Resolving tumor evolution:a phylogenetic approach

The evolutionary dynamics of cancer,characterized by its profound heterogeneity,demand sophisticated tools for a holistic understanding.This review delves into tumor phylogenetics,an essential approach bridging evolu-tionary biology with oncology,offering unparalleled insights into cancer’s evolutionary trajectory.We provide an overview of the workflow,encompassing study design,data acquisition,and phylogeny reconstruction.No-tably,the integration of diverse data sets emerges as a transformative step,enhancing the depth and breadth of evolutionary insights.With this integrated perspective,tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

shinyTempSignal:an R shiny application for exploring temporal and other phylogenetic signals

The molecular clock model is fundamental for inferring species divergence times from molecular sequences.However,its direct application may introduce significant biases due to sequencing errors,recombination events,and inaccurately labeled sampling times.Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences.Furthermore,while not all branches of a phylogenetic tree may exhibit a clear temporal signal,specific branches may still adhere to the assumptions,with varying evolutionary rates.Supporting a relaxed molecular clock model better aligns with the complexities of evolution.The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals.Despite its utility,there remains a lack of corresponding software implementations for broader applications.To address this gap,we present shinyTempSignal,an interactive web application implemented with the shiny framework,available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal.This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models.By extending the root-to-tip regression method to diverse signals,shinyTempSignal helps in the detection of evolving features or traits,thereby laying the foundation for deeper insights and subsequent analyses.

Decoding the brain:Unveiling comprehensive cellular atlases through multiomics mega-data

The pursuit to comprehend the intricate nature of the brain,an organ of unparalleled complexity,remains a persistent endeavor in the field of neuroscience.1 Recent strides by the National Institutes of Health’s BRAIN Initiative–Cell Census Network(BICCN)have yielded comprehensive cellular atlases for mice and humans,2,3 published in Nature and Science,respectively.Additionally,a 3D cell-type atlas of the macaque brain,contributed by Cell,offers profound insights into non-human primate cortical intricacies.4 These collaborative initiatives,driven by the US’s BICCN and China’s Brain Project,represent a significant leap forward in unraveling the cellular intricacies of the brain,culminating in groundbreaking exploration across mice,non-human primates,and humans.The confluence of these works serves to elucidate the intricate cellular landscapes of the brain through a multiomics approach,signifying a pivotal advancement in neuroscience research toward comprehensive understanding and collaborative exploration(Figure 1).

Understanding the risks of mercury sulfide nanoparticles in the environment: Formation, presence, and environmental behaviors

Mercury(Hg) could be microbially methylated to the bioaccumulative neurotoxin methylmercury(Me Hg), raising health concerns. Understanding the methylation of various Hg species is thus critical in predicting the Me Hg risk. Among the known Hg species, mercury sulfide(HgS) is the largest Hg reservoir in the lithosphere and has long been considered to be highly inert. However, with advances in the analytical methods of nanoparticles, HgS nanoparticles(HgS NPs) have recently been detected in various environmental matrices or organisms. Furthermore, pioneering laboratory studies have reported the high bioavailability of HgS NPs. The formation, presence, and transformation(e.g., methylation) of HgS NPs are intricately related to several environmental factors, especially dissolved organic matter(DOM). The complexity of the behavior of HgS NPs and the heterogeneity of DOM prevent us from comprehensively understanding and predicting the risk of HgS NPs. To reveal the role of HgS NPs in Hg biogeochemical cycling, research needs should focus on the following aspects: the formation pathways, the presence, and the environmental behaviors of HgS NPs impacted by the dominant influential factor of DOM. We thus summarized the latest progress in these aspects and proposed future research priorities, e.g., developing the detection techniques of HgS NPs and probing HgS NPs in various matrices, further exploring the interactions between DOM and HgS NPs. Besides, as most of the previous studies were conducted in laboratories, our current knowledge should be further refreshed through field observations, which would help to gain better insights into predicting the Hg risks in natural environment.

clusterProfiler 4.0:A universal enrichment tool for interpreting omics data

Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.

Sulfur-driven methylmercury production in paddies continues following soil oxidation

Methylmercury(MeHg) production in paddy soils and its accumulation in rice raise global concerns since rice consumption has been identified as an important pathway of human exposure to MeHg. Sulfur(S) amendment via fertilization has been reported to facilitate Hg methylation in paddy soils under anaerobic conditions, while the dynamic of S-amendment induced MeHg production in soils with increasing redox potential remains unclear. This critical gap hinders a comprehensive understanding of Hg biogeochemistry in rice paddy system which is characterized by the fluctuation of redox potential. Here, we conducted soil incubation experiments to explore MeHg production in slow-oxidizing paddy soils amended with different species of S and doses of sulfate. Results show that the elevated redox potential(1) increased MeHg concentrations by 10.9%-35.2%, which were mainly attributed to the re-oxidation of other S species to sulfate and thus the elevated abundance of sulfatereducing bacteria, and(2) increased MeHg phytoavailability by up to 75% due to the reductions in acid volatile sulfide(AVS) that strongly binds MeHg in soils. Results obtained from this study call for attention to the increased MeHg production and phytoavailability in paddy soils under elevated redox potentials due to water management, which might aggravate the MeHg production induced by S fertilization and thus enhance MeHg accumulation in rice.

MicrobiotaProcess:A comprehensive R package for deep mining microbiome

The data output from microbiome research is growing at an accelerating rate,yet mining the data quickly and efficiently remains difficult.There is still a lack of an effective data structure to represent and manage data,as well as flexible and composable analysis methods.In response to these two issues,we designed and developed the MicrobiotaProcess package.It provides a comprehensive data structure,MPSE,to better integrate the primary and intermediate data,which improves the integration and exploration of the downstream data.Around this data structure,the downstream analysis tasks are decomposed and a set of functions are designed under a tidy framework.These functions independently perform simple tasks and can be combined to perform complex tasks.