Biology subjects

Lu, X.

Publications and source records attributed to Lu, X..

At least 253 records · Page 14Linked to original sources

bioRxiv2021

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (1) co-expression network analyses focusing on correlations between genes; (2) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: a slight change of parameterization or dataset could lead to dramatic alternations of outcomes. Here, we propose Stabilized Core gene and Pathway Election, or SCOPE, a tool integrating bootstrapped LASSO and co-expression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations towards complex interactions using transcriptome data. Availabilityhttps://github.com/QingrunZhangLab/SCOPE

bioinformatics↗

bioRxiv2021

RNA2Immune: A Manually Curated Database of Experimentally Supported Data Linking Noncoding RNA Regulation to the Immune System

Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis as well as vaccine responses. A repository of ncRNA-immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics for immune disorders as well as vaccines. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, immune disease, cancer immunology, and vaccines. The current version of RNA2Immune documents 50,433 immune-ncRNA associations in 42 host species, including: (i) 6690 ncRNA associations with immune functions involving 31 immune cell types; (ii) 38,672 ncRNA associations with 348 immune diseases; (iii) 4833 ncRNA associations with cancer immunology; and (iv) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching and downloading ncRNA-immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, the pathological consequences of dysregulation of these ncRNAs (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp.

immunology↗

bioRxiv2021

A novel Phytophthora sojae effector PsFYVE1 modulates transcription and alternative splicing of immunity related genes by targeting host RZ-1A protein

Oomycete pathogens secrete many effectors to manipulate plant immunity and promote infection. However, relatively few effector types have been well characterized. In this study, members of a FYVE domain-containing protein family that is highly expanded in oomycetes were systematically identified, and one secreted protein, PsFYVE1, was selected for further study. PsFYVE1 enhanced Phytophthora infection in Nicotiana benthamiana and was necessary for P. sojae virulence. The FYVE domain of PsFYVE1 had PI3P-binding activity that depended on four conservative amino acid residues. Furthermore, PsFYVE1 targeted RNA-binding proteins RZ-1A/1B/1C in N. benthamiana and soybean, and silencing of NbRZ-1A/1B/1C genes attenuates plant immunity. NbRZ-1A was associated with spliceosome that included three important components, NbGRP7, NbGRP8, and NbU1-70K. Notably, PsFYVE1 could disrupt NbRZ-1A-NbGRP7 interaction. RNA-seq and subsequent experimental analysis demonstrated that PsFYVE1 and NbRZ-1A not only co-regulated transcription of NbHCT, NbEIN2, and NbSUS4 genes but also modulated pre-mRNA alternative splicing (AS) of the NbNSL1 gene, which participated in plant immunity. Collectively, these findings indicate that the FYVE domain-containing protein family includes potential new effector types and also highlight that plant pathogen effectors can regulate plant immunity related genes at both transcription and AS levels to promote disease. Author summaryMany plant pathogenic oomycetes secrete effector proteins into plants to facilitate infection. Discovering potential repertoire of novel effectors and corresponding molecular mechanisms are major themes in the study of oomycete-plant interactions. Here, we characterized a FYVE domain-containing protein (PsFYVE1) in P. sojae. PsFYVE1 carries a functional secretory signal peptide and is a virulence-essential effector for P. sojae infection. We demonstrated that PsFYVE1 interacted with a class of plant RNA-binding proteins, including soybean GmRZ-1A/1B/1C and N. benthamiana NbRZ-1A/1B/1C. Silencing of NbRZ-1A/1B/1C proteins increased Phytophthora infection and suppressed plant defense. Furthermore, NbRZ-1A interacted with the spliceosome components, and PsFYVE1 disrupted association between NbRZ-1A and spliceosome component NbGRP7. We examined the global transcription and alternative splicing (AS) changes regulated by PsFYVE1 and NbRZ-1A, which indicated that PsFYVE1 and NbRZ-1A co-regulated transcription and pre-mRNA AS of immunity-related genes. Thus, this study identifies a novel virulence-related effector from P. sojae and a class of positive regulators of plant immunity, and reveals a detailed mechanism of effector-medicated transcription and AS regulation during pathogen-plant interactions.

pathology↗

bioRxiv2021

Epigenetic and Transcriptional Dysregulation in CD4+ T cells of Patients with Atopic Dermatitis

Atopic dermatitis (AD) is one of the most common skin disorders in children. Disease etiology involves genetic and environmental factors, with the 29 independent AD risk loci enriched for risk allele-dependent gene expression in the skin and CD4+ T cell compartments. We investigated epigenetic mechanisms that may account for genetic susceptibility in CD4+ T cells. To understand gene regulatory activity differences in peripheral blood T cells in AD, we measured chromatin accessibility (ATAC-seq), NFKB1 binding (ChIP-seq), and gene expression (RNA-seq) in stimulated CD4+ T cells from subjects with active moderate-to-severe AD and age-matched, non-allergic controls. Open chromatin regions in stimulated CD4+ T cells were highly enriched for AD genetic risk variants, with almost half of AD risk loci overlapping with AD-dependent ATAC-seq peaks. AD-specific open chromatin regions were strongly enriched for NF{kappa}B DNA binding motifs. ChIP-seq identified hundreds of NFKB1-occupied genomic loci that were AD-specific or Control-specific. As expected, the AD-specific ChIP-seq peaks were strongly enriched for NF{kappa}B DNA binding motifs. Surprisingly, Control-specific NKFB1 ChIP-seq peaks were not enriched for NFKB1 motifs, instead containing motifs for other classes of human TFs, suggesting a mechanism involving altered indirect NFKB1 binding. Using DNA sequencing data, we identified 63 instances of genotype-dependent chromatin accessibility at 36 AD risk variants (30% of AD risk loci) that could lead to genotype-dependent expression at these loci. We propose that CD4+ T cells respond to stimulation in an AD-specific manner, resulting in disease and genotype-dependent chromatin accessibility involving NFKB binding. AUTHOR SUMMARYStimulated CD4+ T cells from patients with atopic dermatitis have disease-dependent regulation of how gene expression is regulated. This regulation is disease dependent and the way the DNA is accessible and the transcription factor NFKB1 binds is enriched for genetic risk variants. Clinically, the CD4+ T cells in the peripheral blood of patients with AD respond to stimulation in a disease and genotype-dependent manner.

genomics↗

bioRxiv2021

An instance-specific causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Cells within a tumor microenvironment (TME) dynamically communicate and influence each others cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor-specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

cancer biology↗

bioRxiv2021

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity, but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOA) and gene targets, we find that the two assays provide a partially shared, but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

bioinformatics↗

bioRxiv2021

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset.*

bioinformatics↗

bioRxiv2021

The edible seaweed Laminaria japonica contains cholesterol analogues that inhibit Lipid Peroxidation and Cyclooxygenase Enzymes

In this study, 5 sterols were isolated and purified from Laminaria japonica, commonly known as edible brown seaweed, and their structures were identified based on detailed chemical methods and spectroscopic analyses. Spectroscopic analyses characterized 5 sterols as 29-Hydroperoxy-stigmasta-5,24(28)-dien-3{beta}-ol, saringosterol (24-vinyl-cholest-5-ene-3{beta},24-diol), 24-methylenecholesterol, fucosterol (stigmasta-5,24-diene-3{beta}-ol), and 24-Hydroperoxy-24-vinyl-cholesterol. The bioactivities of these sterols were tested using lipid peroxidation (LPO) and cyclooxygenase (COX-1 and -2) enzyme inhibitory assays. Fucosterol exhibited the highest COX-1 and -2 enzyme inhibitory activities at 59 and 47%, respectively. Saringosterol, 24-methylenecholesterol and fucosterol showed higher LPO inhibitory activity at >50% than the other compounds. In addition, the results of molecular docking revealed that the 5 sterols were located in different pocket of COX-1 and -2 and fucosterol with tetracyclic skeletons and olefin methine achieved the highest binding energy (-7.85 and -9.02 kcal/mol) through hydrophobic interactions and hydrogen bond. Our results confirm the presence of 5 sterols in L. japonica and its significant anti-inflammatory and antioxidant activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/463984v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@16c3f43org.highwire.dtl.DTLVardef@1ad7385org.highwire.dtl.DTLVardef@7b3c4borg.highwire.dtl.DTLVardef@b3675a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISterols 29-hydroperoxy-stigmasta-5,24(28)-dien-3{beta}-ol and 24-hydroperoxy-24-vinyl-cholesterol are identified for the first time in L. japonica. C_LIO_LISaringosterol, 24-methylenecholesterol and fucosterol showed strong LPO inhibitory activity. C_LIO_LIFucosterol showed highest binding affinity for COX-1 and -2 enzymes through hydrophobic interactions. C_LI

biochemistry↗

bioRxiv2021

HOXB13 suppresses de novo lipogenesis through HDAC3-mediated epigenetic reprogramming

HOXB13, a homeodomain transcription factor, critically regulates androgen receptor (AR) function and promotes androgen-dependent prostate cancer (PCa) growth. However, the functions of HOXB13 in an AR-independent context remain elusive. Here we report an essential role of HOXB13 in directly suppressing lipogenic transcriptional programs in both AR-positive and -negative PCa cells. The MEIS domain (aa70-150) of HOXB13 interacts with the histone deacetylase HDAC3, which is disrupted by HOXB13 G84E mutation that has been associated with early-onset PCa. Thus, HOXB13 wildtype (WT), but not G84E mutant, recruits HDAC3 to lipogenic enhancers to catalyze histone de-acetylation and suppress lipogenic programs. HOXB13 knockdown unleashes the expression of key lipogenic regulators such as fatty acid synthase (FASN), requiring HDAC3. Analysis of human tissues revealed that HOXB13 is lost in about 30% of metastatic castration-resistant PCa, at least in part, through DNA hypermethylation. Functionally, loss of HOXB13 leads to massive lipid accumulation in PCa cells, thereby promoting cell motility in vitro and fueling xenograft tumor metastasis in vivo, which is mitigated by pharmaceutical inhibitors of FASN. In summary, our study discovers an essential AR-independent function of HOXB13 in repressing de novo lipogenesis and inhibiting tumor metastasis and defines a subclass of PCa that may benefit from lipogenic pathway inhibitors.

cancer biology↗

bioRxiv2021

PEAK2VEC ENABLES INFERRENCE OF TRANSCRIPTIONAL REGULATION FROM ATAC-SEQ

Transcription factor (TF) binding sites in ATAC-seq are typically determined by footprint analysis. However, the performance of footprint analysis remains unsatisfying and most TFs do not exhibit footprint patterns. In this study, we modified the convolutional neural network to project sequences into an embedding space. Sequences with similar nucleotide patterns will stay close together in the embedding. The dimensionality of this embedding space represents binding specificities of various TFs. In the simulation experiment, peak2vec accurately distinguished the three TFs in the embedding space while conventional deep learning cannot. When applied to the ATAC-seq profiles of hepatitis carcinoma, peak2vec recovered multiple motifs curated in database, while significant portion of sequences corresponding to the TF are located at the promoter region of its regulated genes.

bioinformatics↗

bioRxiv2021

ZBP1 induces inflammatory signaling via RIPK3 and promotes SARS-CoV-2-induced cytokine expression

COVID-19 caused by the SARS-CoV-2 virus remains a threat to global health. The disease severity is mediated by cell death and inflammation, which regulate both the antiviral and the pathological innate immune responses. ZBP1, an interferon-induced cytosolic nucleic acid sensor, facilitates antiviral responses via RIPK3. Although ZBP1-mediated cell death is widely described, whether and how it promotes inflammatory signaling is unclear. Here, we report a ZBP1-induced inflammatory signaling pathway that depends on ubiquitination and RIPK3s scaffolding ability independently of cell death. In human cells, ZBP1 associates with RIPK1 and RIPK3 as well as ubiquitin ligases cIAP1 and LUBAC. RIPK1 and ZBP1 are ubiquitinated to promote TAK1- and IKK-mediated inflammatory signaling. Additionally, RIPK1 recruits the p43/41-caspase-8-p43-FLIP heterodimer to suppress RIPK3 kinase activity, which otherwise promotes inflammatory signaling in a kinase activity-dependent manner. Lastly, we show that ZBP1 contributes to SARS-CoV-2-induced cytokine production. Taken together, we describe a ZBP1-RIPK1-RIPK3-mediated inflammatory signaling pathway relayed by the scaffolding role of RIPKs and regulated by caspase-8. Our results suggest the ZBP1 pathway contributes to inflammation in response to SARS-CoV-2 infection.

immunology↗

bioRxiv2021

Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

Predictive determinants of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. What drives proliferation decisions of single cells at any given time? We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window ([~]8.5-40 hours). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Machine learning algorithms show that simple metrics of central tendency in this time window are most predictive for subsequent cell division; median ERK and Akt activities classify individual division events with an AUC=0.76. Surprisingly, ERK dynamics alone predict division in individual cells with an AUC=0.74, suggesting Akt activity dynamics contribute little to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics are substantially more important than Akt activity dynamics for driving cell division in this non-transformed context. Single cell imaging along with machine learning algorithms provide a better basis to understand cell cycle progression on the single cell level.

systems biology↗

bioRxiv2021

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

bioinformatics↗

bioRxiv2021

DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: an in silico strategy towards precision oncology

BackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs. MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk. ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an immune-cold phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS. ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.

bioinformatics↗

bioRxiv2021

Dynamic assembly of the calcium hemostasis modulator 1 channel gates ATP permeation

Calcium hemostasis modulator 1 (CALHM1) is a voltage- and Ca2+-gated ATP channel that plays an important role in neuronal signaling. The currently reported CALHM structures are all in an ATP-conducting state, and the gating mechanism of ATP permeation remains elusive. Here, we report three cryo-EM reconstructions of Danio rerio heptameric CALHM1s with ordered or flexible long C-terminal helices as well as Danio rerio octameric CALHM1 with flexible long C-terminal helices at resolutions of 3.2 [A], 2.9 [A], and 3.5 [A]. Structural analysis revealed that the heptameric CALHM1s are in an ATP nonconducting state in which the pore diameter in the middle is approximately 6.6 [A]. Compared with those inside the octameric CALHM1s, the N- helices inside heptameric CALHM1s are in the "down" position to avoid steric clash with neighboring TM1 helices. Molecular dynamic simulation shows that the pore size is significantly increased for ATP molecule permeation during the movement of the N- helix from the "down" position to the "up" position. Therefore, we proposed a mechanism in which the "piston-like" motion of the N-helix drives the dynamic assembly of the CALHM1 channel for ATP molecule permeation. Our results provide insights into the ATP permeation mechanism of the CALHM1 channel.

biochemistry↗

bioRxiv2021

Cryo-EM structure of the human TACAN channel in a closed state

TACAN is an ion channel involved in sensing mechanical pain. It has recently been shown to represent a novel and evolutionarily conserved class of mechanosensitive channels. Here, we present the cryoelectron microscopic structure of human TACAN (hTACAN). hTACAN forms a dimer in which each protomer consists of a transmembrane globular domain (TMD) that is formed of six helices and an intracellular domain (ICD) that is formed of two helices. Molecular dynamic simulations suggest that a putative ion conduction pathway is located inside each protomer. Single point mutation of the key residue Met207 significantly increased the surface tension activated currents. Moreover, cholesterols were identified at the flank of each subunit. Our data show the molecular assembly of hTACAN and suggest that the wild type hTACAN is in a closed state, providing a basis for further understanding the activation mechanism of the hTACAN channel.

biochemistry↗

bioRxiv2021

Quantifying microbial associations of dissolved organic matter under global change

Microbes play a critical role in regulating the size, composition, and turnover of dissolved organic matter (DOM), which is one of the largest pools of carbon in aquatic ecosystems. Global change may alter DOM-microbe associations with implications for biogeochemical cycles, although disentangling these complex interactions remains a major challenge. Here we develop a framework called Energy-Diversity-Trait integrative Analysis (EDTiA) to examine the associations between DOM and bacteria along temperature and nutrient gradients in a manipulative field experiment on mountainsides in contrasting subarctic and subtropical climates. In both study regions, the chemical composition of DOM correlated with bacterial communities, and was primarily controlled by nutrients and to a lesser degree by temperature. At a molecular-level, DOM-bacteria associations depended strongly on the molecular traits of DOM, with negative associations indicative of decomposition as molecules are more biolabile. Using bipartite networks, we further demonstrated that negative associations were more specialized than positive associations indicative of DOM production. Nutrient enrichment promoted specialization of positive associations, but decreased specialization of negative associations particularly at warmer temperatures in subtropical climate. These global change drivers influenced specialization of negative associations most strongly via molecular traits, while both molecular traits and bacterial diversity similarly affected positive associations. Together, our framework provides a quantitative approach to understand DOM-microbe associations and wider carbon cycling across scales under global change.

ecology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗