Cryptic genetic variation in a heat shock protein modifies the outcome of a mutation affecting epidermal stem cell development in C. elegans

Inference of a three-gene network underpinning epidermal stem cell development in Caenorhabditis elegans

Gene regulatory networks are crucial in cellular decision-making, making the inference of their architecture essential for understanding organismal development. The gene network of Caenorhabditis elegans epidermal stem cells, known as seam cells, remains undefined. Here, we integrate experimental data, mathematical modeling, and statistical inference to investigate this network, focusing on three core transcription factors (TFs), namely ELT-1, EGL-18, and CEH-16. We use single-molecule FISH to quantify TF mRNA levels in single seam cells of wild-type and mutant backgrounds across four early larval stages. Using Modular Response Analysis, we predict TF interactions and uncover a repressive interaction between CEH-16 and egl-18 consistent across time points. We validate its significance at the L1 stage with ordinary differential equations and Bayesian modeling, making testable predictions for a double mutant. Our findings reveal TF regulatory relationships in seam cells and demonstrate a flexible mathematical framework for inferring gene regulatory networks from gene expression data.

Higher-order epistasis shapes natural variation in germ stem cell niche activity

To study how natural allelic variation explains quantitative developmental system variation, we characterized natural differences in germ stem cell niche activity, measured as progenitor zone (PZ) size, between two Caenorhabditis elegans isolates. Linkage mapping yielded candidate loci on chromosomes II and V, and we found that the isolate with a smaller PZ size harbours a 148 bp promoter deletion in the Notch ligand, lag-2/Delta, a central signal promoting germ stem cell fate. As predicted, introducing this deletion into the isolate with a large PZ resulted in a smaller PZ size. Unexpectedly, restoring the deleted ancestral sequence in the isolate with a smaller PZ did not increase-but instead further reduced-PZ size. These seemingly contradictory phenotypic effects are explained by epistatic interactions between the lag-2/Delta promoter, the chromosome II locus, and additional background loci. These results provide first insights into the quantitative genetic architecture regulating an animal stem cell system.

Programmed Upregulation of HSP70 by Metal-Organic Frameworks Nanoamplifier for Enhanced Microwave Thermal-Immunotherapy

Thermotherapy can directly kill tumor cells whilst being accompanied by immune-enhancing effects. However, this immune-enhancing effect suffers from insufficient expression of immune response factors (e.g., heat shock protein 70, HSP70), resulting in no patient benefiting due to the recurrence of tumor cells after thermotherapy. Herein, a nanoengineered strategy of programmed upregulating of the immune response factors for amplifying synergistic therapy is explored. Metal-organic frameworks nanoamplifiers (teprenone/nitrocysteine@ZrMOF-NH₂ @L-menthol@triphenylphosphine, GGA/CSNO@ZrMOF-NH₂ -LM-TPP nanoamplifier, and GCZMT nanoamplifier) achieve excellent microwave (MW) thermal-immunotherapy by programmed induction of HSP70 expression. After intravenous administration, GCZMT nanoamplifiers target the mitochondria, and then release nitric oxide (NO) under MW irradiation. NO inhibits the growth of tumor cells by interfering with the energy supply of cells. Subsequently, under the combination of MW, NO, and GGA, HSP70 expression can be programmed upregulated, which can induce the response of cytotoxic CD4⁺ T cells and CD8⁺ T cells, and effectively activate antitumor immunotherapy. Hence, GCZMT nanoamplifier-mediated MW therapy can achieve a satisfactory therapeutic effect with the tumor inhibition of 97%. This research offers a distinctive insight into the exploitation of metal-organic frameworks nanoamplifiers for enhanced tumor therapy, which provides a new approach for highly effective cancer treatment.

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins enhancing the evolvability, has therefore attracted interest. However, our current understanding of how genes influence adaptation kinetics is limited, partly because accurately measuring adaptation for many cell populations is challenging. We used a high-throughput adaptive laboratory evolution platform to track the adaptation of >18,000 cell populations corresponding to single-gene deletion strains in the haploid yeast deletion collection. We report that the preadaptation fitness of gene knockouts near-perfectly (R2= 0.91) predicts their adaptation to arsenic, leaving at the most a marginal role for dedicated evolvability gene functions. We tracked the adaptation of another >23,000 gene knockout populations to a diverse range of selection pressures and generalized the almost perfect (R2=0.72-0.98) capacity of preadaptation fitness to predict adaptation. We also reconstructed mutations in FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wild-type cells, in gene deletions covering a broad fitness range and show that the predictability of arsenic adaptation can be understood as a by global epistasis, where excluding arsenic is more beneficial to arsenic unfit cells. The paucity of genes with a meaningful evolvability effect on adaptation diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

Automated recognition and analysis of head thrashes behavior in C. elegans

Background

Locomotive behaviors are a rapid evaluation indicator reflecting whether the nervous system of worms is damaged, and has been proved to be sensitive to chemical toxicity. In many toxicological studies, C. elegans head thrashes is a key indicator of locomotive behaviors to measure the vitality of worms. In previous studies, the number of head thrashes was manually counted, which is time-consuming and labor-intensive.

Results

This paper presents an automatic recognition and counting method for head thrashes behavior of worms from experimental videos. First, the image processing algorithm is designed for worm morphology features calculation, mean gray values of head and tail are used to locate the head of worm accurately. Next, the worm skeleton is extracted and divided into equal parts. The angle formulas are used to calculate the bending angle of the head of worm. Finally, the number of head thrashes is counted according to the bending angle of the head in each frame. The robustness of the proposed algorithm is evaluated by comparing the counting results of the manual counting. It is proved that the proposed algorithm can recognize the occurrence of head thrashes of C. elegans of different strains. In addition, the difference of the head thrashes behavior of different worm strains is analyzed, it is proved that the relationship between worm head thrashes behavior and lifespan.

Conclusions

A new method is proposed to automatically count the number of head thrashes of worms. This algorithm makes it possible to count the number of head thrashes from the worm videos collected by the automatic tracking system. The proposed algorithm will play an important role in toxicological research and worm vitality research. The code is freely available at https://github.com/hthana/HTC.

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.