Biology subjects

Xiao, X.

Publications and source records attributed to Xiao, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

NRF2 Regulates Contractile Vascular Smooth Muscle Cells Phenotypic Switching In Abdominal Aortic Aneurysm

OBJECTIVEEvidence from previous studies has demonstrated that NRF2 can protect cardiovascular progression,which prompted us to study if NRF2 is involved in AAA development. APPROACH AND RESULTSThe difference and function of NRF2 in AAA were verified on human aortic tissues combined with scRNA-seq samples.We then used angiotensin II (Ang II) infusion mouse model with VSMCs-specific knockout to study the role of NRF2 in AAA formation.Primary cultured VSMCs were used to study how NRF2 regulates contractile VSMCs phenotype.Patient specimens were obtained to investigate the relevance of NRF2 expression to human AAA disease. NRF2 was induced in abdominal aortic VSMCs in both mouse and human AAA tissues. VSMCs-specific NRF2 knockout increased experimental AAA formation. Mechanistically, NRF2 regulates the contractile VSMCs phenotypic through miRNA145, leading to increased classic contractile protein level to prevent the development of AAA. CONCLUSIONSNRF2 is a negative regulator of AAA development, and thus may represent a potentially new therapeutic target to inhibit AAA growth and rupture.

bioinformatics↗

bioRxiv2023

Deficiency of Orexin Receptor Type 1 in Dopaminergic Neurons Increases Novelty-Induced Locomotion and Exploration

Orexin signaling in the ventral tegmental area and substantia nigra promotes locomotion and reward processing, but it is not clear whether dopaminergic neurons directly mediate these effects. We show that dopaminergic neurons in these areas mainly express orexin receptor subtype 1 (Ox1R). In contrast, only a minor population in the medial ventral tegmental area express orexin receptor subtype 2 (Ox2R). To analyze the functional role of Ox1R signaling in dopaminergic neurons, we deleted Ox1R specifically in dopamine transporter-expressing neurons of mice and investigated the functional consequences. Deletion of Ox1R increased locomotor activity and exploration during exposure to novel environments or when intracerebroventricularely injected with orexin A. Spontaneous activity in home cages, anxiety, reward processing, and energy metabolism did not change. Positron emission tomography imaging revealed that Ox1R signaling in dopaminergic neurons affected distinct neural circuits depending on the stimulation mode. In line with an increase of neural activity in the lateral paragigantocellular nucleus (LPGi) of Ox1R{Delta}DAT mice, we found that dopaminergic projections innervate the LPGi in regions where the inhibitory dopamine receptor subtype D2 but not the excitatory D1 subtype resides. These data suggest a crucial regulatory role of Ox1R signaling in dopaminergic neurons in novelty-induced locomotion and exploration.

neuroscience↗

bioRxiv2023

Correlative single-cell X-ray tomography and X-ray fluorescence imaging

X-ray tomography (XRT) and X-ray fluorescence (XRF) imaging are two non-invasive imaging techniques to study cellular structures and chemical element distributions, respectively. However, correlative X-ray tomography and fluorescence imaging for the same cell have yet to be routinely realized due to challenges in sample preparation and X-ray radiation damage. Here we report an integrated experimental and computational workflow for achieving correlative multi-modality X-ray imaging of a single cell. The method consists of the preparation of radiation-resistant single-cell samples using live-cell imaging-assisted chemical fixation and freeze-drying procedures, targeting and labeling cells for correlative XRT and XRF measurement, and computational reconstruction of the correlative and multi-modality images. With XRT, cellular structures including the overall structure and intracellular organelles are visualized, while XRF imaging reveals the distribution of multiple chemical elements within the same cell. Our correlative method demonstrates the feasibility and broad applicability of using X-rays to understand cellular structures and the roles of chemical elements and related proteins in signaling and other biological processes.

cell biology↗

bioRxiv2023

Aster-dependent non-vesicular transport facilitates dietary cholesterol uptake

Intestinal cholesterol absorption is an important contributor to systemic cholesterol homeostasis. Niemann-Pick C1 Like 1 (NPC1L1), the target of the drug ezetimibe (EZ), assists in the initial step of dietary cholesterol uptake. However, how cholesterol moves downstream of NPC1L1 is unknown. Here we show that Aster-B and Aster-C are critical for non-vesicular cholesterol movement in enterocytes, bridging NPC1L1 at the plasma membrane (PM) and ACAT2 in the endoplasmic reticulum (ER). Loss of NPC1L1 diminishes accessible PM cholesterol in enterocytes and abolishes Aster recruitment to the intestinal brush border. Enterocytes lacking Asters accumulate cholesterol at the PM and display evidence of ER cholesterol depletion, including decreased cholesterol ester stores and activation of the SREBP-2 transcriptional pathway. Aster-deficient mice have impaired cholesterol absorption and are protected against diet-induced hypercholesterolemia. Finally, we show that the Aster pathway can be targeted with a small molecule inhibitor to manipulate dietary cholesterol uptake. These findings identify the Aster pathway as a physiologically important and pharmacologically tractable node in dietary lipid absorption. One-Sentence SummaryIdentification of a targetable pathway for regulation of dietary cholesterol absorption

physiology↗

bioRxiv2023

Personalized connectivity-based network targeting model of TMS for treatment of psychiatric disorders: computational feasibility and reproducibility

Consider the limited clinical efficacy of transcranial magnetic stimulation (TMS) due to heterogeneity in treatment outcomes, the utilization of individual functional connectivity (FC) can enhance the prediction accuracy in the network targeting model. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC (rsFC). To overcome this challenge, proposed solutions include increasing the scan duration and employing clustering approaches to enhance the stability of FC. In this study, we aimed to evaluate the stability of a personalized functional-based network targeting model in individuals with major depressive disorder (MDD) and schizophrenia with auditory verbal hallucinations (AVH). Using resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP), we assessed the models stability and employed longer scan durations (7 minutes, 14 minutes, 21 minutes, 28 minutes) and clustering methodologies to improve the precision of identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the clustering approach lead to stable identification of individual sites, as evidenced by the intraindividual distance falling below the [~]1cm spatial resolution of TMS. These findings contribute to the understanding of individualized TMS targeting and have implications for improving treatment outcomes in psychiatric disorders.

neuroscience↗

bioRxiv2023

dsRID: Editing-free in silico identification of dsRNA region using long-read RNA-seq data

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases. Here, we report dsRID (double-stranded RNA identifier), a machine learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimers disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

bioinformatics↗

bioRxiv2023

Transformer with Convolution and Graph-Node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has always been a fascinating but challenging task due to the huge differences between the two modal data. Previous works have used modified DenseNet121 to encode the local images and make gene expression predictions. And later works improved the prediction accuracy of gene expression by incorporating the coordinate information from images and using all spots in the tissue region as input. While these methods were limited in use due to model complexity, large demand on GPU memory, and insufficient encoding of local images, thus the results had low interpretability, relatively low accuracy, and over-smooth prediction of gene expression among neighbor spots. In this paper, we propose TCGN, (Transformer with Convolution and Graph-Node co-embedding method) for gene expression prediction from H&E stained pathological slide images. TCGN consists of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone for histopathological image analysis. We trained TCGN and compared its performance with three existing methods on a publicly available spatial transcriptomic dataset. Even in the absence of the coordinates information and neighbor spots, TCGN still outperformed the existing methods by 5% and achieved 10 times higher prediction accuracy than the counterpart model. Besides its higher accuracy, our model is also small enough to be run on a personal computer and does not need complex building graph preprocessing compared to the existing methods. Moreover, TCGN is interpretable in recognizing special cell morphology and cell-cell interactions compared to models using all spots as input that are not interpretable. A more accurate omics information prediction from pathological images not only links genotypes to phenotypes so that we can predict more biomarkers that are expensive to test from histopathological images that are low-cost to obtain, but also provides a theoretical basis for future modeling of multi-modal data. Our results support that TCGN is a useful tool for inferring gene expressions from histopathological images and other potential histopathological image analysis studies. HighlightsO_LIFirst deep learning model to integrate CNN, GNN, and transformer for image analysis C_LIO_LIAn interpretable model that uses cell morphology and organizations to predict genes C_LIO_LIHigher gene expression prediction accuracy without global information C_LIO_LIAccurately predicted genes are related to immune escape and abnormal metabolism C_LIO_LIPredict important biomarkers for breast cancer accurately from cheaper images C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/542669v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1c6a4dforg.highwire.dtl.DTLVardef@7266d4org.highwire.dtl.DTLVardef@bcfd0aorg.highwire.dtl.DTLVardef@188d452_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

A Comprehensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT

In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models(LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP related tasks and benchmarks and got excellent results. To evaluate the performance of ChatGPT on biomedical related tasks, this paper presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions and so on. Through a series of experiments, we demonstrated the effectiveness and versatility of Chat-GPT in biomedical text understanding, reasoning and generation.

bioinformatics↗

bioRxiv2023

Sterile Soil as Prebiotics Mitigate Intergenerational Loss of Gut Microbial Diversity and Anxiety-Like Behavior Induced by Antibiotics in mice

The decline in gut microbial diversity of modern human is closely associated with the rising prevalence of various diseases. It is imperative to investigate the underlying causes of gut microbial loss and the rescue measures. Although the impact of non-perinatal antibiotic use on gut microbiota has been recognized, its intergenerational effects remain unexplored. Our previous research has highlighted soil in the farm environment as a key prebiotic for gut microbiome health by restoring gut microbial diversity and balance. In this study, we investigated the intergenerational consequences of antibiotic exposure and the therapeutic potential of soil prebiotics. We treated mice with vancomycin and streptomycin for 2 weeks continuously, followed by 4-8 weeks of withdrawal period before breeding. The process was repeated across 3 generations. Half of the mice in each generation received an oral soil prebiotic intervention. We assessed gut microbial diversity, anxiety behavior, microglia reactivity, and gut barrier integrity across generations. The antibiotics exposure led to a decrease in gut microbial diversity over generations, along with aggravated anxiety behavior, microglia abnormalityies, and altered intestinal tight junction protein expression. Notably, the third generation of male mice exhibited impaired reproductive capacity. Oral sterile soil intervention restored gut microbial diversity in adult mice across generations, concomitantly rescuing abnormalities in behavior, microglia activity, and intestinal barrier integrity. In conclusion, this study simulated an important process of the progressive loss gut microbiota diversity in modern human and demonstrated the potential of sterile soil as a prebiotic to reverse this process. The study provides a theoretical and experimental basis for the research and therapeutic interventions targeting multiple modern chronic diseases related to intestinal microorganisms.

microbiology↗

bioRxiv2023

Cancer associated fibroblasts serve as an ovarian cancer stem cell niche through noncanonical Wnt5a signaling

Frequent relapse and chemoresistance cause poor outcome in ovarian cancer (OC) and cancer stem cells (CSCs) are important contributors. While most studies focus exclusively on CSCs, the role of the microenvironment in providing optimal conditions to maintain their tumor-initiating potential remains poorly understood. Cancer associated fibroblasts (CAFs) are a major constituent of the OC tumor microenvironment and we show that CAFs and CSCs are enriched following chemotherapy in patient tumors. CAFs significantly increased OC cell resistance to carboplatin. Using heterotypic CAF-OC cocultures and in vivo limiting dilution assay, we confirmed that the CAFs act by enriching the CSC population. CAFs were found to increase the symmetric division of CSCs as well as the dedifferentiation of bulk OC cells into CSCs. The effect of CAFs was limited to OC cells in their immediate neighborhood, which could be prevented by inhibiting Wnt. Analysis of single cell RNA-seq data from OC patients revealed that Wnt5a as the highest expressed Wnt in CAFs and that certain subpopulations of CAFs express higher levels of Wnt5a. We found that Wnt5a from CAFs activated a noncanonical Wnt signaling pathway involving the ROR2/PKC/CERB1 axis in the neighboring CSCs. While canonical Wnt signaling was predominant in interactions between cancer cells in patients, non-canonical Wnt pathway was activated by CAF-OC crosstalk. Treatment with a Wnt5a inhibitor sensitized tumors to carboplatin in vivo. Together, our findings demonstrate a novel mechanism of CSC maintenance by signals from the microenvironmental CAFs, which can be targeted to treat OC chemoresistance and relapse. Statement of significanceCAFs serve as CSC niche through a Wnt5a mediated noncanonical Wnt signaling. Disease relapse and development of chemoresistance is a major problem in OC, which can be potentially addressed by targeting Wnt5a.

cancer biology↗

bioRxiv2023

LINE-associated cryptic splicing induces dsRNA-mediated interferon response and tumor immunity

RNA splicing plays a critical role in post-transcriptional gene regulation. Exponential expansion of intron length poses a challenge for accurate splicing. Little is known about how cells prevent inadvertent and often deleterious expression of intronic elements due to cryptic splicing. In this study, we identify hnRNPM as an essential RNA binding protein that suppresses cryptic splicing through binding to deep introns, preserving transcriptome integrity. Long interspersed nuclear elements (LINEs) harbor large amounts of pseudo splice sites in introns. hnRNPM preferentially binds at intronic LINEs and represses LINE-containing pseudo splice site usage for cryptic splicing. Remarkably, a subgroup of the cryptic exons can form long dsRNAs through base-pairing of inverted Alu transposable elements scattered in between LINEs and trigger interferon immune response, a well-known antiviral defense mechanism. Notably, these interferon-associated pathways are found to be upregulated in hnRNPM-deficient tumors, which also exhibit elevated immune cell infiltration. These findings unveil hnRNPM as a guardian of transcriptome integrity. Targeting hnRNPM in tumors may be used to trigger an inflammatory immune response thereby boosting cancer surveillance.

genomics↗

bioRxiv2023

Instructive and reorganizing effects of experience on development of human visual cortices

Comparisons of visual cortex function across blind and sighted adults reveals effects of experience on human brain function. Since almost all research has been done with adults, little is known about the developmental origins of plasticity. We compared resting state functional connectivity of visual cortices of blind adults (n = 30), blindfolded sighted adults (n = 50) to a large cohort of infants (Developing Human Connectome Project, n = 475). Visual cortices of sighted adults show stronger coupling with non-visual sensory-motor networks (auditory, somatosensory/motor), than with higher-cognitive prefrontal cortices (PFC). In contrast, visual cortices of blind adults show stronger coupling with higher-cognitive PFC than with nonvisual sensory-motor networks. Are infant visual cortices functionally like those of sighted adults, with blindness leading to functional change? We find that, on the contrary that secondary visual cortices of infants are functionally more like those of blind adults: stronger coupling with PFC than with nonvisual sensory-motor networks, suggesting that visual experience modifies elements of the sighted-adult long-range functional connectivity profile. Infant primary visual cortices are in-between blind and sighted adults i.e., more balanced PFC and sensory-motor connectivity than either adult group. The lateralization of occipital-to-frontal connectivity in infants resembles the sighted adults, consistent with the idea that blindness leads to functional change. These results suggest that both vision and blindness modify functional connectivity through experience-driven (i.e., activity-dependent) plasticity.

neuroscience↗

bioRxiv2023

Comprehensive Allergen Profile of Blomia tropicalis Provides Insights into the Component-Resolved Diagnosis of Mite Allergy

BackgroundBlomia (B.) tropicalis, as an important species of house dust mites (HDMs), plays a critical role in allergic diseases in tropical populations, but its allergen components are less investigated than those of other HDMs. Multiple omics methods have largely improved the identification of mite allergens. Here, we sought to identify a comprehensive allergen profile of B. tropicalis and advance the allergen component-resolved diagnosis (CRD) of mite allergy. MethodsReference mite allergen sequences were searched in a high-quality genome of B. tropicalis. Comparative analysis was performed for important allergen groups. ELISA was used to assess the allergenicities of recombinant proteins of specific allergens. ResultsA complete allergen profile of B. tropicalis was revealed, including thirty-seven allergen groups (up to Blo t 42). In-depth comparative analysis not only determined the homology of major allergen groups 5 and 21 but also shed light on the emergence and divergence of chitin-binding allergens. The specific Blo t 12 was identified to be a chitin-binding protein originating from the chitinase of allergen group 15. Immunoassays of recombinant proteins verified three novel allergens and the ELISA results suggested geographical differences in the B. tropicalis sensitization rate. ConclusionsThe comprehensive allergen profile revealed in B. tropicalis, the comparative analysis of allergen groups and the immunoassay assessment of recombinant proteins largely expanded our knowledge to B. tropicalis allergens and could ultimately benefit the CRD of HDM allergy.

immunology↗

bioRxiv2023

Design of 8-mer Peptides that Block Clostridioides difficile Toxin A in Intestinal Cells

Clostridioides difficile (C. diff.) is a bacterium that causes severe diarrhea and inflammation of the colon. The pathogenicity of C. diff. infection is derived from two major toxins, toxins A (TcdA) and B (TcdB). Peptide inhibitors that can be delivered to the gut to inactivate these toxins are an attractive therapeutic strategy. In this work, we present a new approach that combines a peptide binding design algorithm (PepBD), molecular-level simulations, rapid screening of candidate peptides for toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block the glucosyltransferase activity of TcdA by targeting its glucosyltransferase domain (GTD). Using PepBD and explicit-solvent molecular dynamics simulations, we identified seven candidate peptides, SA1-SA7. These peptides were selected for specific TcdA GTD binding through a custom solid-phase peptide screening system, which eliminated the weaker inhibitors SA5-SA7. The efficacies of SA1-SA4 were then tested using a trans-epithelial electrical resistance (TEER) assay on monolayers of the human gut epithelial culture model. One peptide, SA1, was found to block TcdA toxicity in primary-derived human jejunum (small intestinal) and colon (large intestinal) epithelial cells. SA1 bound TcdA with a KD of 56.1 {+/-} 29.8 nM as measured by surface plasmon resonance (SPR). Significance StatementInfections by Clostridioides difficile, a bacterium that targets the large intestine (colon), impact a significant number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can inhibit the biocatalytic activity of these toxins represent a promising strategy to prevent and treat C. diff. infection. We describe an approach that combines a Peptide Binding Design (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in small intestinal and colon epithelial cells. Importantly, our designed peptide, SA1, bound toxin A with nanomolar affinity and blocked toxicity in colon cells.

bioengineering↗

bioRxiv2022

Metal-driven anaerobic oxidation of methane as an important methane sink in methanic cold seep sediments

Anaerobic oxidation of methane (AOM) coupled with reduction of metal oxides is supposed to be a globally important bioprocess in marine sediments. However, the responsible microorganisms and their contributions to methane budget are not clear in deep sea cold seep sediments. Here, we combined geochemistry, muti-omics and numerical modeling to study metal-dependent AOM in methanic cold seep sediments in the northern continental slope of the South China Sea. Geochemical data based on methane concentrations, carbon stable isotope, solid-phase sediment analysis and pore water measurements indicate the occurrence of anaerobic methane oxidation coupled to metal oxides reduction in the methanic zone. The 16S rRNA gene amplicons and transcripts, along with metagenomic and metatranscriptomic data suggest that diverse ANME groups actively mediated methane oxidation in the methanic zone either independently or in syntrophy with e.g. ETH-SRB1 as potential metal reducers. Modeling results suggest that the estimated rates of methane consumption via Fe-AOM and Mn-AOM were both 0.3 mol cm-2 yr-1, which account for [~]3% of total CH4 removal in sediments. Overall, our results highlight metal-driven anaerobic oxidation of methane as an important methane sink in methanic cold seep sediments.

microbiology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

Novel and unexpected genetic and microbial diversity for arsenic cycling in deep sea cold seep sediments

Cold seeps, where cold hydrocarbon-rich fluid escapes from the seafloor, showed strong enrichment of toxic metalloid arsenic (As). The toxicity and mobility of As can be greatly altered by microbial processes that play an important role in global As biogeochemical cycling. However, a global overview of genes and microbes involved in As transformation at seeps remains to be fully unveiled. Using 87 sediment metagenomes and 33 metatranscriptomes derived from 13 globally distributed cold seeps, we show that As detoxification genes (arsM, arsP, arsC1/arsC2, acr3) were prevalent at seeps and more phylogenetically diverse than previously expected. Asgardarchaeota and a variety of unidentified bacterial phyla (e.g. 4484-113, AABM5-125-24 and RBG-13-66-14) may also function as the key players in As transformation. The abundances of As-cycling genes and the compositions of As-associated microbiome shifted across different sediment depths or types of cold seep. The energy-conserving arsenate reduction or arsenite oxidation could impact biogeochemical cycling of carbon and nitrogen, via supporting carbon fixation, hydrocarbon degradation and nitrogen fixation. Overall, this study provides a comprehensive overview of As-cycling genes and microbes at As-enriched cold seeps, laying a solid foundation for further studies of As cycling in deep sea microbiome at the enzymatic and processual levels.

microbiology↗

bioRxiv2022

Genetic pathways regulating the longitudinal acquisition of cocaine self administration in mice

To identify genetic pathways for addiction, we analyzed intravenous self-administration of cocaine or saline in a panel of 84 inbred and recombinant inbred mouse strains over 10 days. We integrated the behavior data with RNA-Seq data from the medial frontal cortex and nucleus accumbens from 41 strains. The self-administration of cocaine and saline showed distinct genetic bases. We maximized power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci were identified in the genome-wide association study (GWAS). A transcriptome-wide association study (TWAS) highlighted the Trpv2 ion channel as a key locus for cocaine self-administration from the GWAS. In addition, 17 genes supplementary to the GWAS were identified including Arhgef26, Slc18b1 and Slco5a1. We found numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, a known cannabinoid receptor, for the response to cocaine as well as identifying further relevant loci.

genetics↗