pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing Halloumi cheese bacterial communities through metagenomic analysis
Authors: Eleni Kamilari, Dimitrios A. Anagnostopoulos, Photis Papademas, Andreas Kamilaris, Dimitris Tsaltas
Date: 2020-04-03 00:00:00
Tasks: Diversity

Halloumi is a semi hard cheese produced in Cyprus for centuries and its popularity has significantly risen over the past years. High throughput sequencing (HTS) was applied in the present research to characterize traditional Cyprus Halloumi bacterial diversity. Eighteen samples made by different milk mixtures and produced in different areas of the country were analyzed, to reveal that Halloumi microbiome was mainly comprised by lactic acid bacteria (LAB), including Lactobacillus, Leuconostoc, and Pediococcus, as well as halophilic bacteria, such as Marinilactibacillus and Halomonas. Additionally, spore forming bacteria and spoilage bacteria, were also detected. Halloumi produced with the traditional method, had significantly richer bacterial diversity compared to Halloumi produced with the industrial method. Variations detected among the bacterial communities highlight the contribution of the initial microbiome that existed in milk and survived pasteurization, as well as factors associated with Halloumi manufacturing conditions, in the final microbiota composition shaping. Identification and characterization of Halloumi microbiome provides an additional, useful tool to characterize its typicity and probably safeguard it from fraud products that may appear in the market. Also, it may assist producers to further improve its quality and guarantee consumers safety.
HIV-1 virus cycle replication: a review of RNA polymerase II transcription, alternative splicing and protein synthesis
Authors: Miguel Ramos Pascual
Date: 2019-03-12 00:00:00
Tasks: TAR

HIV virus replication is a time-related process that includes several stages. Focusing on the core steps, RNA polymerase II transcripts in an early stage pre-mRNA containing regulator proteins (i.e nef,tat,rev,vif,vpr,vpu), which are completely spliced by the spliceosome complex (0.9kb and 1.8kb) and exported to the ribosome for protein synthesis. These splicing and export processes are regulated by tat protein, which binds on Trans-activation response (TAR) element, and by rev protein, which binds to the Rev-responsive Element (RRE). As long as these regulators are synthesized, splicing is progressively inhibited (from 4.0kb to 9.0kb) and mRNAs are translated into structural and enzymatic proteins (env, gag-pol). During this RNAPII scanning and splicing, around 40 different multi-cystronic mRNA have been produced. Long-read sequencing has been applied to the HIV-1 virus genome (type HXB2CG) with the HIV.pro software, a fortran 90 code for simulating the virus replication cycle, specially RNAPII transcription, exon/intron splicing and ribosome protein synthesis, including the frameshift at gag/pol gene and the ribosome pause at env gene. All HIV-1 virus proteins have been identified as far as other ORFs. As observed, tat/rev protein regulators have different length depending on the splicing cleavage site: tat protein varies from 224aa to a final state of 72aa, whereas rev protein from 25aa to 27aa, with a maximum of 119aa. Furthermore, several ORFs coding for small polypeptides sPEP (less than 10 amino acids) and for other unidentified proteins have been localised with unknown functionality. The detailed analysis of the HIV virus replication and the virus proteomics are important for identifying which antigens are presented by macrophages to CD4 cells, for localizing reactive epitopes or for creating transfer vectors to develop new HIV vaccines and effective therapies.
Coronavirus SARS-CoV-2: Analysis of subgenomic mRNA transcription, 3CLpro and PL2pro protease cleavage sites and protein synthesis
Authors: Miguel Ramos Pascual
Date: 2020-04-02 00:00:00
Tasks: Diagnostic

Coronaviruses have recently caused world-wide severe outbreaks: SARS (Severe Acute Respiratory Syndrome) in 2002 and MERS (Middle-East Respiratory Syndrome) in 2012. At the end of 2019, a new coronavirus outbreak appeared in Wuhan (China) seafood market as first focus of infection, becoming a pandemics in 2020, spreading mainly into Europe and Asia. Although the virus family is well-known, this specific virus presents considerable differences, as higher transmission rates, being a challenge for diagnostic methods, treatments and vaccines. Coronavirus(C++).pro is a C++ application which simulates Coronavirus replication cycle. This software has identified virus type in short times and provided FASTA files of virus proteins, a list of mRNA sequences and secondary structures. Furthermore, the software has identified a list of structural, non-structural and accessory proteins in 2019-nCoV virus genome more similar to SARS than to MERS, as several fusion proteins characteristics of this virus type. These results are useful as a first step in order to develop diagnostic methods, new vaccines or antiviral drugs, which could avoid virus replication in any stage: fusion inhibitors, RdRp inhibitors and PL2pro/3CLpro protease inhibitors.
Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing
Authors: Jie Liu, Xiaotian Wu, Kai Zhang, Bing Liu, Renyi Bao, Xiao Chen, Yiran Cai, Yiming Shen, Xinjun He, Jun Yan, Weixing Ji
Date: 2020-04-01 00:00:00

With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing. Here we report on the evaluation of an optimized germline mutation calling pipeline, HummingBird, by assessing its performance against the widely accepted BWA-GATK pipeline. We found that the HummingBird pipeline can significantly reduce the running time of the primary data analysis for whole genome sequencing and whole exome sequencing while without significantly sacrificing the variant calling accuracy. Thus, we conclude that expansion of such software usage will help to improve the primary data analysis efficiency for next generation sequencing.
Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones
Authors: Anastasia A. Minervina, Mikhail V. Pogorelyy, Ekaterina A. Komech, Vadim K. Karnaukhov, Petra Bacher, Elisa Rosati, Andre Franke, Dmitriy M. Chudakov, Ilgar Z. Mamedov, Yuri B. Lebedev, Thierry Mora, Aleksandra M. Walczak
Date: 2019-10-25 00:00:00
Tasks: Diversity

The diverse repertoire of T-cell receptors (TCR) plays a key role in the adaptive immune response to infections. Previous studies show that secondary responses to the yellow fever vaccine - the model for acute infection in humans - are weaker than primary ones, but only quantitative measurements can describe the concentration changes and lineage fates for distinct T-cell clones in vivo over time. Using TCR alpha and beta repertoire sequencing for T-cell subsets, as well as single-cell RNAseq and TCRseq, we track the concentrations and phenotypes of individual T-cell clones in response to primary and secondary yellow fever immunization showing their large diversity. We confirm the secondary response is an order of magnitude weaker, albeit $\sim10$ days faster than the primary one. Estimating the fraction of the T-cell response directed against the single immunodominant epitope, we identify the sequence features of TCRs that define the high precursor frequency of the two major TCR motifs specific for this particular epitope. We also show the consistency of clonal expansion dynamics between bulk alpha and beta repertoires, using a new methodology to reconstruct alpha-beta pairings from clonal trajectories.
Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1
Authors: Chengxin Zhang, Wei Zheng, Xiaoqiang Huang, Eric W. Bell, Xiaogen Zhou, Yang Zhang
Date: 2020-02-08 00:00:00

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.
De Novo Assembly of Uca minax Transcriptome from Next Generation Sequencing
Authors: Hanin Omar, Casey A. Cole, Arjang Fahim, Giuliana Gusmaroli, Stephen Borgianini, Homayoun Valafar
Date: 2020-01-09 00:00:00
Tasks: Management

High-throughput cDNA sequencing (RNA-seq) is a very powerful technique to quantify gene expression in an unbiased way. The Crustacean family is among the groups of organisms sparsely represented in current genomic databases. Here we present transcriptome data from Uca minax (red-jointed fiddler crab) as an opportunity to extend our knowledge. Next generation sequencing was performed on six tissue samples from Uca minax using the Illumina HiSeq system. Six Transcriptome libraries were created using Trinity; a free, open-source software tool for de novo transcriptome assembly of high-throughput mRNA sequencing (RNA-seq) data with the absence of a reference genome. In addition, several tools that aid in management of data were used, such as RSEM, Bowtie, Blast, and IGV; a tool for visualizing RNA-seq analysis results. Fast quality control (FastQC) analysis of the raw sequenced files revealed that both adapter and PCR primer sequences were prevalently present, which may require a preprocessing step.
Minimum error correction-based haplotype assembly: considerations for long read data
Authors: Sina Majidian, Mohammad Hossein Kahaei, Dick de Ridder
Date: 2018-03-13 00:00:00

The single nucleotide polymorphism (SNP) is the most widely studied type of genetic variation. A haplotype is defined as the sequence of alleles at SNP sites on each haploid chromosome. Haplotype information is essential in unravelling the genome-phenotype association. Haplotype assembly is a well-known approach for reconstructing haplotypes, exploiting reads generated by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often used for reconstruction of haplotypes from reads. However, problems with the MEC metric have been reported. Here, we investigate the MEC approach to demonstrate that it may result in incorrectly reconstructed haplotypes for devices that produce error-prone long reads. Specifically, we evaluate this approach for devices developed by Illumina, Pacific BioSciences and Oxford Nanopore Technologies. We show that imprecise haplotypes may be reconstructed with a lower MEC than that of the exact haplotype. The performance of MEC is explored for different coverage levels and error rates of data. Our simulation results reveal that in order to avoid incorrect MEC-based haplotypes, a coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.
A mean first passage time genome rearrangement distance
Authors: Andrew Francis, Henry Wynn
Date: 2019-04-12 00:00:00

This paper introduces a new way to define a genome rearrangement distance, using the concept of mean first passage time from probability theory. Crucially, this distance estimate provides a genuine metric on genome space. We develop the theory and introduce a link to a graph-based zeta function. The approach is very general and can be applied to a wide variety of group-theoretic models of genome evolution.
Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins
Authors: Martin G. Frasch, Byung-Jun Yoon, Dario-Lucas Helbing, Gal Snir, Marta C. Antonelli, Reinhard Bauer
Date: 2019-09-08 00:00:00

Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal glia cell populations, play a pivotal role in neurodevelopment, predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1-4 years of age is autism spectrum disorder (ASD). A pathological glial-neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene-environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conduct an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirm 21 gene hits. Using unsupervised statistical network analysis, we then identify six clusters of probable protein-protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner.
Analytic solution of chemical master equations involving gene switching. I: Representation theory and diagrammatic approach to exact solution
Authors: John J. Vastola, Gennady Gorin, Lior Pachter, William R. Holmes
Date: 2021-03-19 00:00:00
Tasks: valid

The chemical master equation (CME), which describes the discrete and stochastic molecule number dynamics associated with biological processes like transcription, is difficult to solve analytically. It is particularly hard to solve for models involving bursting/gene switching, a biological feature that tends to produce heavy-tailed single cell RNA counts distributions. In this paper, we present a novel method for computing exact and analytic solutions to the CME in such cases, and use these results to explore approximate solutions valid in different parameter regimes, and to compute observables of interest. Our method leverages tools inspired by quantum mechanics, including ladder operators and Feynman-like diagrams, and establishes close formal parallels between the dynamics of bursty transcription, and the dynamics of bosons interacting with a single fermion. We focus on two problems: (i) the chemical birth-death process coupled to a switching gene/the telegraph model, and (ii) a model of transcription and multistep splicing involving a switching gene and an arbitrary number of downstream splicing steps. We work out many special cases, and exhaustively explore the special functionology associated with these problems. This is Part I in a two-part series of papers; in Part II, we explore an alternative solution approach that is more useful for numerically solving these problems, and apply it to parameter inference on simulated RNA counts data.
Impact of the error structure on the design and analysis of enzyme kinetic models
Authors: Elham Yousefi, Werner G. Müller
Date: 2021-03-17 00:00:00

The statistical analysis of enzyme kinetic reactions usually involves models of the response functions which are well defined on the basis of Michaelis-Menten type equations. The error structure however is often without good reason assumed as additive Gaussian noise. This simple assumption may lead to undesired properties of the analysis, particularly when simulations are involved and consequently negative simulated reaction rates may occur. In this study we investigate the effect of assuming multiplicative lognormal errors instead. While there is typically little impact on the estimates, the experimental designs and their efficiencies are decisively affected, particularly when it comes to model discrimination problems.
SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models
Authors: Fernando Palluzzi, Mario Grassi
Date: 2021-03-15 00:00:00
Tasks: Causal Inference

With the advent of high-throughput sequencing (HTS) in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. Although network theory provided a framework to represent biological systems and study their hidden properties, different algorithms still offer low reproducibility and robustness, dependence on user-defined setup, and poor interpretability. Here we discuss the R package SEMgraph, combining network analysis and causal inference within the framework of structural equation modeling (SEM). It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, that is readily interpretable in terms of causal effects among system components. In addition, SEMgraph offers several functions for perturbed path finding, model reduction, and parallelization options for the analysis of large interaction networks.
Integral feedback in synthetic biology: Negative-equilibrium catastrophe
Authors: Tomislav Plesa, Alex Dack, Thomas E. Ouldridge
Date: 2021-02-21 00:00:00

A central goal of synthetic biology is the design of molecular controllers that can manipulate the dynamics of intracellular networks in a stable and accurate manner. To address the fact that detailed knowledge about intracellular networks is unavailable, integral-feedback controllers (IFCs) have been put forward for controlling molecular abundances. These controllers can maintain accuracy in spite of the uncertainties in the controlled networks. However, this desirable feature is achieved only if stability is also maintained. In this paper, we show that molecular IFCs can suffer from a hazardous instability called negative-equilibrium catastrophe (NEC), whereby all nonnegative equilibria vanish under the action of the controllers, and some of the molecular abundances blow up. We show that unimolecular IFCs do not exist due to a NEC. We then derive a family of bimolecular IFCs that are safeguarded against NECs when uncertain unimolecular networks, with any number of molecular species, are controlled. However, when IFCs are applied on uncertain bimolecular (and hence most intracellular) networks, we show that preventing NECs generally becomes an intractable problem as the number of interacting molecular species increases.
A tunable population timer in multicellular consortia
Authors: Carlos Toscano-Ochoa, Jordi Garcia-Ojalvo
Date: 2020-11-04 00:00:00

Processing time-dependent information requires cells to quantify the duration of past regulatory events and program the time span of future signals. At the single-cell level, timer mechanisms can be implemented with genetic circuits: sets of genes connected to achieve specific tasks. However, such systems are difficult to implement in single cells due to saturation in molecular components and stochasticity in the limited intracellular space. Multicellular implementations, on the other hand, outsource some of the components of information-processing circuits to the extracellular space, and thereby might escape those constraints. Here we develop a theoretical framework, based on a trilinear coordinate representation, to study the collective behavior of a cellular population composed of three cell types under stationary conditions. This framework reveals that distributing different processes (in our case the production, detection and degradation of a time-encoding signal) across distinct cell types enables the robust implementation of a multicellular timer. Our analysis also shows the circuit to be easily tunable by varying the cellular composition of the consortium.
Asymptomatic Phase and Convergent Evolution of Coronavirus
Authors: J. C. Phillips
Date: 2021-03-12 00:00:00

CoV2019 has evolved to be much more dangerous than CoV2003. Experiments suggest that structural rearrangements dramatically enhance CoV2019 activity. We identify a new first stage of infection that precedes structural rearrangements by using biomolecular evolutionary theory to identify sequence differences enhancing viral attachment rates. We find a small cluster of four single mutations which show that CoV-2 has a new feature that promotes much stronger viral attachment and enhances contagiousness. The extremely dangerous dynamics of human coronavirus infection is a dramatic example of evolutionary approach of self-organized networks to criticality. It may favor a very successful vaccine. The identified mutations can be used to test the present theory experimentally. The theory also works well for the newer strains and explains their increased contagiousness.
eQuilibrator 3.0 -- a platform for the estimation of thermodynamic constants
Authors: Moritz E. Beber, Mattia G. Gollub, Dana Mozaffari, Kevin M. Shebek, Elad Noor
Date: 2021-02-28 00:00:00

eQuilibrator (equilibrator.weizmann.ac.il) is a calculator for biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts ${\sim}1000$ distinct monthly users, its design could not accommodate larger compound databases and it lacked an application programming interface (API) for integration in other tools developed by the systems biology community. Here, we report a new python-based package for eQuilibrator, that comes with many new features such as a 50-fold larger compound database, the ability to add novel compound structures, improvements in speed and memory use, and correction for Mg2+ ion concentrations. Moreover, it adds the ability to compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modeling. We foresee that these improvements will make thermodynamic modeling more accessible and facilitate the integration of eQuilibrator into other software platforms.