. Here we show that GUARDIN is an essential component of a transcriptional repressor complex involving LRP130 and PGC1 which suppresses FOXO4 expression. GUARDIN acts as a scaffold to stabilize LRP130/PGC1 heterodimers and their occupancy at the FOXO4 promotor. Destabilizing this complex by silencing of GUARDIN, LRP130 or PGC1 leads to FOXO4-dependent upregulation of p21, thereby driving cells into senescence. We also found that GUARDIN expression was induced by rapamycin, a senolytic agent that suppresses cell senescence. FOS-Like Antigen 2 (FOSL2) acts as a transcriptional repressor of GUARDIN with increased levels in the presence of rapamycin resulting from downregulation of FOSL2. Together, these results demonstrate that GUARDIN inhibits p21-dependent senescence through a LRP130-PGC1-FOXO4 signaling axis and moreover, GUARDIN contributes to the anti-senolytic activities of rapamycin.

cell biology↗

bioRxiv2019

Regulating Soil Bacterial Diversity, Enzyme Activities and Community Composition Using Residues from Golden Apple Snails

Golden apple snails (GAS) have become a serious pest for agricultural production in Asia. A sustainable method for managing GAS is urgently needed, including potentially using them to produce commercial products. In this study, we evaluate the effects of GAS residues (shell and meat) on soil pH, bacterial diversity, enzyme activities, and other soil characteristics. Results showed that the amendment of GAS residues significantly elevated soil pH (to near-neutral), total organic carbon (TOC) (by 10-134%), NO3-N (by 46-912%), NH4-N (by 18-168%) and total nitrogen (TN) (by 12-132%). Bacterial diversity increased 13% at low levels of amendment and decreased 5% at high levels, because low-levels of GAS residues increased soil pH to near-neutral, while high-levels of amendment substantially increased soil nutrients and subsequently suppressed bacterial diversity. The dominant phyla of bacteria were: Proteobacteria (about 22%), Firmicutes (15-35%), Chloroflexi (12%-22%), Actinobacteria (8%-20%) Acidobacteria, Gemmatimonadetes, Cyanobacteria and Bacterioidetes. The amendment of GAS residues significantly increased the relative abundance of Firmicutes, Gemmatimonadetes, Bacterioidetes and Deinococcus-Thermus, but significantly decreased the relative abundance of Chloroflexi, Actinobacteria, Acidobacteria, Cyanobacteria and Planctomycetes. Our results suggest that GAS residues treatment induces a near-neutral and nutrient-rich soil. In this soil, soil pH may not be the best predictor of bacterial community composition or diversity; rather soil nutrients (ie., NH4-N and NO3-N) and soil TOC showed stronger correlations with bacterial community composition. Overall, GAS residues could replace lime for remediation of acidic and degraded soils, not only to remediate physical soil properties, but also microbial communities.\n\nImportanceThe wide spreading golden apple snail (GAS) is a harmful pest to crop productions and could result in soil and air pollutions after death. In the previous study, we developed a biocontrol method: adding GAS residues to acidic soil to mitigate the living GAS invasion and spread, improve soil quality, and reduce soil and air pollution. However, the effects of GAS residues amendment on bacterial diversity and community still remain unclear. This study provided insights into bacterial diversity and community compositions to facilitate the evaluation of GAS residues application.

ecology↗

bioRxiv2019

Single-cell profiling guided combinatorial immunotherapy for fast-evolving CDK4/6 inhibitor resistant HER2-positive breast cancer

Development of acquired resistance to targeted cancer therapy is one of the most significant clinical challenges. Acquiring resistance under drug selection pressure is a result of evolutionary adaptation to a complex and dynamic tumor microenvironment (TME). New therapy regimens combining CDK4/6 inhibitor are under active investigation in clinical trials to treat HER2+ breast cancer patients. In parallel with clinical trial settings, in this study, we sought to prospectively model the tumor evolution in response to a targeted therapy regimen in vivo and identify a clinically actionable strategy to combat potential acquired resistance. Notably, despite a promising initial response, acquired resistance emerged rapidly to the anti-Her2/Neu antibody plus CDK4/6 inhibitor Palbociclib combination treatment. By leveraging high-throughput single-cell analyses of the evolving tumors over the course of treatments, we revealed a distinct immunosuppressive immature myeloid cell (IMC) population infiltrated in the resistant TME. Guided by single-cell transcriptome analysis, we demonstrated a combinatorial immunotherapy of IMC-targeting tyrosine kinase inhibitor cabozantinib and immune checkpoint blockades enhanced anti-tumor immunity, and overcame the resistance. Further, sequential combinatorial immunotherapy enabled a sustained control of the rapidly evolving CDK4/6 inhibitor-resistant tumors. Our study demonstrates a translational framework for treating rapidly evolving tumors through preclinical modeling and single-cell analyses. Our findings provide a rationale for an immediate clinical proposition of combinatorial immunotherapy for HER2+ breast cancer as a strategy to mitigate the emergence of resistance.

cancer biology↗

bioRxiv2019

VSEPRnet: Physical structure encoding of sequence-based biomolecules for functionality prediction: Case study with peptides

Predicting structure-dependent functionalities of biomolecules is crucial for accelerating a wide variety of applications in drug-screening, biosensing, disease-diagnosis, and therapy. Although the commonly used structural \"fingerprints\" work for biomolecules in traditional informatics implementations, they remain impractical in a wide range of machine learning approaches where the model is restricted to make data-driven decisions. Although peptides, proteins, and oligonucleotides have sequence-related propensities, representing them as sequences of letters, e.g., in bioinformatics studies, causes a loss of most of their structure-related functionalities. Biomolecules lacking sequence, such as polysaccharides, lipids, and their peptide conjugates, cannot be screened with models using the letter-based fingerprints. Here we introduce a new fingerprint derived from valence shell electron pair repulsion structures for small peptides that enables construction of structural feature-maps for a given biomolecule, regardless of the sequence or conformation. The feature-map introduced here uses a simple encoding derived from the molecular graph - atoms, bonds, distances, bond angles, etc., that make up each of the amino acids in the sequence, allowing a Residual Neural network model to take greater advantage of information in molecular structure. We make use of the short peptides binding to Major-Histocompatibility-Class-I protein alleles that are encoded in terms of their extended structures to predict allele-specific binding-affinities of test-peptides. Predictions are consistent, without appreciable loss in accuracy between models for different length sequences, marking an improvement over the current models. Biological processes are heterogeneous interactions, which justifies encoding all biomolecules universally in terms of structures and relating them to their functionality. The capabilities facilitated by the model expands the paradigm in establishing structure-function correlations among small molecules, short and longer sequences including large biomolecules, and genetic conjugates that may include polypeptides, polynucleotides, RNAs, lipids, peptidoglycans, peptido-lipids, and other biomolecules that could be implemented in a wide range of medical and nanobiotechnological applications in the future.

bioinformatics↗

bioRxiv2019

Histone acetylome-wide association study of tuberculosis

Host-cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here, we describe the first histone acetylome-wide association study (HAWAS) of an infectious disease, based on genome-wide H3K27 acetylation profiling of peripheral granulocytes and monocytes from subjects with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Chinese discovery cohort, which were validated in a subsequent multi-ethnic cohort, thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression in a third cohort. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulated Akt-mTOR signaling and promoted Mtb clearance in vitro. We performed histone acetylation QTL analysis on the dataset and identified candidate causal variants for immune phenotypes. Our study serves as proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

immunology↗