Search

This paper studies the relationship between undirected (unrooted) and directed (rooted) phylogenetic networks. We describe a polynomial-time algorithm for deciding whether an undirected nonbinary phylogenetic network, given the locations of the root and reticulation vertices, can be oriented as a directed nonbinary phylogenetic network. Moreover, we characterize when this is possible and show that, in such instances, the resulting directed nonbinary phylogenetic network is unique. In addition, without being given the location of the root and the reticulation vertices, we describe an algorithm for deciding whether an undirected binary phylogenetic network N can be oriented as a directed binary phylogenetic network of a certain class. The algorithm is fixed-parameter tractable (FPT) when the parameter is the level of N and is applicable to classes of directed phylogenetic networks that satisfy certain conditions. As an example, we show that the well-studied class of binary tree-child networks satisfies these conditions., Discrete Mathematics and Optimization

The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry’s single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf, a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools. Simpleaf is written in Rust and released under a BSD 3-Clause license. It is freely available from its GitHub repository https://github.com/COMBINE-lab/simpleaf, and via bioconda. Documentation for simpleaf is available at https://simpleaf.readthedocs.io/en/latest/ and tutorials for simpleaf that have been developed can be accessed at https://combine-lab.github.io/alevin-fry-tutorials.

mTORC1 senses nutrients and growth factors and phosphorylates downstream targets, including the transcription factor TFEB, to coordinate metabolic supply and demand. These functions position mTORC1 as a central controller of cellular homeostasis, but the behavior of this system in individual cells has not been well characterized. Here, we provide measurements necessary to refine quantitative models for mTORC1 as a metabolic controller. We developed a series of fluorescent protein-TFEB fusions and a multiplexed immunofluorescence approach to investigate how combinations of stimuli jointly regulate mTORC1 signaling at the single-cell level. Live imaging of individual MCF10A cells confirmed that mTORC1-TFEB signaling responds continuously to individual, sequential, or simultaneous treatment with amino acids and the growth factor insulin. Under physiologically relevant concentrations of amino acids, we observe correlated fluctuations in TFEB, AMPK, and AKT signaling that indicate continuous activity adjustments to nutrient availability. Using partial least squares regression modeling, we show that these continuous gradations are connected to protein synthesis rate via a distributed network of mTORC1 effectors, providing quantitative support for the qualitative model of mTORC1 as a homeostatic controller and clarifying its functional behavior within individual cells.

BackgroundProtein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.Areas coveredIn this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.Expert opinionComputational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

In order to understand morphogenesis, it is necessary to know the material properties or forces shaping the living tissue. In spite of this need, very few in vivo measurements are currently available. Here, using the early Drosophila embryo as a model, we describe a novel cantilever-based technique which allows for the simultaneous quantification of applied force and tissue displacement in a living embryo. By analyzing data from a series of experiments in which embryonic epithelium is subjected to developmentally relevant perturbations, we conclude that the response to applied force is adiabatic and is dominated by elastic forces and geometric constraints, or system size effects. Crucially, computational modeling of the experimental data indicated that the apical surface of the epithelium must be softer than the basal surface, a result which we confirmed experimentally. Further, we used the combination of experimental data and comprehensive computational model to estimate the elastic modulus of the apical surface and set a lower bound on the elastic modulus of the basal surface. More generally, our investigations revealed important general features that we believe should be more widely addressed when quantitatively modeling tissue mechanics in any system. Specifically, different compartments of the same cell can have very different mechanical properties; when they do, they can contribute differently to different mechanical stimuli and cannot be merely averaged together. Additionally, tissue geometry can play a substantial role in mechanical response, and cannot be neglected.

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Understanding the molecular mechanisms of osteoarthritis (OA) is critical for early diagnosis and effective treatment. OA is a leading cause of disability and poses an increasing burden on healthcare systems, particularly with an aging global population. Despite the potential of proteomics to elucidate the complex biology underlying OA, its application remains limited due to challenges including gaps in computational tools and insights into the early stages of the disease. This thesis addresses these limitations through approaches that combines inventive computational strategies with biological exploration. The thesis presents a comprehensive computational framework that aims to facilitate proteomics analyses. In paper I, we developed ProteoMill, a user-friendly, web-based platform designed to make proteomics data analysis and biological interpretation available to a broader scientific community. In paper II, we introduced, proteasy, a specialized computational tool aimed at identifying proteolytic events. Using this tool, we performed a peptidomic analysis to identify key proteolytic enzymes involved in the degradation of proteins that contribute to OA progression in human synovial fluid (SF). This study presented a broad array of differentially abundant endogenously cleaved peptides, and their potential cleaving actor. We demonstrated that the proteolytic activity of the predicted proteases extends beyond the extracellular matrix (ECM) of the surrounding tissues, and can also affect factors such as chylomicron assembly, potentially leading to hampered homeostasis. In paper III, we established a human meniscus ex vivo model, that enabled us to perform controlled studies on cytokine-mediated effects on meniscal tissues. Our analyses highlight an increase in catabolic processes in response to some of the cytokine treatments while IL1ß had a limited catabolic effect. Finally, in paper IV we utilized the SOMAscan assay, an aptamer-based proteomics platform that is capa

Dengue is an acute viral illness caused by the RNA virus of the family Flaviviridae and spread by Aedes mosquitoes. Intense dengue is a spreading cause of serious disease and death in some ’Asian and South American’ countries. Pathogenesis is associated with the amended functioning of our innate immune system during infection. Toll Like Receptor is influential for the involucre of innate immunity able to cause dengue infection disease like pattern receptor recognition. Toll-like receptors induced by injury of a certain severity arbitrate activation of interferons and Fc receptors arbitrate the involucre of cytokines. Clearance of dengue virus is associated with interferon protein; however regulatory mechanisms have been adopted against this modified effect. The clearance is considered to be a steady state known to be characterized by a low threshold level of DENV. The pathogenic state is characterized by a high threshold level of SOCS. SOCS protein is also induced due to interferon and cytokine-amended signaling, which can subsequently play its part in the regulation of interferon and cytokine production. Our hypothesis in this research the innate immunity system is associated between the pathogenesis of the dengue virus and the SOCS-amended inhibition. We used the static formalism model of the biological regulatory network of Toll-like receptors induced by the pathogenesis of dengue amended signaling pathway. A model verification method used in GINsim was used to deduce the logical parameters for the qualitative modelling. Therefore, a multidisciplinary and translational study we constructed a formal model constraint the approach with a static and integrative computational analysis, which may identify new immunopathological mechanisms and biomarkers for differential diagnosis, opening the way for the development of specific therapies that will reduce mortality and induce morbidity by dengue virus.

In machine learning, a sub-field of computer science, a two-tower architecture model is a specialised type of neural network model that encodes paired data from different modalities (like text and images, sound and video, or proteomics and gene expression profiles) into a shared latent representation space. However, when training these models using a specific contrastive loss function, known as the multimodalinfoNCE loss, seems to often lead to a unique geometric phenomenon known as the modality gap. This gap is a clear geometric separation of the embeddings of the modalities in the joint contrastive latent space. This thesis investigates the modality gap in multimodal machine learning, specifically in two-tower neural networks trained with multimodal-infoNCE loss. We examine the adequacy of the current definition of the modality gap, the conditions under which the modality gap phenomenon manifests, and its impact on representation quality and downstream task performance. The approach to address these questions consists of a two-phase experimental strategy. Phase I involves a series of experiments, ranging from toy synthetic simulations to true multimodal machine learning with complex datasets, to explore and characterise the modality gap under varying conditions. Phase II focuses on modifying the modality gap and analysing representation quality, evaluating different loss functions and their impact on the modality gap. This methodical exploration allows us to systematically dissect the emergence and implications of the modality gap phenomenon, providing insights into its impact on downstream tasks, measured with proxy metrics based on semantic clustering in the shared latent representation space and modality-specific linear probe evaluation. Our findings reveal that the modality gap definition proposed by W. Liang et al. 2022, is insufficient. We demonstrate that similar modality gap magnitudes can exhibit varying linear separability between modality embeddings in

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

Biological processes resulting in observable cell state changes, such as synaptic plasticity inneuronal dendritic spines and muscular hypertrophy in skeletal muscles, share common characteristics. Both are influenced by calcium signaling, metabolic networks, mitochondria, and protein kinase activation. This research presents computational models of these processes, offering insights into synaptic plasticity and the skeletal muscle response to exercise. The first model is a spatial representation of mitochondrial ATP generation during calcium signaling in neurons, emphasizing the role of mitochondrial ER contact sites in increasing calcium levels in the mitochondria, thus enhancing mitochondrial potential. This model underscores the importance of subcellular geometry, metabolic consumption rates, and transport processes in ATP production, in addition to enzymatic activity. The second model is a compartmental model exploring the role of signaling frequency in protein kinase activation in dendritic spines. This model analyzes the interaction between the canonical neuronal signaling cascade, the AMP-Activated Protein Kinase (AMPK) cascade, and the insulin signaling cascade in conditions of high neurotransmitter stimulation rates, which activate calcium signaling and consume significant cellular energy. The final model, a logic-based representation of skeletal muscle signaling in response to resistance, endurance, and sprint exercise, investigates potential mechanisms of exercise-induced changes in mitochondrial morphology. Together, these models deepen our understanding of the dynamic interplay between signaling and metabolism in synaptic plasticity and exercise physiology, laying the groundwork for further investigation and potential development of therapeutic strategies for neurological disorders and improvements in exercise efficiency.

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data are typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n = 12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n = 2) during an instrumental task from calcium fluorescence in orbitofrontal cortex. DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array hardware for real-time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.

BackgroundDetailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.MethodsWe built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.ResultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.ConclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

MotivationThe investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes that are active or modulated in a specific experimental context.ResultsThe Network Data Exchange Integrated Query (NDEx IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The NDEx IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. NDEx IQuery's integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources.Availability and implementationNDEx IQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java.

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Marinolides A and B, two new 24- and 26-membered bacterial macrolactones, were isolated from the marine-derived actinobacterium AJS-327 and their stereostructures initially assigned by bioinformatic data analysis. Macrolactones typically possess complex stereochemistry, the assignments of which have been one of the most difficult undertakings in natural products chemistry, and in most cases, the use of X-ray diffraction methods and total synthesis have been the major methods of assigning their absolute configurations. More recently, however, it has become apparent that the integration of bioinformatic data is growing in utility to assign absolute configurations. Genome mining and bioinformatic analysis identified the 97 kb mld biosynthetic cluster harboring seven type I polyketide synthases. A detailed bioinformatic investigation of the ketoreductase and enoylreductase domains within the multimodular polyketide synthases, coupled with NMR and X-ray diffraction data, allowed for the absolute configurations of marinolides A and B to be determined. While using bioinformatics to assign the relative and absolute configurations of natural products has high potential, this method must be coupled with full NMR-based analysis to both confirm bioinformatic assignments as well as any additional modifications that occur during biosynthesis.

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER's predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher's toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds.

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations.

This study analyzes a basic mathematical model for the dynamic interactions among tumor cells, infected tumor cells and viruses population, focusing on the viral lytic cycle for oncolytic virotherapy. I study the time delay effect of viral infection on tumor cell populations by identifying bifurcation thresholds in both the burst rate and time delay of viral infection in oncolytic virus therapy. Time delay plays an important role in changing the structure of tumor cell populations in a dynamical system. The multi-bifurcation thresholds of the time delay are observed and also dependent on the bursting rate. This study demonstrates a strong relationship between viral burst rates and time delays in population dynamics. The results of this study show that time delay affects oscillation generation and results in back-to-back Hopf bifurcation. This study provides insight into understanding the relationship between the two control parameters, in which tumor cell populations pattern from equilibrium steady-state solutions to periodic solutions and from periodic solutions to equilibrium-state solutions.

This study analyzes a basic mathematical model for the dynamic interactions among tumor cells, infected tumor cells and viruses population, focusing on the viral lytic cycle for oncolytic virotherapy. I study the time delay effect of viral infection on tumor cell populations by identifying bifurcation thresholds in both the burst rate and time delay of viral infection in oncolytic virus therapy. Time delay plays an important role in changing the structure of tumor cell populations in a dynamical system. The multi-bifurcation thresholds of the time delay are observed and also dependent on the bursting rate. This study demonstrates a strong relationship between viral burst rates and time delays in population dynamics. The results of this study show that time delay affects oscillation generation and results in back-to-back Hopf bifurcation. This study provides insight into understanding the relationship between the two control parameters, in which tumor cell populations pattern from equilibrium steady-state solutions to periodic solutions and from periodic solutions to equilibrium-state solutions.

BACKGROUND: All cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no computationally efficient bioinformatics tool that allows visualizing and exploring these large-scale mutational events. RESULTS: Here, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. CONCLUSIONS: The new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. RESULTS: We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. CONCLUSIONS: Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Glioblastoma is the fastest-growing, and the most common malignant brain tumour in adults. It is normally treated with surgery and radio- or chemotherapy, but the approximate life expectancy is of 15 months with a high probability of cancer recurring. Therefore, there is a need for decreasing its severity. Bulk and single-cell RNA sequencing allow the identification of cellular states in tumours affected by cell-intrinsic and extrinsic factors. Four different cellular states have been identified in glioblastoma: neural progenitor-like, oligodendrocyte progenitor-like, astrocyte-like, and mesenchymal-like. As glioblastoma is an immunosuppressive tumour, it can alter the immune system and increase the tumour's immune escaping by segregating immunosuppressive factors or interacting with the brain microenvironment.Two datasets were used in this study to explore if the localization of the tumour in the brain microenvironment and the tendency of glioblastomas to activate microglial cells are due to particular ligand-receptor interactions. Data quality control was applied to both datasets and SingleCellSignalR and CellphoneDB packages were used to predict the possible interactions. A total of seven experiments were designed for this study. The first dataset, GBmap, allowed us to do a comparison between tumour cells and microglia, tumour cells and other cell types in the brain, and the four cellular states of glioblastoma with microglia and macrophages. Next, healthy microglia from GBmap was used to compare with the tumour bulk data from the second dataset, HGCC. The bootstrap technique was performed to compare bulk data vs single-cell data, and a comparison between tumour cells and microglia or other cell types was analysed.Results showed specific and shared interactions between cell types or cellular states, revealing the different localization of the tumour cells depends on the expressed ligand-receptor pairs. Also, a total of four patterns of interactions were found in

Background: Abdominal aortic aneurysm (AAA) is a vascular disease with a mortality rate of >80% if ruptured. Mitochondrial dysfunction has been previously implicated in AAA pathogenesis. In this study, we aimed to characterize the mitochondrial genetic landscape in AAA. Methods and Results: Whole mitochondrial genome sequencing and bioinformatics analysis were performed in comorbidity matched 48 cases without AAA and 48 cases with AAA, objectively diagnosed, and selected from a cohort of 65‐year‐old men recruited for a screening program. We identified differential mutational landscapes in men with and without AAA, with errors in mitochondrial DNA replication or repair as potential sources. Heteroplasmic insertions and overall heteroplasmy of structural rearrangements were significantly elevated in AAA cases. Three heteroplasmic variants were associated with risk factors of AAA: leukocyte concentration, plasma glucose, and cholesterol levels, respectively. Interestingly, mutations were more prevalent in regulatory part of the mitochondria, the displacement loop region, in AAA as compared with controls (P value <0.05), especially in the conserved and critical mitochondrial extended termination‐associated sequence region. Moreover, we report a novel 24 bp mitochondrial DNA duplication present exclusively in cases with AAA (4%) and 75% of the unmatched AAA biopsies. Finally, the haplogroup cluster JTU was overrepresented in AAA and significantly associated with a positive family history of AAA (odds ratio, 2.9 [95% CI, 1.1–8.1]). Conclusions: This is the first study investigating the mitochondrial genome in AAA, where important genetic alterations and haplogroups associated with AAA and clinical risk factors were identified. Our findings have the potential to fill in gaps in the missing genetic information on AAA.

In eukaryotes, cellular differentiation is often orchestrated by programmed arrays of activation and repression of genes underlying the specific phenotypes of cell-types. To complete its life cycle, the single-celled Apicomplexan parasite Plasmodium falciparum, the most deadly of the human malaria parasites, must repeatedly differentiate and convert into unique cell types that can exploit niches within their human and mosquito hosts. One of these key developmental transitions occurs in the human host and includes sexual-stage commitment, followed by gametocyte development, which results in male and female gametocytes that can infect the female anopheline mosquito once taken up during the blood meal. The following gamete stage undergoes fusion and meiosis, followed by morphological changes as they invade the midgut wall to form the oocyst. The sexual stages are responsible for malaria transmission and the spread of antimalarial resistance, making it an important target for malaria eradication. Previous studies have established the understanding of a few mechanisms involved in sexual differentiation; the transcription factor, AP2-G, is a master regulator of sexual commitment. Uncovering genes and regulators underlying P. falciparum sexual stage development, including host-parasite interactions are the subjects of this thesis. In paper I, we describe the temporal landscape of differential transcriptional programs underlying the P. falciparum male and female gametocyte development and highlight a bifurcation point for the onset of differential male and female transcriptional programs. Furthermore, we predict novel candidate driver genes underlying sexual cell fate determination in P. falciparum. Finally, we traced the potential sex-specific regulatory mechanisms among the ApiAP2 family of transcription factors including prediction of target genes based on co-expression and the presence of upstream binding sites, rendering a motif-driven gene regulatory network. In

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions., We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

High-throughput sequencing methods are extremely powerful tools to quantify gene expression in bulk tissue and individual cells. Experimental designs often aim to quantify expression shifts to characterize developmental trajectories, disease states, or cellular drug responses. Experimental and genetic methods are also rapidly evolving to capture specific aspects of gene expression such as in targeting individual cell types, regulatory stages, and spatially resolved cell subcompartments. These studies frequently involve a variety of experimental conditions that require many samples to guarantee sufficient statistical power for subsequent analyses. These studies are frequently processed in multiple batches due to limitations on the number of samples that can be collected, processed, and sequenced at once. To eliminate erroneous results in subsequent analyses, it is necessary to deconvolve non-biological variation (batch effect) from biological signal. Here, we explored variational contributions in multi-batch high throughput sequencing experiments by developing new methods, evaluating heterogeneity-contributors in an axon-TRAP-RiboTag protocol case-study, and highlighting biological results from this protocol. First, we describe iDA, a novel dimensionality reduction method for high-throughput sequencing data. High-dimensional data in complex, multi-batch experiments often result in discrete clustering of samples or cells. Existing unsupervised linear dimensionality reduction methods like PCA often do not resolve discreteness simply with projections of maximum variance. We show that iDA can produce better projections for separating discrete clustering that correlates with known experimental biological and batch factors. Second, we provide a case study of special considerations for a complex, multi-batch high throughput experiment. We investigated the multi-faceted heterogenic contributions of a study using the axon-TRAP-RiboTag translatomic isolation protocol in a neur

Cell-cell communication plays a significant role in shaping the functionality of the cells. Communication between the cells is also responsible for maintaining the physiological state of the cells and the tissue. Therefore, it is important to study the different ways by which cell-cell communication impacts the functional state of cells. Alterations in cell-cell communication can contribute to the development of disease conditions. In this thesis, we present two computational tools and a study to explore the different aspects of cell-cell communication. In the first manuscript, FunRes was developed to leverage the cell-cell communication model to investigate functional heterogeneity in cell types and characterize cell states based on the integration of interand intra-cellular signalling. This tool utilizes a combination of receptors and transcription factors (TFs) based on the reconstructed cell-cell communication network to split the cell types into functional states. The tool was applied to the TabulaMurisSenis atlas to discover functional cell states in both young and old mouse datasets. In addition, we compared our tool with state-of-the-art tools and validated the cell states using available markers from the literature. Secondly, we studied the evolution of gene expression in developing astrocytes under normal and inflammatory conditions. We characterized these cells using both transcriptional and chromatin accessibility data which were integrated to reconstruct the gene regulatory networks (GRNs) specific to the condition and timepoints. The GRNs were then topologically analyzed to identify key regulators of the developmental process under both normal and inflammatory conditions. In the final manuscript, we developed a computational tool that identified regulators of allergy and tolerance in a mouse model. The tool works by first reconstructing the cell-cell communication network and then analyzing the network for feedback loops. These feedback loops are impor

Biological processes involve movements across all measurable scales. Similarity measures can be applied to compare and analyze these movements but differ in how differences in movement are aggregated across space and time. The present study reviews frequently-used similarity measures, such as the Hausdorff distance, Fréchet distance, Dynamic Time Warping, and Longest Common Subsequence, jointly with several measures less used in biological applications (Wasserstein distance, weak Fréchet distance, and Kullback-Leibler divergence), and provides computational tools for each of them that may be used in computational biology. We illustrate the use of the selected similarity measures in diagnosing differences within two extremely contrasting sets of biological data, which, remarkably, may both be relevant for magnetic field perception by migratory birds. Specifically, we assess and discuss cryptochrome protein conformational dynamics and extreme migratory trajectories of songbirds between Alaska and Africa. We highlight how similarity measures contrast regarding computational complexity and discuss those which can be useful in noise elimination or, conversely, are sensitive to spatiotemporal scales.

Motivation Genotype imputation has the potential to increase the amount of information that can be gained from the often limited biological material available in ancient samples. As many widely used tools have been developed with modern data in mind, their design is not necessarily reflective of the requirements in studies of ancient DNA. Here, we investigate if an imputation method based on the full probabilistic Li and Stephens model of haplotype frequencies might be beneficial for the particular challenges posed by ancient data. Results We present an implementation called prophaser and compare imputation performance to two alternative pipelines that have been used in the ancient DNA community based on the Beagle software. Considering empirical ancient data downsampled to lower coverages as well as present-day samples with artificially thinned genotypes, we show that the proposed method is advantageous at lower coverages, where it yields improved accuracy and ability to capture rare variation. The software prophaser is optimized for running in a massively parallel manner and achieved reasonable runtimes on the experiments performed when executed on a GPU., eSSENCE - An eScience Collaboration

Glioblastoma is the fastest-growing, and the most common malignant brain tumour in adults. It is normally treated with surgery and radio- or chemotherapy, but the approximate life expectancy is of 15 months with a high probability of cancer recurring. Therefore, there is a need for decreasing its severity. Bulk and single-cell RNA sequencing allow the identification of cellular states in tumours affected by cell-intrinsic and extrinsic factors. Four different cellular states have been identified in glioblastoma: neural progenitor-like, oligodendrocyte progenitor-like, astrocyte-like, and mesenchymal-like. As glioblastoma is an immunosuppressive tumour, it can alter the immune system and increase the tumour's immune escaping by segregating immunosuppressive factors or interacting with the brain microenvironment.Two datasets were used in this study to explore if the localization of the tumour in the brain microenvironment and the tendency of glioblastomas to activate microglial cells are due to particular ligand-receptor interactions. Data quality control was applied to both datasets and SingleCellSignalR and CellphoneDB packages were used to predict the possible interactions. A total of seven experiments were designed for this study. The first dataset, GBmap, allowed us to do a comparison between tumour cells and microglia, tumour cells and other cell types in the brain, and the four cellular states of glioblastoma with microglia and macrophages. Next, healthy microglia from GBmap was used to compare with the tumour bulk data from the second dataset, HGCC. The bootstrap technique was performed to compare bulk data vs single-cell data, and a comparison between tumour cells and microglia or other cell types was analysed.Results showed specific and shared interactions between cell types or cellular states, revealing the different localization of the tumour cells depends on the expressed ligand-receptor pairs. Also, a total of four patterns of interactions were found in

In eukaryotes, cellular differentiation is often orchestrated by programmed arrays of activation and repression of genes underlying the specific phenotypes of cell-types. To complete its life cycle, the single-celled Apicomplexan parasite Plasmodium falciparum, the most deadly of the human malaria parasites, must repeatedly differentiate and convert into unique cell types that can exploit niches within their human and mosquito hosts. One of these key developmental transitions occurs in the human host and includes sexual-stage commitment, followed by gametocyte development, which results in male and female gametocytes that can infect the female anopheline mosquito once taken up during the blood meal. The following gamete stage undergoes fusion and meiosis, followed by morphological changes as they invade the midgut wall to form the oocyst. The sexual stages are responsible for malaria transmission and the spread of antimalarial resistance, making it an important target for malaria eradication. Previous studies have established the understanding of a few mechanisms involved in sexual differentiation; the transcription factor, AP2-G, is a master regulator of sexual commitment. Uncovering genes and regulators underlying P. falciparum sexual stage development, including host-parasite interactions are the subjects of this thesis. In paper I, we describe the temporal landscape of differential transcriptional programs underlying the P. falciparum male and female gametocyte development and highlight a bifurcation point for the onset of differential male and female transcriptional programs. Furthermore, we predict novel candidate driver genes underlying sexual cell fate determination in P. falciparum. Finally, we traced the potential sex-specific regulatory mechanisms among the ApiAP2 family of transcription factors including prediction of target genes based on co-expression and the presence of upstream binding sites, rendering a motif-driven gene regulatory network. In

In eukaryotes, cellular differentiation is often orchestrated by programmed arrays of activation and repression of genes underlying the specific phenotypes of cell-types. To complete its life cycle, the single-celled Apicomplexan parasite Plasmodium falciparum, the most deadly of the human malaria parasites, must repeatedly differentiate and convert into unique cell types that can exploit niches within their human and mosquito hosts. One of these key developmental transitions occurs in the human host and includes sexual-stage commitment, followed by gametocyte development, which results in male and female gametocytes that can infect the female anopheline mosquito once taken up during the blood meal. The following gamete stage undergoes fusion and meiosis, followed by morphological changes as they invade the midgut wall to form the oocyst. The sexual stages are responsible for malaria transmission and the spread of antimalarial resistance, making it an important target for malaria eradication. Previous studies have established the understanding of a few mechanisms involved in sexual differentiation; the transcription factor, AP2-G, is a master regulator of sexual commitment. Uncovering genes and regulators underlying P. falciparum sexual stage development, including host-parasite interactions are the subjects of this thesis. In paper I, we describe the temporal landscape of differential transcriptional programs underlying the P. falciparum male and female gametocyte development and highlight a bifurcation point for the onset of differential male and female transcriptional programs. Furthermore, we predict novel candidate driver genes underlying sexual cell fate determination in P. falciparum. Finally, we traced the potential sex-specific regulatory mechanisms among the ApiAP2 family of transcription factors including prediction of target genes based on co-expression and the presence of upstream binding sites, rendering a motif-driven gene regulatory network. In

Cell-cell communication plays a significant role in shaping the functionality of the cells. Communication between the cells is also responsible for maintaining the physiological state of the cells and the tissue. Therefore, it is important to study the different ways by which cell-cell communication impacts the functional state of cells. Alterations in cell-cell communication can contribute to the development of disease conditions. In this thesis, we present two computational tools and a study to explore the different aspects of cell-cell communication. In the first manuscript, FunRes was developed to leverage the cell-cell communication model to investigate functional heterogeneity in cell types and characterize cell states based on the integration of interand intra-cellular signalling. This tool utilizes a combination of receptors and transcription factors (TFs) based on the reconstructed cell-cell communication network to split the cell types into functional states. The tool was applied to the TabulaMurisSenis atlas to discover functional cell states in both young and old mouse datasets. In addition, we compared our tool with state-of-the-art tools and validated the cell states using available markers from the literature. Secondly, we studied the evolution of gene expression in developing astrocytes under normal and inflammatory conditions. We characterized these cells using both transcriptional and chromatin accessibility data which were integrated to reconstruct the gene regulatory networks (GRNs) specific to the condition and timepoints. The GRNs were then topologically analyzed to identify key regulators of the developmental process under both normal and inflammatory conditions. In the final manuscript, we developed a computational tool that identified regulators of allergy and tolerance in a mouse model. The tool works by first reconstructing the cell-cell communication network and then analyzing the network for feedback loops. These feedback loops are impor

Genome-wide mappings of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome. Computational approaches such as ChromHMM have been applied to discover the combinatorial and spatial patterns of chromatin marks in a biosample, characterize them as chromatin states, and subsequently annotate the biosample’s epigenome into chromatin states. As more biosamples’ chromatin marks data are generated, it becomes more challenging to manually study biological similarities and differences in the chromatin state maps across many biosamples. We therefore have developed methods to derive epigenome annotations that incorporate data from multiple biosamples and highlight notable epigenetic properties. First, we introduced a large-scale application of ChromHMM that generates a universal chromatin state map for the human genome that can be shared across cell types. In particular, we trained ChromHMM with input data from >1,000 experiments in >100 human biosamples from Roadmap Epigenomics and ENCODE projects. We denoted the resulting chromatin state map the ‘full-stack’ annotation. We conducted comprehensive analyses to characterize the full-stack states’ biological interpretations, and uncovered patterns of cell-type-specific and constitutive regulatory activities in each state. The full-stack annotation, along with detailed state characterizations, are useful for researchers in understanding the epigenetic contexts of genomic loci of interests. Building on this work, we developed and analyzed an equivalent universal chromatin state annotation for the mouse genome. We trained such an annotation using input data from >900 ChI-seq/ATAC-seq or DNase-seq experiments from the ENCODE and Mouse ENCODE projects, and characterized the resulting states and related them with those from the human full-stack model. Given the wide applications of mice as a model organism to study human disease mechanisms, the

Spinal Cord Injury (SCI) is a devastating condition that results in a serious burden on the health care system. Currently, no therapeutics for the disease exist, and thus research into generating effective therapies for SCI is a paramount concern. One exciting avenue for treatment is neural stem cell (NSC) transplantation. This method has shown efficacy in rodent models, but the therapeutic outcome is highly dependent upon the timing of the transplantation relative to injury. Acutely transplanted cells will migrate primarily towards the injury epicenter, exhibiting pronounced astrogliosis, and not improving animal recovery vs. vehicle control. NSC transplanted with a delay of 9 or more days relative to injury will instead migrate more distally from the epicenter, exhibit a more neuronal and oligodendrocytic phenotype, and will improve animal locomotor recovery vs. vehicle control. This dramatic shift in outcome is due to parallel shifts in the post-SCI microenvironment, including an increase in immune components such as C1q, the recognition molecule of the complement cascade. Here, I first highlight recently published data from my lab showing that C1q signals to NSC through several receptors, including CD44, a receptor known to regulate a wide variety of cellular behaviors. We have shown that C1q signals to NSC through CD44, serving as a chemotactic cue. CD44 knockout (KO) NSC, when transplanted into an acute SCI niche, exhibited significantly reduced astrogliosis and migration to the epicenter compared to wild-type (WT) NSC. Additionally, CD44 KO NSC were able to restore locomotor function beyond vehicle control, whereas the WT NSC remained unable to. Next, I then show data from three distinct projects. First, I examine the effect of CD44 KO on human NSC transplanted into the chronic phase of SCI, where I observe that CD44 is necessary for transplant engraftment. Then, I test the effect of CD44 KO on the endogenous response to SCI, discovering that CD44 KO animals

Dengue is an acute viral illness caused by the RNA virus of the family Flaviviridae and spread by Aedes mosquitoes. Intense dengue is a spreading cause of serious disease and death in some ’Asian and South American’ countries. Pathogenesis is associated with the amended functioning of our innate immune system during infection. Toll Like Receptor is influential for the involucre of innate immunity able to cause dengue infection disease like pattern receptor recognition. Toll-like receptors induced by injury of a certain severity arbitrate activation of interferons and Fc receptors arbitrate the involucre of cytokines. Clearance of dengue virus is associated with interferon protein; however regulatory mechanisms have been adopted against this modified effect. The clearance is considered to be a steady state known to be characterized by a low threshold level of DENV. The pathogenic state is characterized by a high threshold level of SOCS. SOCS protein is also induced due to interferon and cytokine-amended signaling, which can subsequently play its part in the regulation of interferon and cytokine production. Our hypothesis in this research the innate immunity system is associated between the pathogenesis of the dengue virus and the SOCS-amended inhibition. We used the static formalism model of the biological regulatory network of Toll-like receptors induced by the pathogenesis of dengue amended signaling pathway. A model verification method used in GINsim was used to deduce the logical parameters for the qualitative modelling. Therefore, a multidisciplinary and translational study we constructed a formal model constraint the approach with a static and integrative computational analysis, which may identify new immunopathological mechanisms and biomarkers for differential diagnosis, opening the way for the development of specific therapies that will reduce mortality and induce morbidity by dengue virus.

In eukaryotes, cellular differentiation is often orchestrated by programmed arrays of activation and repression of genes underlying the specific phenotypes of cell-types. To complete its life cycle, the single-celled Apicomplexan parasite Plasmodium falciparum, the most deadly of the human malaria parasites, must repeatedly differentiate and convert into unique cell types that can exploit niches within their human and mosquito hosts. One of these key developmental transitions occurs in the human host and includes sexual-stage commitment, followed by gametocyte development, which results in male and female gametocytes that can infect the female anopheline mosquito once taken up during the blood meal. The following gamete stage undergoes fusion and meiosis, followed by morphological changes as they invade the midgut wall to form the oocyst. The sexual stages are responsible for malaria transmission and the spread of antimalarial resistance, making it an important target for malaria eradication. Previous studies have established the understanding of a few mechanisms involved in sexual differentiation; the transcription factor, AP2-G, is a master regulator of sexual commitment. Uncovering genes and regulators underlying P. falciparum sexual stage development, including host-parasite interactions are the subjects of this thesis. In paper I, we describe the temporal landscape of differential transcriptional programs underlying the P. falciparum male and female gametocyte development and highlight a bifurcation point for the onset of differential male and female transcriptional programs. Furthermore, we predict novel candidate driver genes underlying sexual cell fate determination in P. falciparum. Finally, we traced the potential sex-specific regulatory mechanisms among the ApiAP2 family of transcription factors including prediction of target genes based on co-expression and the presence of upstream binding sites, rendering a motif-driven gene regulatory network. In

Motivation Genotype imputation has the potential to increase the amount of information that can be gained from the often limited biological material available in ancient samples. As many widely used tools have been developed with modern data in mind, their design is not necessarily reflective of the requirements in studies of ancient DNA. Here, we investigate if an imputation method based on the full probabilistic Li and Stephens model of haplotype frequencies might be beneficial for the particular challenges posed by ancient data. Results We present an implementation called prophaser and compare imputation performance to two alternative pipelines that have been used in the ancient DNA community based on the Beagle software. Considering empirical ancient data downsampled to lower coverages as well as present-day samples with artificially thinned genotypes, we show that the proposed method is advantageous at lower coverages, where it yields improved accuracy and ability to capture rare variation. The software prophaser is optimized for running in a massively parallel manner and achieved reasonable runtimes on the experiments performed when executed on a GPU., eSSENCE - An eScience Collaboration