Biology subjects

Desai, N.

Publications and source records attributed to Desai, N..

At least 19 recordsLinked to original sources

bioRxiv2026

More fruits, more frugivores, but not necessarily more visits: findings from two tropical plant-avian frugivore communities

Fruit resource availability can influence the local abundance of avian frugivores through bottom-up regulation, but whether greater resource availability translates into higher visitation rates to fruiting trees remains poorly understood. Greater fruit availability could increase visitation by supporting larger frugivore populations, or reduce visitation to individual trees if increases in frugivore abundance are not proportionate to those in fruit availability or if frugivores distribute their foraging effort. Understanding these relationships can provide insights into the relative role of abundance and trait-based mechanisms in structuring plant-frugivore communities. We compared fruit availability, avian frugivore abundance, and frugivore visitation rates on fruiting trees across two protected wet tropical forest sites: Anamalai Tiger Reserve in the Western Ghats and Namdapha Tiger Reserve in the Eastern Himalaya. Specifically, we quantified the availability of bird-consumed fruits across one-ha patches, estimated local abundances of major frugivore groups, and tested whether visitation rates on individual fruiting trees were related to frugivore abundance, body mass, and degree of frugivory. We found that small-seeded fruit availability was significantly higher in Anamalai than in Namdapha. Local abundances of several relatively small-bodied frugivore groups, including bulbuls, barbets, fairy-bluebirds, and mynas, were also higher in Anamalai. However, visitation rates of several frugivore groups on individual fruiting trees were higher in Namdapha. Visitation rates across all avian frugivore species were associated with frugivore abundances only in Anamalai, and not associated with body mass or degree of frugivory in either site. Thus, although greater fruit availability was likely associated with higher frugivore abundance, this increase in abundance did not consistently translate into higher visitation rates at individual fruiting trees, which provides some support for the resource dilution hypothesis. Our results highlight the importance of considering fruit resource availability and frugivore abundance when examining variation in tropical plant-frugivore interactions.

ecology↗

bioRxiv2026

Spatial/molecular heterogeneity and treatment response in HER2+ early breast cancer: Translational analyses from the DAPHNe trial

Given the heterogeneity of HER2-positive breast cancer, reliable biomarkers to guide treatment are needed. We conducted multi-platform biomarker analyses of baseline tumor tissue from the DAPHNe trial of neoadjuvant paclitaxel/trastuzumab/pertuzumab (THP) for HER2-positive early breast cancer to characterize inter- and intra-tumor heterogeneity and to identify molecular predictors of response. A total of 98 patients with stage II-III HER2-positive breast cancer received neoadjuvant THP followed by surgery. Gene expression profiling, spatial protein profiling, and single-cell imaging (cyclic immunofluorescence) were performed on pre-treatment biopsies and a subset of residual disease specimens. Pathologic response was assessed using the residual cancer burden (RCB) score. Among HER2-positive patients included in the biomarker analysis, 34% of patients had node-positive breast cancer, and 66% had hormone receptor (HR)-positive tumors. HR-positive and HR-negative tumors differed significantly across gene expression, protein expression, and single-cell profiling. High ERBB2 gene signature, p53 gene signature, and HER2 protein expression predicted favorable response (RCB 0/1), while ESR1/PGR gene, estrogen receptor (ER) signaling gene signature, and ER alpha protein expression were associated with unfavorable response (RCB 2/3). Single-cell spatial analysis revealed that cancer cells clustered by shared HER2, ER, and PR expression, suggesting local expansion of phenotypically distinct subpopulations. ER-positive cancer cells were associated with lower HLA-A and PD-L1 expression, suggesting a less immunologically active tumor cell state. Baseline HER2 and ER expression are key predictors of response to neoadjuvant HER2-targeted therapy. Single-cell spatial profiling highlights intra-tumoral heterogeneity and suggests ER-driven immune exclusion as a potential mechanism of resistance.

cancer biology↗

bioRxiv2026

Novel Target Combinations in Lung Squamous Cell Carcinoma proposed by the Emet AI Research Environment and supported by discovery stage experimentation

Early drug discovery is frequently bottlenecked by target identification, a challenge that becomes particularly difficult in complex diseases driven by overlapping, redundant pathways rather than a single dominant driver. Lung squamous cell carcinoma (LUSC) is one such disease: mutationally complex, lacking a dominant actionable target, and marked by a long series of failed single-agent targeted trials. We hypothesized a research environment, capable of reasoning across interconnected datasets, would be well-suited to generate novel, mechanistically grounded therapeutic hypotheses for LUSC. Emet, an AI research environment utilizing a biomedical knowledge graph of more than 1.5 billion triples connected to over 100 specialised biological databases, was paired with an Agentic Research Director that pursues each hypothesis through a graph-of-thoughts search, invoking scoped link-prediction and retrieval tools and committing every round of findings to persistent memory. The top-ranked hypotheses were two-target combinations rather than a single target. The four highest-ranked combinations were advanced to dose-matrix testing in NCI-H520 and SK-MES-1 cells, and two produced combination effects exceeding either single agent; to our knowledge neither of these two pairings had previously been evaluated in LUSC. Dual inhibition of CDC7 (TAK-931) and PKMYT1 (RP-6306) produced statistically significant synergy in both lines that strengthened from day 5 to day 7 (HSA 19.24 to 24.19 in NCI-H520; 11.07 to 13.73 in SK-MES-1). Dual inhibition of USP13 (spautin-1) and PI3K (alpelisib) produced an additive, cell-line-dependent effect, and immunoblotting confirmed the predicted mechanism: time-dependent depletion of MCL-1, c-Myc and SOX-2 driven by the USP13 arm. Agentic reasoning over multi-domain biomedical evidence can therefore nominate testable, mechanism-bearing combination hypotheses that survive experimental scrutiny.

bioinformatics↗

bioRxiv2026

Blautia wexlerae Transforms Dietary Fatty Acids to Activate Enteroendocrine Signaling and Improve Metabolic Health in Mice and Humans

Metabolites produced by the gut microbiome influence host metabolic health, but how this occurs remains incompletely defined. Here, we report that a common human gut commensal, Blautia wexlerae, converts dietary fats into bioactive metabolites that induce gut hormone production to affect glucose metabolism and suppress appetite. We found that colonization with Blautia wexlerae correlated with healthier eating behaviors in humans. Blautia wexlerae encodes a unique acyl transferase and is capable of producing acyl amines from nutrient substrates. These Blautia acyl amines stimulated human enteroendocrine cells to secrete GLP-1 and other gut peptide hormones more potently than endogenously produced acyl amines. When fed to mice, acyl amines improved glycemic control and decreased appetite. In humans, higher stool levels of Blautia DNA encoding acyl amine synthesis genes correlated with leanness and decreased dietary fat intake. These results define a mechanism of action for how Blautia wexlerae affects host metabolic control.

microbiology↗

bioRxiv2026

Frugivore species richness influences dietary specialisation and network properties in Asian wet tropical forests

AimTo examine how variation in frugivore species richness influences dietary specialisation and the organisation of plant-frugivore interaction networks in tropical forests. LocationSix undisturbed lowland wet tropical forest sites across four biodiversity hotspots in south and south-east Asia. Time period2016-2024. Major taxa studiedAvian frugivores and fleshy-fruited woody plants. MethodsWe recorded plant-avian frugivore interactions across six undisturbed evergreen forest sites spanning a seven-fold gradient in frugivore species richness, while holding forest type and phylogenetic composition broadly comparable. Using over 4,200 hours of focal observations on 551 fruiting plants, we recorded more than 34,000 feeding visits by 138 frugivore species on 133 plant species. We used a) Joint species distribution models to determine the relative influence of fruit and seed traits, and b) network analyses to evaluate how dietary breadth and network properties varied with frugivore species richness. ResultsAcross sites, frugivore visitation was primarily explained by fruit and seed morphology, with seed size accounting for an average of 39.7% of explained variation, followed by fruit width (24.4%), fruit crop size (21.9%), and pulp lipid content (14.1%). Frugivores in species-rich communities exhibited narrower dietary breadth (Pearsons r = -0.87 between normalised degree and species richness). Correspondingly, plant-frugivore networks became less connected and nested, and more modular, with increasing frugivore richness (Pearsons r = -0.9, -0.98, and 0.84, respectively). Main conclusionsIncreasing frugivore species richness intensifies dietary specialisation, which in turn drives changes in plant-frugivore network structure. These findings highlight how local species richness shapes interaction networks through changes in consumer niche breadth, with implications for the organisation of tropical forest mutualistic communities.

ecology↗

bioRxiv2025

Effects of frugivore species pool and seed size on the diversity and functional composition of frugivores visiting fruiting trees

The relationship between biodiversity and ecosystem functioning in seed dispersal remains understudied despite its critical role in maintaining plant diversity in the tropics. Field studies on this relationship are often confounded by environmental and phylogenetic variations across species richness gradients. We examined how overall avian frugivore species richness at a site influenced the frugivore richness, visitation rates and functional composition of two key effect traitsbeak width and hand-wing indexon fruiting trees. Across six sites in tropical Asia, spanning a sevenfold gradient in frugivore species richness but with similar forest types and phylogenetically nested frugivore communities, we recorded 34,014 interactions between 134 avian frugivores and 131 plant species. Our results provide some support for the biodiversity-ecosystem functioning relationship, as higher overall frugivore species richness increased the number of frugivore species visiting individual fruiting trees but not the functional composition of frugivores. Seed size had a stronger influence on the frugivore species richness, visitation rates, and the beak size of visiting frugivores, highlighting the dominant role of morphological trait matching in influencing plant-frugivore interactions. Our findings suggest functional redundancy in certain aspects of seed dispersal effectiveness due to density compensation and the presence of key seed disperser lineages in species-poor sites.

ecology↗

bioRxiv2024

Modelling mucus clearance in sinuses: thin-film flow inside a fluid-producing cavity lined with an active surface

The paranasal sinuses are a group of hollow spaces within the human skull, surrounding the nose. They are lined with an epithelium that contains mucusproducing cells and tiny hairlike active appendages called cilia. The cilia beat constantly to sweep mucus out of the sinus into the nasal cavity, thus maintaining a clean mucus layer within the sinuses. This process, called mucociliary clearance, is essential for a healthy nasal environment and disruption in mucus clearance leads to diseases such as chronic rhinosinusitis, specifically in the maxillary sinuses, which are the largest of the paranasal sinuses. We present here a continuum mathematical model of mucociliary clearance inside the human maxillary sinus. Using a combination of analysis and computations, we study the flow of a thin fluid film inside a fluid-producing cavity lined with an active surface: fluid is continuously produced by a wall-normal flux in the cavity and then is swept out, against gravity, due to an effective tangential flow induced by the cilia. We show that a steady layer of mucus develops over the cavity surface only when the rate of ciliary clearance exceeds a threshold, which itself depends on the rate of mucus production. We then use a scaling analysis, which highlights the competition between gravitational retention and cilia-driven drainage of mucus, to rationalise our computational results. We discuss the biological relevance of our findings, noting that measurements of mucus production and clearance rates in healthy sinuses fall within our predicted regime of steady-state mucus layer development.

biophysics↗

bioRxiv2024

Pharmacological rescue of motor circuit dysfunction in a Drosophila model of paroxysmal dyskinesia

BackgroundParoxysmal dyskinesias (PxDs) are characterised by bouts of involuntary dystonic and choreiform movements. The patho-mechanisms underlying these debilitating disorders remain poorly understood, and drug treatments are often limited. We recently generated a Drosophila model of a PxD-linked mutation causing BK potassium channel gain- of-function (BK GOF), and showed that BK GOF perturbs movement in Drosophila by disrupting neurodevelopment. However, whether locomotor capacity in BK GOF flies can be pharmacologically restored following neurodevelopmental insults has remained unclear. ObjectiveTo identify pharmacological suppressors of motor defects caused by BK GOF. MethodsUsing adult BK GOF flies, we performed an unbiased, in vivo, locomotion-based screen of 370 FDA-approved drugs. To test the impact of positive hits from this screen on motor circuit activity, we used optical imaging to record the intrinsic rhythmic activity of Drosophila larval motor circuits driving peristalsis and turning behaviors. ResultsWe found that inhibitors of acetylcholinesterase - a protein that degrades acetylcholine in cholinergic synapses - partially rescued movement defects caused by BK GOF. Inhibition of acetylcholinesterase also partially restored intrinsic activity of motor circuits controlling forward movement and turning in BK GOF larvae. ConclusionsOur findings indicate that elevating cholinergic tone can reverse motor circuit dysfunction in an animal model of PxD caused by BK GOF. Furthermore, our study provides proof-of-principle that Drosophila can be utilised for screens to uncover putative drug treatments for involuntary movement disorders.

neuroscience↗

bioRxiv2023

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking. We find that HADDOCK can generate accurate models of antibodyantigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. The data set used to benchmark the docking protocols in this study is available at github.com/haddocking/ai-antibodies. The docking models will be deposited at data.sbgrid.org/labs/32/ upon acceptance.

bioinformatics↗

bioRxiv2023

Connectivity Regression

SO_SCPLOWUMMARYC_SCPLOWAssessing how brain functional connectivity networks vary across individuals promises to uncover important scientific questions such as patterns of healthy brain aging through the lifespan or dysconnectivity associated with disease. In this article we introduce a general regression framework, Connectivity Regression (ConnReg), for regressing subject-specific functional connectivity networks on covariates while accounting for within-network inter-edge dependence. ConnReg utilizes a multivariate generalization of Fishers transformation to project network objects into an alternative space where Gaussian assumptions are justified and positive semidefinite constraints are automatically satisfied. Penalized multivariate regression is fit in the transformed space to simultaneously induce sparsity in regression coefficients and in covariance elements, which capture within network inter-edge dependence. We use permutation tests to perform multiplicity-adjusted inference to identify covariates associated with connectivity, and stability selection scores to identify network edges that vary with selected covariates. Simulation studies validate the inferential properties of our proposed method and demonstrate how estimating and accounting for within-network inter-edge dependence leads to more efficient estimation, more powerful inference, and more accurate selection of covariate-dependent network edges. We apply ConnReg to the Human Connectome Project Young Adult study, revealing insights into how connectivity varies with language processing covariates and structural brain features.

neuroscience↗

bioRxiv2023

Mapping Cell-to-cell Interactions from Spatially Resolved Transcriptomics Data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacias power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between {gamma}{delta} T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

bioinformatics↗

bioRxiv2022

The role of the cerebellum in learning to predict reward: evidence from cerebellar ataxia

Recent findings in animals have challenged the traditional view of the cerebellum solely as the site of motor control, suggesting that the cerebellum may also be important for learning to predict reward from trial-and-error feedback. Yet, evidence for the role of the cerebellum in reward learning in humans is lacking. Moreover, open questions remain about which specific aspects of reward learning the cerebellum may contribute to. Here we address this gap through an investigation of multiple forms of reward learning in individuals with cerebellum dysfunction, represented by cerebellar ataxia cases. Nineteen participants with cerebellar ataxia and 57 age- and sex-matched healthy controls completed two separate tasks that required learning about reward contingencies from trial-and-error. To probe the selectivity of reward learning processes, the tasks differed in their underlying structure: while one task measured incremental reward learning ability alone, the other allowed participants to use an alternative learning strategy based on episodic memory alongside incremental reward learning. We found that individuals with cerebellar ataxia were profoundly impaired at reward learning from trial-and-error feedback on both tasks, but retained the ability to learn to predict reward based on episodic memory. These findings provide evidence from humans for a specific and necessary role for the cerebellum in incremental learning of reward associations based on reinforcement. More broadly, the findings suggest that alongside its role in motor learning, the cerebellum likely operates in concert with the basal ganglia to support reinforcement learning from reward.

neuroscience↗

bioRxiv2022

Starvation Decreases Immunity and Immune Regulatory Factor NF-κB in the Starlet Sea Anemone Nematostella vectensis

Lack of proper nutrition (malnutrition) or the complete absence of all food (starvation) have important consequences on the physiology of all organisms. In many cases, nutritional status affects immunity, but, for the most part, the relationship between nutrition and immunity has been limited to studies in vertebrates and terrestrial invertebrates. Herein, we describe a positive correlation between nutrition and immunity in the sea anemone Nematostella vectensis. Gene expression profiling of adult fed and starved anemones showed downregulation of many genes involved in nutrient metabolism and cellular respiration, as well as immune-related genes, in starved animals. Starved adult anemones also had reduced protein levels and DNA-binding activity of immunity-related transcription factor NF-{kappa}B. Starved juvenile anemones had increased sensitivity to bacterial infection and also had lower NF-{kappa}B protein levels, as compared to fed controls. Weighted Gene Correlation Network Analysis (WGCNA) revealed significantly correlated gene networks that were inversely associated with starvation. Based on the WGCNA and a reporter gene assay, we identified TRAF3 as a likely NF-{kappa}B target gene in N. vectensis. Overall, these experiments demonstrate a correlation between nutrition and immunity in a basal marine metazoan, and the results have implications for the survival of marine organisms as they encounter changing environments. Significance StatementAdequate nutrition is required to sustain proper biological function. One factor threatening many marine organisms, as a result of modern day anthropogenic environmental changes, is nutrient availability. Here, we characterize transcriptional changes following food deprivation in the cnidarian model sea anemone Nematostella vectensis. We show that starvation is correlated with decreased expression of genes associated with nutrient metabolism and immunity, among others. Moreover, starvation reduces the level of expression and activity of immune regulatory transcription factor NF-{kappa}B and causes anemones to have increased susceptibility to bacterial infection. These results demonstrate that this basal organism responds at the transcriptional level to the absence of food, and that, in addition to changes in metabolic factors, starvation leads to a reduction in immunity.

immunology↗

bioRxiv2021

Basal forebrain cholinergic neurons are part of the threat memory engram

Although the engagement of cholinergic signaling in threat memory is well established (Knox, 2016a), our finding that specific cholinergic neurons are requisite partners in a threat memory engram is likely to surprise many. Neurons of the basal forebrain nucleus basalis and substantia innominata (NBM/SIp) comprise the major source of cholinergic input to the basolateral amygdala (BLA), whose activation are required for both the acquisition and retrieval of cued threat memory and innate threat response behavior. The retrieval of threat memory by the presentation of the conditioning tone alone elicits acetylcholine (ACh) release in the BLA and the BLA-projecting cholinergic neurons manifest immediate early gene responses and display increased intrinsic excitability for 2-5 hours following the cue-elicited memory response to the conditioned stimulus. Silencing cue-associated engram-enrolled cholinergic neurons prevents the expression of the defensive response and the subset of cholinergic neurons activated by cue is distinct from those engaged by innate threat. Taken together we find that distinct populations of cholinergic neurons are recruited to signal distinct aversive stimuli via the BLA, demonstrating exquisite, functionally refined organization of specific types of memory within the cholinergic basal forebrain.

neuroscience↗

bioRxiv2021

Visualising formation of the ribosomal active site in mitochondria

Ribosome assembly is an essential and complex process that is regulated at each step by specific biogenesis factors. Using cryo-electron microscopy, we identify and order major steps in the formation of the highly conserved peptidyl transferase centre (PTC) and tRNA binding sites in the large subunit of the human mitochondrial ribosome (mitoribosome). The conserved GTPase GTPBP7 regulates the folding and incorporation of core 16S ribosomal RNA (rRNA) helices and the ribosomal protein bL36m, and ensures that the PTC base U3039 has been 2'-O-methylated. Additionally, GTPBP7 binds the RNA methyltransferase NSUN4 and MTERF4, which facilitate earlier steps by sequestering H68-71 of the 16S rRNA and allowing biogenesis factors to access the maturing PTC. Consistent with the central role of NSUN4*MTERF4 and GTPBP7 during mitoribosome biogenesis, in vivo mutagenesis designed to disrupt binding of their Caenorhabditis elegans orthologs to the large subunit potently activates mitochondrial stress responses and results in severely reduced viability, developmental delays and sterility. Next-generation RNA sequencing reveals widespread gene expression changes in these mutant animals that are indicative of mitochondrial stress response activation. We also answer the long-standing question of why NSUN4 but not its enzymatic activity, is indispensable for mitochondrial protein synthesis in metazoans.

biochemistry↗

bioRxiv2020

An automated, high throughput methodology optimized for quantitative cell-free mitochondrial and nuclear DNA isolation from plasma

Circulating, cell-free mitochondrial DNA (ccf-mtDNA) and nuclear DNA (ccf-nDNA) are under investigation as biomarkers for various diseases. Optimal ccf-mtDNA isolation parameters, like those outlined for ccf-nDNA, have not been established. Here, we optimized a protocol for both ccf-mtDNA and ccf-nDNA recovery using a magnetic bead-based isolation process on an automated 96-well platform. Using the optimized protocol, our data show 6-fold improved yields of ccf-mtDNA when compared to the starting protocol. Digestion conditions, liquid handling characteristics, and magnetic particle processor programming all contributed to increased recovery and improved reproducibility. To our knowledge, this is the first high-throughput approach optimized for mtDNA and nDNA recovery and serves as an important starting point for clinical studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/206987v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@13ab547org.highwire.dtl.DTLVardef@1535020org.highwire.dtl.DTLVardef@b191aaorg.highwire.dtl.DTLVardef@1d23fa4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2019

Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

IntroductionAccurate prediction of risk of death following acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making. Contemporary machine-learning may improve risk-prediction by identifying complex relationships between predictors and outcomes. Methods and ResultsWe studied 993,905 patients in the American College of Cardiology Chest Pain-MI Registry hospitalized with AMI (mean age 64 {+/-} 13 years, 34% women) between January 2011 and December 2016. We developed and validated three machine learning models to predict in-hospital mortality and compared the performance characteristics with a logistic regression model. In an independent validation cohort, we compared logistic regression with lasso regularization (c-statistic, 0.891 [95% CI, 0.890-0.892]), gradient descent boosting (c-statistic, 0.902 [0.901-0.903]), and meta-classification that combined gradient descent boosting with a neural network (c-statistic, 0.904 [0.903-0.905]) with traditional logistic regression (c-statistic, 0.882 [0.881-0.883]). There were improvements in classification of individuals across the spectrum of patient risk with each of the three methods; the meta-classifier model - our best performing model - reclassified 20.9% of individuals deemed high-risk for mortality in logistic regression appropriately as low-to-moderate risk, and 8.2% of deemed low-risk to moderate-to-high risk based consistent with the actual event rates. ConclusionsMachine-learning methods improved the prediction of in-hospital mortality for AMI compared with logistic regression. Machine learning methods enhance the utility of risk models developed using traditional statistical approaches through additional exploration of the relationship between variables and outcomes.

bioinformatics↗

bioRxiv2018

The landscape of viral associations in human cancers

Potential viral pathogens were systematically investigated in the whole-genome and transcriptome sequencing of 2,656 donors as part of the Pan-Cancer Analysis of Whole Genomes using a consensus approach integrating three independent pathogen detection pipelines. Viruses were detected in 382 genomic and 68 transcriptome data sets. We extensively searched and characterized numerous features of virus-positive cancers integrating various PCAWG datasets. We show the high prevalence of known tumor associated viruses such as EBV, HBV and several HPV types. Our systematic analysis revealed that HPV presence was significantly exclusive with well-known driver mutations in head/neck cancer. A strong association was observed between HPV infection and the APOBEC mutational signatures, suggesting the role of impaired mechanism of antiviral cellular defense as a driving force in the development of cervical, bladder and head neck carcinoma. Viral integration into the host genome was observed for HBV, HPV16, HPV18 and AAV2 and associated with a local increase in copy number variations. The recurrent viral integrations at the TERT promoter were coupled to high telomerase expression uncovering a further mechanism to activate this tumor driving process. High levels of endogenous retrovirus ERV1 expression is linked to worse survival outcome in kidney cancer.

cancer biology↗