Profiling population-wide exposure to environmental chemicals: A case study of naphthalene

Long-term exposure to environmental chemicals can detrimentally impact human health, and understanding the relationship between age distribution and levels of external and internal exposure is crucial. Nonetheless, existing methods for assessing population-wide exposure across age groups are limited. To bridge this research gap, we introduced a modeling approach designed to assess both chronic external and internal exposure to chemicals at the population level. The external and internal exposure assessments were quantified in terms of the average daily dose (ADD) and steady-state blood concentration of the environmental chemical, respectively, which were categorized by age and gender groups. The modeling process was presented within a spreadsheet framework, affording users the capability to execute population-wide exposure analyses across a spectrum of chemicals. Our simulation outcomes underscored a salient trend: younger age groups, particularly infants and children, exhibited markedly higher ADD values and blood concentrations of environmental chemicals compared to their older counterparts. This observation is due to the elevated basal metabolic rate per unit of body weight characteristic of younger individuals, coupled with their diminished biotransformation kinetics of xenobiotics within their livers. These factors collectively contribute to increased intake rates of environmental chemicals per unit of body weight through air and food consumption, along with heightened bioaccumulation of these chemicals within their bodies (e.g., blood). Furthermore, we augmented the precision of the external and internal exposure assessment by incorporating the age distribution across the population. The simulation outcomes unveiled that, to estimate the central tendency of the population's exposure levels, employing the baseline value group (age group 21-30) or the surrogate age of 25 serves as a simple yet dependable approach. However, for comprehensive population protection, our recommendation aligns with conducting exposure assessments for the younger age groups (age group 0-11). Future studies should integrate individual-level exposure assessment, analyze vulnerable population groups, and refine population structures within our developed model.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.

SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

BACKGROUND

The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology.

RESULTS

We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system.

CONCLUSIONS

SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

TrackUSF, a novel tool for automated ultrasonic vocalization analysis, reveals modified calls in a rat model of autism

Background

Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals.

Results

Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls.

Conclusions

TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01299-y.

Computational tools and resources for CRISPR/Cas genome editing

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genomic editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduced the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focused on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights on the limitations of available computational tools that surely help researchers of this field for further optimization. Lastly, we suggested a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.

In silico characterization of the GH5-cellulase family from uncultured microorganisms: physicochemical and structural studies

BACKGROUND

Hydrolysis of cellulose-based biomass by cellulases produce fermented sugar for making biofuels, such as bioethanol. Cellulases hydrolyze the β-1,4-glycosidic linkage of cellulose and can be obtained from cultured and uncultured microorganisms. Uncultured microorganisms are a source for exploring novel cellulase genes through the metagenomic approach. Metagenomics concerns the extraction, cloning, and analysis of the entire genetic complement of a habitat without cultivating microbes. The glycoside hydrolase 5 family (GH5) is a cellulase family, as the largest group of glycoside hydrolases. Numerous variants of GH5-cellulase family have been identified through the metagenomic approach, including CelGH5 in this study. University-CoE-Research Center for Biomolecule Engineering, Universitas Airlangga successfully isolated CelGH5 from waste decomposition of oil palm empty fruit bunches (OPEFB) soil by metagenomics approach. The properties and structural characteristics of GH5-cellulases from uncultured microorganisms can be studied using computational tools and software.

RESULTS

The GH5-cellulase family from uncultured microorganisms was characterized using standard computational-based tools. The amino acid sequences and 3D-protein structures were retrieved from the GenBank Database and Protein Data Bank. The physicochemical analysis revealed the sequence length was roughly 332-751 amino acids, with the molecular weight range around 37-83 kDa, dominantly negative charges with pI values below 7. Alanine was the most abundant amino acid making up the GH5-cellulase family and the percentage of hydrophobic amino acids was more than hydrophilic. Interestingly, ten endopeptidases with the highest average number of cleavage sites were found. Another uniqueness demonstrated that there was also a difference in stability between in silico and wet lab. The II values indicated CelGH5 and ACA61162.1 as unstable enzymes, while the wet lab showed they were stable at broad pH range. The program of SOPMA, PDBsum, ProSA, and SAVES provided the secondary and tertiary structure analysis. The predominant secondary structure was the random coil, and tertiary structure has fulfilled the structure quality of QMEAN4, ERRAT, Ramachandran plot, and Z score.

CONCLUSION

This study can afford the new insights about the physicochemical and structural properties of the GH5-cellulase family from uncultured microorganisms. Furthermore, in silico analysis could be valuable in selecting a highly efficient cellulases for enhanced enzyme production.

LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing

BACKGROUND

Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate < 150 bp reads. However, there is a general lack of software tools for gene fusion detection from long-read RNA-seq data, which takes into account the high basecalling error rates and the presence of alignment errors.

RESULTS

In this study, we developed a fast computational tool, LongGF, to efficiently detect candidate gene fusions from long-read RNA-seq data, including cDNA sequencing data and direct mRNA sequencing data. We evaluated LongGF on tens of simulated long-read RNA-seq datasets, and demonstrated its superior performance in gene fusion detection. We also tested LongGF on a Nanopore direct mRNA sequencing dataset and a PacBio sequencing dataset generated on a mixture of 10 cancer cell lines, and found that LongGF achieved better performance to detect known gene fusions over existing computational tools. Furthermore, we tested LongGF on a Nanopore cDNA sequencing dataset on acute myeloid leukemia, and pinpointed the exact location of a translocation (previously known in cytogenetic resolution) in base resolution, which was further validated by Sanger sequencing.

CONCLUSIONS

In summary, LongGF will greatly facilitate the discovery of candidate gene fusion events from long-read RNA-Seq data, especially in cancer samples. LongGF is implemented in C++ and is available at https://github.com/WGLab/LongGF .

Development of a new educational package based on e-learning to study engineering thermodynamics process: combustion, energy and entropy analysis

This paper presents a new educational package based on e-learning called TermolabUA integrated by three programs, which are VOLCONTROL focused on the analysis of steady-state flow devices, CarnotCycle aimed to analyze reversible and irreversible processes, and CombustionUA to study combustion processes. The educational package was designed for both, to promote significant learning on some thermodynamic topics in undergraduate students, and to help the student to reach the cognitive competencies of interpreting, arguing and proposing, and interacting with the different graphical user interfaces to solve relevant cases studies. Also, the teaching-learning activity helps them to understand the influence of a specific variable on the energy and entropy behavior of the selected systems, which is traditionally studied manually in a classroom. The results of the t-Student tests showed that the average grades obtained by the students in the problems using the software were higher than the average grade without using the software. The estimate for the average grade difference was 0.56 with a P-value = 3.31E-13 for Problem 1 and 0.631 with a P-value = 3.31E-13 for Problem 2 in the Workshop- VOLCONTROL. Similar results were obtained for the problems reported in the CarnotCycle and CombustionUA Workshop with an estimate for average grade differences and P-values lower than 0.79 and 0.05, respectively. It means that the new software package significantly improved the learning skills of the students.

Motion microscopy for label-free detection of circulating breast tumor cells

Circulating tumor cells (CTCs) are cancer cells that have been shed from a primary tumor and circulate in the bloodstream during progression of cancer. They may thus serve as circulating biomarkers that can predict, diagnose and guide therapy. Moreover, phenotypic and genotypic analysis of CTCs can facilitate prospective assessment of mutations and enable personalized treatment. A number of methodologies based on biological and physical differences between circulating tumor and non-tumor cells have been developed over the past few years. However, these methods did not have sufficient sensitivity or specificity. In this work, a remote analysis protocol was designed using motion microscopy that amplifies cellular micro motions in a captured video by re-rendering small motions to generate extreme magnified visuals to detect dynamic motions that are not otherwise visible by naked eye. Intriguingly, motion microscopy demonstrated fluctuations around breast tumor cells, which we referred to herein as cellular trail. Phenomena of cellular trail mostly emerged between 0.5 and 1.5 Hz on amplified video images. Interestingly, cellular trails were associated with cell surface proteins and flow rates rather than mitochondrial activity. Moreover, cellular trails were present only around circulating tumor cells from individuals with breast cancer under conditions of 20-30 μm/s and 0.5-1.5 Hz. Thus, motion microscopy based CTC detection method can offer a valuable supplementary diagnostic tool for assessment of drug efficacy and identifying physical characteristics of tumor cells for further research.

NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data

BACKGROUND

Recent advances in single-molecule sequencing techniques, such as Nanopore sequencing, improved read length, increased sequencing throughput, and enabled direct detection of DNA modifications through the analysis of raw signals. These DNA modifications include naturally occurring modifications such as DNA methylations, as well as modifications that are introduced by DNA damage or through synthetic modifications to one of the four standard nucleotides.

METHODS

To improve the performance of detecting DNA modifications, especially synthetically introduced modifications, we developed a novel computational tool called NanoMod. NanoMod takes raw signal data on a pair of DNA samples with and without modified bases, extracts signal intensities, performs base error correction based on a reference sequence, and then identifies bases with modifications by comparing the distribution of raw signals between two samples, while taking into account of the effects of neighboring bases on modified bases ("neighborhood effects").

RESULTS

We evaluated NanoMod on simulation data sets, based on different types of modifications and different magnitudes of neighborhood effects, and found that NanoMod outperformed other methods in identifying known modified bases. Additionally, we demonstrated superior performance of NanoMod on an E. coli data set with 5mC (5-methylcytosine) modifications.

CONCLUSIONS

In summary, NanoMod is a flexible tool to detect DNA modifications with single-base resolution from raw signals in Nanopore sequencing, and will facilitate large-scale functional genomics experiments that use modified nucleotides.

A tool for calculating concentration ratios from large environmental datasets

This paper presents a tool for calculating concentration ratios from a large and structured environmental dataset of radionuclide activity and metal concentrations. The tool has been developed in MS Excel™ and includes a simple user interface for setting up queries. The tool is capable of matching environmental media samples to biota samples based on user-defined spatial and temporal criteria to derive a representative estimate of the environmental exposure conditions of an organism and its accumulation. Some potential benefits and uses of the tool are discussed.