系统生物学

Bayesian phylogenetic analysis is essential for elucidating evolutionary relationships among organisms. Traditional methods often rely on fixed models and manual parameter settings, which can limit accuracy and efficiency. This protocol presents an integrated workflow that leverages GUIDANCE2 for rigorous sequence alignment, ProtTest and MrModeltest for robust model selection, and MrBayes for phylogenetic tree estimation through Bayesian inference. By automating key steps and providing detailed command-line instructions, this protocol enhances the reliability and reproducibility of phylogenetic studies.

With reduced genotyping costs, genome-wide association studies (GWAS) face more challenges in diverse populations with complex structures to map genes of interest. The complex structure demands sophisticated statistical models, and increased marker density and population size require efficient computing tools. Many statistical models and computing tools have been developed with varied properties in statistical power, computing efficiency, and user-friendly accessibility. Some statistical models were developed with dedicated computing tools, such as efficient mixed model analysis (EMMA), multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). However, there are computing tools (e.g., GAPIT) that implement multiple statistical models, retain a constant user interface, and maintain enhancement on input data and result interpretation. In this study, we developed a protocol utilizing a minimal set of software tools (BEAGLE, BLINK, and GAPIT) to perform a variety of analyses including file format conversion, missing genotype imputation, GWAS, and interpretation of input data and outcome results. We demonstrated the protocol by reanalyzing data from the Rice 3000 Genomes Project and highlighting advancements in GWAS model development.

This manuscript details two modified protocols for the isolation of long-stranded or high molecular weight (HMW) DNA from Magnaporthaceae (Ascomycota) fungal mycelium intended for whole genome sequencing. The Cytiva Nucleon PhytoPure and the Macherey-Nagel NucleoBond HMW DNA kits were selected because the former requires lower amounts of starting material and the latter utilizes gentler methods to maximize DNA length, albeit at a higher requirement for input material. The Cytiva Nucleon PhytoPure kit successfully recovered HMW DNA for half of our fungal species by increasing the amount of RNase A treatment and adding in a proteinase K treatment. To reduce the impact of pigmentation development, which occurs toward later stages of culturing, extractions were run in quadruplicate to increase overall DNA concentration. We also adapted the Macherey-Nagel NucleoBond HMW DNA kit for high-quality HMW DNA by grinding the sample to a fine powder, overnight lysis, and splitting the sample before washing the precipitated DNA. For both kits, precipitated DNA was spooled out pre-washing, ensuring a higher percentage of high-integrity long strands. The Macherey-Nagel protocol offers advantages over the first through the utilization of gravity columns that provide gentler treatment, yielding >50% of high-purity DNA strands exceeding 40 kbp. The limitation of this method is the requirement for a large quantity of starting material (1 g). By triaging samples based on the rate of growth relative to the accumulation of secondary metabolites, our methodologies hold promise for yielding reliable and high-quality HMW DNA from a variety of fungal samples, improving sequencing outcomes.

Zebrafish genetic mutants have emerged as a valuable model system for studying various aspects of disease and developmental biology. Mutant zebrafish embryos are generally identified based on phenotypic defects at later developmental stages, making it difficult to investigate underlying molecular mechanisms at earlier stages. This protocol presents a PCR-based genotyping method that enables the identification of wild-type, heterozygous, and homozygous zebrafish genetic mutants at any developmental stage, even when they are phenotypically indistinguishable. The approach involves the amplification of specific genomic regions using carefully designed primers, followed by gel electrophoresis. This genotyping method facilitates the investigation of the molecular mechanisms driving phenotypic defects that are observed at later timepoints. This protocol allows researchers to perform analyses such as immunofluorescence, RT-PCR, RNA sequencing, and other molecular experiments on early developmental stages of mutants. The availability of this protocol expands the utility of zebrafish genetic mutants for elucidating the molecular underpinnings of various biological processes throughout development.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. According to 2020 reports, globally, 2.2 million cases are reported every year, with the mortality number being as high as 1.8 million patients. To study NSCLC, systems biology offers mathematical modeling as a tool to understand complex pathways and provide insights into the identification of biomarkers and potential therapeutic targets, which aids precision therapy. Mathematical modeling, specifically ordinary differential equations (ODEs), is used to better understand the dynamics of cancer growth and immunological interactions in the tumor microenvironment. This study highlighted the dual role of the cyclic GMP-AMP synthase–stimulator of interferon genes (cGAS/STING) pathway's classical involvement in regulating type 1 interferon (IFN I) and pro-inflammatory responses to promote tumor regression through senescence and apoptosis. Alternative signaling was induced by nuclear factor kappa B (NF-κB), mutated tumor protein p53 (p53), and programmed death-ligand1 (PD-L1), which lead to tumor growth. We identified key regulators in cancer progression by simulating the model and validating it with the following model estimation parameters: local sensitivity analysis, principal component analysis, rate of flow of metabolites, and model reduction. Integration of multiple signaling axes revealed that cGAS-STING, phosphoinositide 3-kinases (PI3K), and Ak strain transforming (AKT) may be potential targets that can be validated for cancer therapy.

Mitochondrial genomes (mitogenomes) display relatively rapid mutation rates, low sequence recombination, high copy numbers, and maternal inheritance patterns, rendering them valuable blueprints for mapping lineages, uncovering historical migration patterns, understanding intraspecific population dynamics, and investigating how environmental pressures shape traits underpinned by genetic variation. Here, we present the bioinformatic pipeline and code used to assemble and annotate the complete mitogenomes of five houndsharks (Chondrichthyes: Triakidae) and compare them to the mitogenomes of other closely related species. We demonstrate the value of a combined assembly approach for detecting deviations in mitogenome structure and describe how to select an assembly approach that best suits the sequencing data. The datasets required to run our analyses are available on the GitHub and Dryad repositories.

The limited standards for the rigorous and objective use of mitochondrial genomes (mitogenomes) can lead to uncertainties regarding the phylogenetic relationships of taxa under varying evolutionary constraints. The mitogenome exhibits heterogeneity in base composition, and evolutionary rates may vary across different regions, which can cause empirical data to violate assumptions of the applied evolutionary models. Consequently, the unique evolutionary signatures of the dataset must be carefully evaluated before selecting an appropriate approach for phylogenomic inference. Here, we present the bioinformatic pipeline and code used to expand the mitogenome phylogeny of the order Carcharhiniformes (groundsharks), with a focus on houndsharks (Chondrichthyes: Triakidae). We present a rigorous approach for addressing difficult-to-resolve phylogenies, incorporating multi-species coalescent modelling (MSCM) to address gene/species tree discordance. The protocol describes carefully designed approaches for preparing alignments, partitioning datasets, assigning models of evolution, inferring phylogenies based on traditional site-homogenous concatenation approaches as well as under multispecies coalescent and site heterogenous models, and generating statistical data for comparison of different topological outcomes. The datasets required to run our analyses are available on GitHub and Dryad repositories.

Capturing produced, consumed, or exchanged metabolites (metabolomics) and the result of gene expression (transcriptomics) require the extraction of metabolites and RNA. Multi-omics approaches and, notably, the combination of metabolomics and transcriptomic analyses are required for understanding the functional changes and adaptation of microorganisms to different physico-chemical and environmental conditions. A protocol was developed to extract total RNA and metabolites from less than 6 mg of a kind of phototrophic biofilm: oxygenic photogranules. These granules are aggregates of several hundred micrometers up to several millimeters. They harbor heterotrophic bacteria and phototrophs. After a common step for cell disruption by bead-beating, a part of the volume was recovered for RNA extraction, and the other half was used for the methanol- and dichloromethane-based extraction of metabolites. The solvents enabled the separation of two phases (aqueous and lipid) containing hydrophilic and lipophilic metabolites, respectively. The ¹H nuclear magnetic resonance (NMR) analysis of these extracts produced spectra that contained over a hundred signals with a signal-to-noise ratio higher than 10. The quality of the spectra enabled the identification of dozens of metabolites per sample. Total RNA was purified using a commercially available kit, yielding sufficient concentration and quality for metatranscriptomic analysis. This novel method enables the co-extraction of RNA and metabolites from the same sample, as opposed to the parallel extraction from two samples. Using the same sample for both extractions is particularly advantageous when working with inherently heterogeneous complex biofilm. In heterogeneous systems, differences between samples may be substantial. The co-extraction will enable a holistic analysis of the metabolomics and metatranscriptomics data generated, minimizing experimental biases, including technical variations and, notably, biological variability. As a result, it will ensure more robust multi-omics analyses, particularly by improving the correlation between metabolic changes and transcript modifications.

Many small molecules require derivatization to increase their volatility and to be amenable to gas chromatographic (GC) separation. Derivatization is usually time-consuming, and typical batch-wise procedures increase sample variability. Sequential automation of derivatization via robotic liquid handling enables the overlapping of sample preparation and analysis, maximizing time efficiency and minimizing variability. Herein, a protocol for the fully automated, two-stage derivatization of human blood–based samples in line with GC–[Orbitrap] mass spectrometry (MS)-based metabolomics is described. The protocol delivers a sample-to-sample runtime of 31 min, being suitable for better throughput routine metabolomic analysis.

Bone repair is a complex regenerative process relying on skeletal stem/progenitor cells (SSPCs) recruited predominantly from the periosteum. Activation and differentiation of periosteal SSPCs occur in a heterogeneous environment, raising the need for single cell/nucleus transcriptomics to decipher the response of the periosteum to injury. Enzymatic cell dissociation can induce a stress response affecting the transcriptome and lead to overrepresentation of certain cell types (i.e., immune and endothelial cells) and low coverage of other cell types of interest. To counteract these limitations, we optimized a protocol to isolate nuclei directly from the intact periosteum and from the fracture callus to perform single-nucleus RNA sequencing. This protocol is adapted for fresh murine periosteum, fracture callus, and frozen human periosteum. Nuclei are isolated using mechanical extraction combined with fluorescence-based nuclei sorting to obtain high-quality nucleus suspensions. This protocol allows the capture of the full diversity of cell types in the periosteum and fracture environment to better reflect the in vivo tissue composition.