Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), can efficiently cluster heterogeneous samples in single-cell RNA sequencing analysis. Yet, the application of t-SNE and UMAP in bulk transcriptomic analysis and comparison with conventional methods have not been achieved. We compare four major dimensionality reduction methods (PCA, multidimensional scaling [MDS], t-SNE, and UMAP) in analyzing 71 large bulk transcriptomic datasets. UMAP is superior to PCA and MDS but shows some advantages over t-SNE in differentiating batch effects, identifying pre-defined biological groups, and revealing in-depth clusters in two-dimensional space. Importantly, UMAP generates sample clusters uncovering biological features and clinical meaning. We recommend deploying UMAP in visualizing and analyzing sizable bulk transcriptomic datasets to reinforce sample heterogeneity analysis.

As a hallmark of immunological aging, low-grade, chronic inflammation with accumulation of effector memory T cells contributes to increased susceptibility to many aging-related diseases. While the proinflammatory state of aged T cells indicates a dysregulation of immune homeostasis, whether and how aging drives regulatory T cell (Treg) aging and alters Treg function are not fully understood owing to a lack of specific aging markers. Here, by a combination of cellular, molecular, and bioinformatic approaches, we discovered that Tregs senesce more severely than conventional T (Tconv) cells during aging. We found that Tregs from aged mice were less efficient than young Tregs in suppressing Tconv cell function in an inflammatory bowel disease model and in preventing Tconv cell aging in an irradiation-induced aging model. Furthermore, we revealed that DDB1- and CUL4-associated factor 1 (DCAF1) was downregulated in aged Tregs and was critical to restrain Treg aging via reactive oxygen species (ROS) regulated by glutathione-S-transferase P (GSTP1). Importantly, interfering with GSTP1 and ROS pathways reinvigorated the proliferation and function of aged Tregs. Therefore, our studies uncover an important role of the DCAF1/GSTP1/ROS axis in Treg senescence, which leads to uncontrolled inflammation and immunological aging.

Genetic polymorphisms leading to variations in human milk oligosaccharide (HMO) composition have been reported. Alpha-Tetrasaccharide (A-tetra), an HMO, has been shown to only be present (>limit of detection; A-tetra+) in the human milk (HM) of women with blood type A, suggesting genetic origins determining the presence or absence (A-tetra-) of A-tetra in HM. This study aimed to determine whether associations exist between HMO concentrations and cognitive development, and whether the associations vary between A-tetra+ and A-tetra- groups in children (<25 months old). We enrolled typically developing children (2–25 months old; mean, 10 months old) who were at least partially breastfed at the study visit. The Mullen Scales of Early Learning (MSEL) were used as the primary outcome measure to assess early cognitive development. Linear mixed effects models were employed by stratifying children based on A-tetra levels (A-tetra+ or A-tetra-) to assess associations between age-removed HMO concentrations and both MSEL composite scores and the 5 subdomain scores. A total of 99 mother-child dyads and 183 HM samples were included (A-tetra+: 57 samples, 33 dyads; A-tetra-: 126 samples, 66 dyads). No significant association was observed between HMOs and MSEL when all samples were analyzed together. The composite score and 3’-sialyllactose (3’-SL) levels were positively associated [P = 0.002; effect size (EF), 13.12; 95% CI, 5.36–20.80] in the A-tetra + group. This association was driven by the receptive (adjusted P = 0.015; EF, 9.95; 95% CI, 3.91–15.99) and expressive (adjusted P = 0.048; EF, 7.53; 95% CI, 2.51–13.79) language subdomain scores. Furthermore, there was an interaction between 3’-SL and age for receptive language (adjusted P = 0.03; EF, -14.93; 95% CI, -25.29 to -4.24). Our study reports the association of 3’-SL and cognition, particularly language functions, in typically developing children who received HM containing detectable A-tetra during infancy.

DNA damage tolerance and mutagenesis are hallmarks and enabling characteristics of neoplastic cells that drive tumorigenesis and allow cancer cells to resist therapy. The ‘Y-family’ trans-lesion synthesis (TLS) DNA polymerases enable cells to replicate damaged genomes, thereby conferring DNA damage tolerance. Moreover, Y-family DNA polymerases are inherently error-prone and cause mutations. Therefore, TLS DNA polymerases are potential mediators of important tumorigenic phenotypes. The skin cancer-propensity syndrome xeroderma pigmentosum-variant (XPV) results from defects in the Y-family DNA Polymerase Pol eta (Polη) and compensatory deployment of alternative inappropriate DNA polymerases. However, the extent to which dysregulated TLS contributes to the underlying etiology of other human cancers is unclear. Here we consider the broad impact of TLS polymerases on tumorigenesis and cancer therapy. We survey the ways in which TLS DNA polymerases are pathologically altered in cancer. We summarize evidence that TLS polymerases shape cancer genomes, and review studies implicating dysregulated TLS as a driver of carcinogenesis. Because many cancer treatment regimens comprise DNA-damaging agents, pharmacological inhibition of TLS is an attractive strategy for sensitizing tumors to genotoxic therapies. Therefore, we discuss the pharmacological tractability of the TLS pathway and summarize recent progress on development of TLS inhibitors for therapeutic purposes.

Streptococcus mutanshas been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity ofSelenomonas sputigena, Prevotella salivaeandLeptotrichia wadei, either individually or withS. mutans. We show thatS. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulatingS. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability ofS. sputigenato colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected withS. mutans, S. sputigenacauses extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host–microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.