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Biology subjects

Gao, B.

Publications and source records attributed to Gao, B..

At least 73 records · Page 4Linked to original sources

Phosphatidylserine Receptors Enhance SARS-CoV-2 Infection: AXL as a Therapeutic Target for COVID-19

AUTHOR SUMMARYPhosphatidylserine (PS) receptors are PS binding proteins that mediate uptake of apoptotic bodies. Many enveloped viruses utilize this PS/PS receptor mechanism to adhere to and internalize into the endosomal compartment of cells and this is termed apoptotic mimicry. For viruses that have a mechanism(s) of endosomal escape, apoptotic mimicry is a productive route of virus entry. We evaluated if PS receptors serve as cell surface receptors for SARS-CoV-2 and found that the PS receptors, AXL, TIM-1 and TIM-4, facilitated virus infection when low concentrations of the SARS-CoV-2 cognate receptor, ACE2, was present. Consistent with the established mechanism of PS receptor utilization by other viruses, PS liposomes competed with SARS-CoV-2 for binding and entry. We demonstrated that this PS receptor enhances SARS-CoV-2 binding to and infection of an array of human lung cell lines and is an under-appreciated but potentially important host factor facilitating SARS-CoV-2 entry.

microbiology↗

Pre-existing partner-drug resistance facilitates the emergence and spread of artemisinin resistance: a consensus modelling study

BackgroundArtemisinin-resistant genotypes have now emerged a minimum of five times on three continents despite recommendations that all artemisinins be deployed as artemisinin combination therapies (ACTs). Widespread resistance to the non-artemisinin partner drugs in ACTs has the potential to limit the clinical and resistance benefits provided by combination therapy. MethodsUsing a consensus modelling approach with three individual-based mathematical models of Plasmodium falciparum transmission, we evaluate the effects of pre-existing partner-drug resistance and ACT deployment on artemisinin resistance evolution. We evaluate settings where dihydroartemisinin-piperaquine (DHA-PPQ), artesunate-amodiaquine (ASAQ), or artemether-lumefantrine (AL) are deployed as first-line therapy. We use time until 0.25 artemisinin resistance allele frequency (the establishment time) as the primary outcome measure. FindingsHigher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance. Across all scenarios and pre-existing frequencies of partner-drug resistance explored, a 0.10 increase in partner-drug resistance frequency on average corresponded to 0.7 to 5.0 years loss of artemisinin efficacy. However, the majority of reductions in time to artemisinin establishment were observed after the first increment from 0.0 to 0.10 partner-drug resistance genotype frequency. InterpretationPartner-drug resistance in ACTs facilitates the early emergence of artemisinin resistance and is a major public health concern. Higher grade partner-drug resistance has the largest effect, with piperaquine-resistance accelerating early emergence of artemisinin-resistant alleles the most. Continued investment in molecular surveillance of partner-drug resistant genotypes to guide choice of first-line ACT is paramount. FundingBill and Melinda Gates Foundation; Wellcome Trust.

genetics↗

Chitinase 3-like-1 Contributes to Acetaminophen-induced Liver Injury by Promoting Hepatic Platelet Recruitment

Hepatic platelet accumulation contributes to acetaminophen (APAP)-induced liver injury (AILI). However, little is known about the molecular pathways involved in platelet recruitment to the liver and whether targeting such pathways could attenuate AILI. The present study unveiled a critical role of chitinase 3-like-1 (Chi3l1) in hepatic platelet recruitment during AILI. Increased Chi3l1 and platelets in the liver were observed in patients and mice overdosed with APAP. Compared to wild-type (WT) mice, Chi3l1-/- mice developed attenuated AILI with markedly reduced hepatic platelet accumulation. Mechanistic studies revealed that Chi3l1 signaled through CD44 on macrophages to induce podoplanin expression, which mediated platelet recruitment through C-type lectin-like receptor 2. Moreover, APAP treatment of CD44-/- mice resulted in much lower numbers of hepatic platelets and liver injury than WT mice, a phenotype similar to that in Chi3l1-/- mice. Recombinant Chi3l1 could restore hepatic platelet accumulation and AILI in Chi3l1-/- mice, but not in CD44-/- mice. Importantly, we generated anti-Chi3l1 monoclonal antibodies and demonstrated that they could effectively inhibit hepatic platelet accumulation and AILI. Overall, we uncovered the Chi3l1/CD44 axis as a critical pathway mediating APAP-induced hepatic platelet recruitment and tissue injury. We demonstrated the feasibility and potential of targeting Chi3l1 to treat AILI.

pharmacology and toxicology↗

Phase separation of RNA-binding protein promotes polymerase engagement and transcription

An RNA-involved phase-separation model has been proposed for transcription control. Yet, the molecular links that connect RNA binding to the transcription machinery remain missing. Here we find RNA-binding proteins (RBPs) constitute half of the chromatin proteome in embryonic stem cells (ESCs), and some are colocalized with RNA polymerase (Pol) II at promoters and enhancers. Biochemical analyses of representative RBPs--such as PSPC1 and PTBP1--show that the paraspeckle protein PSPC1 not only prevents the RNA-induced premature release of Pol II, and also makes use of RNA as multivalent molecules to promote Pol II engagement and activity, by enhancing the phase separation and subsequent phosphorylation and release of polymerase condensates. In ESCs, auxin-induced acute degradation of PSPC1 leads to genome-wide defects in Pol II phosphorylation and chromatin-binding and nascent transcription. We propose that the synergistic interplay of RBPs and RNA aids in the rate-limiting step of polymerase condensate formation to promote active transcription.

biochemistry↗

Targeting de novo lipogenesis and the Lands cycle induces ferroptosis in KRAS-mutant lung cancer

Mutant KRAS (KM) is the most common oncogene in lung cancer (LC). KM regulates several metabolic networks, but their role in tumorigenesis is still not sufficiently characterized to be exploited in cancer therapy. To identify metabolic networks specifically deregulated in KMLC, we characterized the lipidome of genetically engineered LC mice, cell lines, patient derived xenografts and primary human samples. We also determined that KMLC, but not EGFR-mutant (EGFR-MUT) LC, is enriched in triacylglycerides (TAG) and phosphatidylcholines (PC). We also found that KM upregulates fatty acid synthase (FASN), a rate-limiting enzyme in fatty acid (FA) synthesis promoting the synthesis of palmitate and PC. We determined that FASN is specifically required for the viability of KMLC, but not of LC harboring EGFR-MUT or wild type KRAS. Functional experiments revealed that FASN inhibition leads to ferroptosis, a reactive oxygen species (ROS)-and iron-dependent cell death. Consistently, lipidomic analysis demonstrated that FASN inhibition in KMLC leads to accumulation of PC with polyunsaturated FA (PUFA) chains, which are the substrate of ferroptosis. Integrating lipidomic, transcriptome and functional analyses, we demonstrated that FASN provides saturated (SFA) and monounsaturated FA (MUFA) that feed the Lands cycle, the main process remodeling oxidized phospholipids (PL), such as PC. Accordingly, either inhibition of FASN or suppression of the Lands cycle enzymes PLA2 and LPCAT3, promotes the intracellular accumulation of lipid peroxides and ferroptosis in KMLC both in vitro and in vivo. Our work supports a model whereby the high oxidative stress caused by KM dictates a dependency on newly synthesized FA to repair oxidated phospholipids, establishing a targetable vulnerability. These results connect KM oncogenic signaling, FASN induction and ferroptosis, indicating that FASN inhibitors already in clinical trial in KMLC patients (NCT03808558) may be rapidly deployed as therapy for KMLC.

cancer biology↗

The Progenetix oncogenomic resource in 2021

In cancer, copy number aberrations (CNA) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138663 (including 115357 tumor) CNV profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive NCIt cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org

cancer biology↗

NUCLEIC TRANSFORMER: DEEP LEARNING ONNUCLEIC ACIDS WITH SELF-ATTENTION ANDCONVOLUTIONS

AO_SCPLOWBSTRACTC_SCPLOWMuch work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge and the resulting models provide very limited interpretability. Here we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in a variety of DNA/RNA tasks. The Nucleic Transformer processes nucleic acid sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing. We demonstrate that the Nucleic Transformer can be trained in both supervised and unsupervised fashion without much domain knowledge to achieve high performance with limited amounts of data in Escherichia coli promoter classification, viral genome identification, and degradation properties of COVID-19 mRNA vaccine candidates. Additionally, we showcase extraction of promoter motifs from learned attention and how direct visualization of self-attention maps assists informed decision making using deep learning models.

bioinformatics↗

Quickomics: exploring omics data in an intuitive, interactive and informative manner

SummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics statistical analysis results and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customizable options and generates both interactive and publication-ready plots to uncover biological insights from data. The modular design makes the tool extensible with ease. AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics datasets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/Quickomics for local installation. Supplementary informationSupplementary materials are available at https://bit.ly/37HP17g.

bioinformatics↗

Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases. However, shotgun metagenomic sequencing is much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having < 80% sequence similarity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average >400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely in part to the extent and curation of the reference databases considered.

microbiology↗

Genomic Copy Number Signatures Based Classifiers for Subtype Identification in Cancer

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected "driver" genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

bioinformatics↗

cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

Tumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information. Availabilityhttps://github.com/bowen-gao/cancerAlign

bioinformatics↗

Programming animal physiology and behaviors through engineered bacteria

A central goal of synthetic biology is to predictably and efficiently reprogram living systems to perform computations and carry out specific biological tasks. Although there have been many advances in the bio-computational design of living systems, these advances have mainly been applied to microorganisms or cell lines; programming animal behavior and altering animal physiology remain challenges for synthetic biology because of the system complexity. Here, we present a bacteria-animal symbiont system in which engineered bacteria recognize external signals and modulate animal gene expression, twitching behavior, and fat metabolism through RNA interference. By using genetic circuits in bacteria to control RNA expression, we programmed the physiology and behavior of the model animal Caenorhabditis elegans with logic gates. We anticipate that engineered bacteria can be used more extensively to program animal metabolism and behaviors for agricultural, therapeutic, and basic science applications. One Sentence SummaryAnimal physiology and behaviors are programmed via engineered bacteria.

synthetic biology↗

Chemical composition, antimicrobial and antioxidant activities of essential oils from the receptacle of sunflower (Helianthus annuus L.)

Sunflower (Helianthus annuus L.) contains active ingredients, such as flavonoids, alkaloids and tannins. However, few studies have focused on essential oils from the receptacle of sunflower (SEOs). The chemical composition, antimicrobial and antioxidant activities of SEOs were assessed whether SEOs could be used as nature preservatives and medicines. The extraction rate of SEOs was 0.4% by using hydro-distillation and solvent with petroleum ether, which is much higher than that by only using hydro-distillation (0.2%) in the previous study. A total of 81 volatile constituents of SEOs were identified by gas chromatography-mass spectrometry (GC-MS). The main constituents of SEOs were -pinene (31.17%), calamenene (5.66%), -terpineol (4.71%), verbenone (3.15%), kaur-16-ene (2.52%) and terpinolene (1.47%). The antimicrobial activities of SEOs were assessed against three bacteria (E. coli, P. aeruginosa, and S. aureus) and two fungi (S. cerevisiae and Candida albicans). The MIC of SEOs against P. aeruginosa and S. aureus was 0.2 mg/mL. The MIC of SEOs against S. cerevisiae was 3.2 mg/mL. The MIC of SEOs against E. coli and Candida albicans was 6.4 mg/mL. The results showed that SEOs had high antibacterial and antifungal actions. The antioxidant activity was determined with three different analytical assays (DPPH, ABTS and iron reducing ability). The results of antioxidant activities showed that SEOs had high antioxidant activities. The results proved that SEOs could be used as natural preservatives and medicines, due to its excellent antimicrobial and antioxidant activities.

microbiology↗

Inhibition of Anti-viral Stress Granule Formation by infectious bronchitis virus endoribonuclease nsp15 Ensures Efficient Virus Replication

Cytoplasmic stress granules (SGs) are generally triggered by stress-induced translation arrest for storing mRNAs. Recently, it has been shown that SGs exert anti-viral functions due to their involvement in protein synthesis shut off and recruitment of innate immune signaling intermediates. The largest RNA virus, coronavirus, mutates frequently and circulates among animals, imposing great threat to public safety and animal health; however, the significance of SGs in coronavirus infections is largely unknown. Infectious bronchitis virus (IBV) is the first identified coronavirus in 1930s and has been prevalent in poultry farm for many years. In this study, we provide evidence that IBV overcomes the host antiviral response by inhibiting SGs formation via the virus-encoded endoribonuclease nsp15. By immunofluorescence analysis, we observed that IBV infection not only did not trigger SGs formation in approximately 80% of the infected cells, but also impaired the formation of SGs triggered by heat shock, sodium arsenite, or NaCl stimuli. We show that the intrinsic endoribonuclease activity of nsp15 is responsible for the inhibition of SGs formation. In fact, nsp15-defective recombinant IBV (rIBV-nsp15-H238A) greatly induced the formation of SGs, along with accumulation of dsRNA and activation of PKR, whereas wild type IBV failed to do so. Consequently, infection with rIBV-nsp15-H238A triggered transcription of IFN-{beta} which in turn greatly affected recombinant virus replication. Further analysis showed that SGs function as antiviral hub, as demonstrated by the attenuated IRF3-IFN response and increased production of IBV in SG-defective cells. Additional evidence includes the aggregation of PRRs and signaling intermediates to the IBV-induced SGs. Collectively, our data demonstrate that the endoribonuclease nsp15 of IBV suppresses the formation of antiviral hub SGs by regulating the accumulation of viral dsRNA and by antagonizing the activation of PKR, eventually ensuring productive virus replication. We speculate that coronaviruses employ similar mechanisms to antagonize the host anti-viral SGs formation for efficient virus replication, as the endoribonuclease function of nsp15 is conserved in all coronaviruses. Author summaryIt has been reported that stress granules (SGs) are part of the host cell antiviral response. Not surprisingly, viruses in turn produce an array of antagonists to counteract such host response. Here, we show that IBV inhibits the formation of SGs through its endoribonuclease nsp15, by reducing the accumulation of viral dsRNA, evading the activation of PKR, and by subsequently inhibiting eIF2 phosphorylation and SGs formation. Nsp15 also inhibits SG formation independent of the eIF2 pathway, probably by targeting host mRNA. Depletion of SG scaffold proteins decreases IRF3-IFN response and increases the production of IBV. All coronaviruses encode a conserved endoribonuclease nsp15, and it will be important to determine whether also other (non-avian) coronaviruses limit the formation of anti-viral SGs in a similar manner.

microbiology↗

Nrf2 Activator PB125 as a Potential TherapeuticAgent Against COVID-19

Nrf2 is a transcription factor that regulates cellular redox balance and the expression of a wide array of genes involved in immunity and inflammation, including antiviral actions. Nrf2 activity declines with age, making the elderly more susceptible to oxidative stress-mediated diseases, which include type 2 diabetes, chronic inflammation, and viral infections. Published evidence suggests that Nrf2 activity may regulate important mechanisms affecting viral susceptibility and replication. We examined gene expression levels by GeneChip microarray and by RNA-seq assays. We found that the potent Nrf2 activating composition PB125(R) downregulates ACE2 and TMPRSS2 mRNA expression in human liver-derived HepG2 cells. ACE2 is a surface receptor and TMPRSS2 activates the spike protein for SARS-Cov-2 entry into host cells. Furthermore, in endotoxin-stimulated primary human pulmonary artery endothelial cells we report the marked downregulation by PB125 of 36 genes encoding cytokines. These include IL1-beta, IL6, TNF-, the cell adhesion molecules ICAM1, VCAM1, and E-selectin, and a group of IFN-{gamma}-induced genes. Many of these cytokines have been specifically identified in the "cytokine storm" observed in fatal cases of COVID-19, suggesting that Nrf2 activation may significantly decrease the intensity of the storm.

cell biology↗

Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells

The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.

genomics↗

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

MotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity. ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells. AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

bioinformatics↗

Not all MDAs should be created equal-determinants of MDA impact and designing MDAs towards malaria elimination

Malaria remains at the forefront of scientific research and global political and funding agendas. Previous malaria models of mass-interventions have consistently oversimplified how mass interventions are implemented. We present an individual based, spatially explicit model of malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. This translates into a higher likelihood of malaria elimination for areas with true prevalence under 3% with a faster implementation, in highly mobile populations. If populations are more static, deploying less interventions teams would be cost optimal and predicted to be as impactful. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in the right context, specifically when paired with effective vector control.

physiology↗