Computational Workflow Research Articles

Data-Independent Acquisition Peptidomics.

In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework "Nextflow" ( https://nextflow.io ). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user's interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.

Parametric modeling and fabrication as capturing knowledge: A design computation workflow for historical brick surfaces in Anatolia

Using computational design tools to create meaningful digital representations of architectural heritage delivers both challenges and opportunities. On one hand, digital tools aid the fast and detailed three-dimensional modeling of architectural elements. On the other hand, these models do not sufficiently document the materials and techniques of making. This research proposes a workflow to use computational design tools to analyze historic Anatolian brick elements while integrating their geometry, construction, and part-whole relations in parametric modeling and robotic fabrication processes. Our approach demonstrates a correlation between the design of the surface pattern and material application in historical bricklaying. The proposed workflow can be applied to formalize implicit design knowledge, integrating it into the digital environment and numeric control production codes. This holistic approach to heritage prioritizes both the tangible aspects, such as form and material, and intangible aspects such as the knowledge base of applied techniques.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine.

Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians' failure to make accurate clinical diagnoses based on patients' clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient's clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient's clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis.

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the “route” of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Thermodynamic calculations using reverse Monte Carlo: A computational workflow for accelerated construction of phase diagrams for metal hydrides

Metal hydrides are promising candidates for hydrogen storage applications. From a materials discovery perspective, an accurate, efficient computational workflow is urgently required that can rapidly analyze/predict thermodynamic properties of these materials. In this paper, we develop a thermodynamic property estimation/phase diagram construction framework based on the lattice reverse Monte Carlo (RMC) method. We apply the technique to the nickel hydride (NiHx) system by calculating the pressure-composition-temperature (PCT) isotherm and constructing its phase diagram. Formation of nickel hydride entails significant volume expansion, strong interaction between hydrogen and the host atoms, lattice strain, and a phase transition. An attractive feature of our approach is that the entire phase diagram can be accurately constructed in few minutes by considering <10 configurations. In contrast, a popular technique based on grand canonical Monte Carlo would require sampling of several million configurations. The promising results obtained with our computational workflow suggest that the approach can be used in future for wider materials search and discovery.

A bi-objective workflow scheduling in virtualized fog-cloud computing using NSGA-II with semi-greedy initialization

Virtualized Fog-Cloud Computing (VFCC) has emerged as a promising computing model in both research and industry. Its inherent characteristics, such as real-time service provisioning, resource heterogeneity, flexibility, and scalable computational resources, present new opportunities and challenges in the scheduling of computational workflows. Efficient workflow scheduling is crucial in optimizing resource utilization and improving service delivery in VFCC environments. In this paper, we first present a bi-objective optimization model for the workflow scheduling problem in VFCC systems with the aim of minimizing both the system’s makespan and energy consumption. We then propose an efficient list-based method to solve the model. The proposed method consists of two distinct phases. In the first phase, we employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine task priorities, establishing the task execution sequence. To ensure both convergence speed and diversity, we implement an intelligent semi-greedy approach when generating the initial population. In the second phase, we introduce an energy and makespan-aware heuristic algorithm to efficiently allocate virtual machines to the prioritized tasks. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using both synthetic and real datasets, including the Epigenomics, Montage, and LIGO graphs. We compare the proposed method with the Heterogeneous Earliest Finish Time (HEFT) and Green-HEFT algorithms in terms of makespan and energy consumption, respectively. Furthermore, we evaluate our proposed method by comparing it to two other algorithms, Multi-objective HEFT (MOHEFT) and Multi-objective ACO (MOACO), using various quality indicators such as generation distance (GD), inverse generation distance (IGD), goal programming approach (GPA), and Hyper volume (HV). The simulation results demonstrate that our method effectively reduces the makespan by 6.38% to 51.19% and energy consumption by 3.52% to 16.49%. Moreover, on average, our method outperforms the other algorithms with an improvement of 2.3%, 5.7%, 9.8%, and 9.34% for the GD, IGD, GPA, and HV metrics, respectively.

A computational design workflow towards a comprehensive digitization of historic buildings

The rapid technological development in recent years and the ability to collect a huge amount of information affects the field of archaeology of standing buildings and conservation. Digital technology is an emerging tool that can be applied in recent works in this field, leading to an effective and efficient recording of building heritage and accompanied information. Hence, such technology can provide a deeper understanding, learning and justification of its behavior as well as utilization of these information throughout a maintenance process. The present study aims to formulate a new computational design workflow based on the 3D modeling of historic buildings, through the creation of an as-build model of a registered listed building. This is achieved through parametric clustering and schematic modeling based on computational methods for developing 3D surfaces and shapes that correspond to building parts. Challenges of 3D modeling originated in the complexity of form, such as the selected building part under investigation, are discussed. For the implementation of the research, the Holy Church of Agios Georgios Tropaiophoros in the village of Dali in the district of Nicosia (CY) is chosen as a case study. The results derived from the specific workflow are evaluated and then used for the development of an Augmented Reality (AR) platform, an emerging technology with great potential in perception and navigation of building heritage.

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase.

Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method. Communicated by Ramaswamy H. Sarma

Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs.

The fundamental role of any neuron within a network is to transform complex spatiotemporal synaptic input patterns into individual output spikes. These spikes, in turn, act as inputs for other neurons in the network. Neurons must execute this function across a diverse range of physiological conditions, often based on species-specific traits. Therefore, it is crucial to determine the extent to which findings can be extrapolated between species and, ultimately, to humans. In this study, we employed a multidisciplinary approach to pinpoint the factors accounting for the observed electrophysiological differences between mice and rats, the two species most used in experimental and computational research. After analyzing the morphological properties of their hippocampal CA1 pyramidal cells, we conducted a statistical comparison of rat and mouse electrophysiological features in response to somatic current injections. This analysis aimed to uncover the parameters underlying these distinctions. Using a well-established computational workflow, we created ten distinct single-cell computational models of mouse CA1 pyramidal neurons, ready to be used in a full-scale hippocampal circuit. By comparing their responses to a variety of somatic and synaptic inputs with those of rat models, we generated experimentally testable hypotheses regarding species-specific differences in ion channel distribution, kinetics, and the electrophysiological mechanisms underlying their distinct responses to synaptic inputs during the behaviorally relevant Gamma and Sharp-Wave rhythms.

Mass uptake during oxidation of metallic alloys: Literature data collection, analysis, and FAIR sharing

The area-normalized change of mass (Δm/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB’s Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. These numbers were obtained assuming an experimental noise of 2.5% of the mass gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is open access and researchers can add their own raw data (those to be included in future publications, as well as negative results) for analysis and to share their work with the community. Such consistent and systematic analysis of open, community-generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance.

Abstract B075: Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery

Abstract Endogenous cellular metabolites have been shown to bind and regulate proteins involved in tumorigenesis. These low-affinity metabolite-protein interactions can modulate protein function through binding at allosteric sites. As an example, in normal cells, the ABL1 protein is regulated by N-terminal myristylation, which binds to a hydrophobic pocket in the C-terminal lobe of the kinase domain autoinhibiting its activity. Fusion of BCR to ABL1 leads to deletion of the N-terminal myristylation site activating BCR-ABL oncoprotein leading to chronic myeloid leukemia (CML). Similarly, autopalmitoylation of TEAD proteins regulates transcriptional output of Hippo signaling and tumorigenesis. Allosteric metabolite binding sites on these targets have been successfully used to generate new medicines (e.g.: Asciminib for BCR-ABL) and investigational cancer therapies (e.g.: IK-930, VT3989 for TEAD). However, there are limited approaches that can be broadly deployed to discover functional regulatory sites on precision oncology targets. Atavistik Bio has developed the AMPS (Atavistik Metabolite Protein Screening) platform that enables systematic and scalable screening of metabolites against proteins or protein complexes of interest. The AMPS platform technology integrates equilibrium dialysis with sensitive mass-spectrometry to screen a proprietary metabolite library. A state-of-the-art computational workflow coupled with deep mechanistic structure-function characterization enables us to discover novel functional allosteric modulators. To validate the platform, we have screened several precision oncology targets and characterized the AMPS metabolite hits with functional assays. For ABL1 tyrosine kinase, we have applied AMPS on distinct enzyme forms to probe the role of metabolite binding in the context of phosphorylation states and drug resistant T315I mutant forms. In addition, AMPS allows us to investigate novel metabolite sensitization of ABL1 in the presence of existing drugs Imatinib and Asciminib. Results from AMPS and follow up experiments highlight phosphorylation state dependency on myristate binding to ABL1. In conclusion, we demonstrate the AMPS platform can identify highly conserved allosteric protein-metabolite interactions to advance small molecule drug discovery efforts for new precision medicines. Citation Format: Anil K Padyana, Anna Schinzel, Gordon Murray, Joseph H LaPointe, Kaitlyn Selmer, Truc Pham, Melissa J Buskes, Shomit Sengupta, Maria-Jesus Blanco, Thomas Roddy, Marion Dorsch. Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B075.

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this regard, several studies have used gene expression changes in blood to predict the presence of CTC and, consequently, cancer. However, the CTC mRNA data has not been used to develop a generic approach that indicates the presence of multiple cancer types. In this study, we developed such a generic approach. Briefly, we designed two computational workflows, one using the raw mRNA data and deep learning (DL) and the other exploiting five hub gene ranking algorithms (Degree, Maximum Neighborhood Component, Betweenness Centrality, Closeness Centrality, and Stress Centrality) with machine learning (ML). Both workflows aim to determine the top genes that best distinguish cancer types based on the CTC mRNA data. We demonstrate that our automated, robust DL framework (DNNraw) more accurately indicates the presence of multiple cancer types using the CTC gene expression data than multiple ML approaches. The DL approach achieved average precision of 0.9652, recall of 0.9640, f1-score of 0.9638 and overall accuracy of 0.9640. Furthermore, since we designed multiple approaches, we also provide a bioinformatics analysis of the gene commonly identified as top-ranked by the different methods. To our knowledge, this is the first study wherein a generic approach has been developed to predict the presence of multiple cancer types using raw CTC mRNA data, as opposed to other models that require a feature selection step.

Hunting for Complex Organic Molecules in the Interstellar Medium: The Role of Accurate Low-Cost Theoretical Geometries and Rotational Constants.

A new approach to computation at affordable cost of accurate geometrical structures and rotational constants for medium-sized molecules in the gas phase is further improved and applied to a large panel of interstellar complex organic molecules. The most distinctive feature of the new model is the effective inclusion of core-valence correlation and vibrational averaging effects in the framework of density functional theory (DFT). In particular, a double-hybrid functional in conjunction with a quadruple-ζ valence/triple-ζ polarization basis set is employed for geometry optimizations, whereas a cheaper hybrid functional in conjunction with a split-valence basis set is used for the evaluation of vibrational corrections. A thorough benchmark based on a wide range of prototypical systems shows that the new scheme approaches the accuracy of state-of-the-art wave function methods with the computational cost of the standard methods (DFT or MP2) routinely employed in the interpretation of microwave spectra. Since the whole computational workflow involves the postprocessing of the output of standard electronic structure codes by a new freely available web utility, the way is paved for the accurate yet not prohibitively expensive study of medium- to large-sized molecules also by nonspecialists.

City-scale buildings damage estimation based on broadband physics-based ground motion simulation of 2021 Ms 6.4 Yangbi, China, earthquake

This article proposed a computational workflow for city-scale building damage estimation based on broadband physics-based ground motion simulation. The dynamic stiffness matrix method in frequency-wavenumber domain is employed for broadband physics-based ground motion simulation of finite fault along with site response correction using equivalent-linear site response analysis approach. With simulated ground motion, seismic response of buildings, using multi-degree of freedom model, is simulated using nonlinear time history analysis, and building damage is evaluated based on a trilinear backbone curve of story’s nonlinear force-drift model. Main advantage of the framework lies in the employed dynamic stiffness matrix method for ground motion simulation, which can efficiently generate broadband ground motions in perfect parallel and naturally capture the physics of finite fault source, propagation path, and spatial variability characteristics of ground motion. To demonstrate the salient features of proposed workflow, the 2021 Ms 6.4 Yangbi earthquake is simulated with frequency resolution up to 20 Hz, and seismic damage of buildings in Yangbi county are estimated. Results show that most traditional wood-adobe structures suffer moderate to severe damage, and engineered seismic buildings (mostly RC frame) in Yangbi county suffer minor damage, which agrees with field investigation results.

Emergence of Highly-Plastic B Cell States Cooperates with Early Immune Microenvironment Remodeling to Drive Follicular Lymphomagenesis

Follicular lymphoma (FL) is a prototypical example of B cell lymphoma arising from germinal center (GC) B cells. Recent single cell transcriptomic studies investigating the transcriptional heterogeneity of FL suggest that tumor B cells are functionally diverse with cells acquiring an intermediate phenotype between GC and memory (mem), in line with the premalignant FL ontogeny where BCL2 + memory B cells require multiple re-entries into the GC to facilitate the transformation process. Since FL cells often hijack the epigenetic machinery through mutations in histone modifying proteins - notably KMT2D - we questioned here how epigenetic alterations contribute to the emergence of this intratumoral heterogeneity. We developed a genetically engineered mouse FL-like model (GEMM) carrying Kmt2d loss-of-function in B cells and BCL2 overexpression recapitulating different progression stages from preneoplastic hyperplasia to FL-like tumors. We analyzed 45,541 high-quality single cell transcriptomes and immune repertoires (B cell receptor, BCR and T cell receptor, TCR) from 18 mice including immunized-control mice in the context of physiological GC reaction. We developed a computational workflow on mutant B cells combining supervised and unsupervised metaclustering approaches in order to highlight recurrent gene programs active in preneoplastic hyperplasia and the various stages leading to FL-like tumors. We used inferCNV to assess the association between genetics and transcriptomic heterogeneity and inferred cell-cell interactions to characterize the microenvironmental determinants associated with B cell heterogeneity throughout FL progression. By modeling the GC cycle dynamics and building B cell signatures during a normal immune response, we found that our FL GEMM mirrored the desynchronization of the GC reaction program previously observed in human FL. Single cell expression profiles of Kmt2d-BCL2 mutant cells also showed a reproducible increase in phenotypic heterogeneity during FL progression, manifesting as a continuum of cell states from proliferating GC-like to quiescent mem-like cell states. We defined this GC-to-Mem axis as the major source of intra-tumor transcriptional heterogeneity. Single-cell BCR sequencing revealed the emergence of highly-plastic B cell states during FL evolution, co-existing within individual mice and being largely independent of subclonal genetic variation. To further elucidate the emergence and maintenance of these cell state transitions, we explored the phenotypic and transcriptional changes within the immune microenvironment along disease progression. Significant compositional changes were found, including the expansion of T follicular helper cells and T regulatory T cells together with the loss of naive CD4, CD8 T, and NK cell populations, preceding malignant transformation and creating a (pre)tumoral supportive niche persisting in advanced tumors. Prior remodeling of a premalignant supportive immune niche may be crucial in establishing malignant cell heterogeneity. Ongoing efforts aim to impair tumor-immune interactions driving tumor B cell dynamics and survival. In conclusion, our results provide a high-resolution view of events spanning FL progression and indicate a major role of early TME remodeling in establishing a suitable niche for progression. Early intervention aimed to target the B cell-tumor microenvironment interactions driving intra-tumoral heterogeneity may represent a promising therapeutic avenue against early disease and prevention of FL recurrence.

Improving genome-wide mapping of nucleosomes in Trypanosome cruzi.

In Trypanosoma cruzi DNA is packaged into chromatin by octamers of histone proteins that form nucleosomes. Transcription of protein coding genes in trypanosomes is constitutive producing polycistronic units and gene expression is primarily regulated post-transcriptionally. However, chromatin organization influences DNA dependent processes. Hence, determining nucleosome position is of uppermost importance to understand the peculiarities found in trypanosomes. To map nucleosomes genome-wide in several organisms, digestion of chromatin with micrococcal nuclease followed by deep sequencing has been applied. Nonetheless, the special requirements for cell manipulation and the uniqueness of the chromatin organization in trypanosomes entails a customized analytical approach. In this work, we adjusted this broadly used method to the hybrid reference strain, CL Brener. Particularly, we implemented an exhaustive and thorough computational workflow to overcome the difficulties imposed by this complex genome. We tested the performance of two aligners, Bowtie2 and HISAT2, and discuss their advantages and caveats. Specifically, we highlight the relevance of using the whole genome as a reference instead of the commonly used Esmeraldo-like haplotype to avoid spurious alignments. Additionally, we show that using the whole genome refines the average nucleosome representation, but also the quality of mapping for every region represented. Moreover, we show that the average nucleosome organization around trans-splicing acceptor site described before, is not just an average since the same chromatin pattern is detected for most of the represented regions. In addition, we extended the study to a non-hybrid strain applying the experimental and analytical approach to Sylvio-X10 strain. Furthermore, we provide a source code for the construction of 2D plots and heatmaps which are easy to adapt to any T. cruzi strain.

A new computational workflow to guide personalized drug therapy

Objective:Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. Methods:GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. Results:To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. Conclusion:GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.