Interaction of MLE with CLAMP zinc finger is involved in proper MSL proteins binding to chromosomes in Drosophila.

The Drosophila male-specific lethal (MSL) complex binds to the male X chromosome to activate transcription. It comprises five proteins (MSL1, MSL2, MSL3, male absent on the first (MOF), and maleless (MLE)) and two long noncoding RNAs (lncRNAs; roX1 and roX2). The MLE helicase remodels the roX lncRNAs, enabling the lncRNA-mediated assembly of the Drosophila dosage compensation complex. MSL2 is expressed only in males and interacts with the N-terminal zinc finger of the transcription factor chromatin-linked adapter for MSL proteins (CLAMP), which is important for the specific recruitment of the MSL complex to the male X chromosome. Here, we found that MLE's unstructured C-terminal region interacts with the sixth zinc-finger domain of CLAMP. In vitro, 4-5 zinc fingers are critical for the specific DNA-binding of CLAMP with GA repeats, which constitute the core motif at the high affinity binding sites for MSL proteins. Deleting the CLAMP binding region in MLE decreases the association of MSL proteins with the male X chromosome and increases male lethality. These results suggest that interactions of unstructured regions in MSL2 and MLE with CLAMP zinc finger domains are important for the specific recruitment of the MSL complex to the male X chromosome.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches.

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

The hallmarks of dietary intervention-resilient gut microbiome.

Maintaining equilibrium of the gut microbiome is crucial for human health. Diet represents an important and generally accessible natural channel of controlling the nutrients supply to the intestinal microorganisms. Although many studies showed that dietary interventions can specifically modulate gut microbiome composition, further progress of the approach is complicated by interindividual variability of the microbial community response. The reported causes of this variability include the baseline microbiome composition features, but it is unclear whether any of them are intervention-specific. Here, we applied a unified computational framework to investigate the variability of microbiome response measured as beta diversity in eight various dietary interventions using previously published 16S rRNA sequencing datasets. We revealed a number of baseline microbiome features which determine the microbiome response in an intervention-independent manner. One of the most stable associations, reproducible for different interventions and enterotypes, was a negative dependence of the response on the average number of genes per microorganism in the community-an indicator of the community functional redundancy. Meanwhile, many revealed microbiome response determinants were enterotype-specific. In Bact1 and Rum enterotypes, the response was negatively correlated with the baseline abundance of their main drivers. Additionally, we proposed a method for preliminary assessment of the microbiome response. Our study delineats the universal features determining microbiome response to diverse interventions. The proposed approach is promising for understanding the mechanisms of gut microbiome stability and improving the efficacy of personalised microbiome-tailored interventions.