Search for: All records

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types. 

Chinese government lifted its “Zero COVID-19” policy in December 2022. The estimated COVDI-19 new cases and deaths after the policy change are 167–279 million (about 12.0% to 20.1% of the Chinese population) and 0.68–2.1 million, respectively. Recent data also revealed continuous drops in fertility rate and historically lowest growth in gross domestic production in China. Thus, balancing COVID-19 control and economic recovery in China is of paramount importance yet very difficult. Supply chain disruption, essential service reduction and shortage of intensive care units have been discussed as the challenges associated with lifting “Zero COVID-19” policy. The additional challenges may include triple epidemic of COVID-19, respiratory syncytial virus and influenza, mental health issues of healthcare providers, care givers and patients, impact on human mobility, lack of robust genomic and epidemiological data and long COVID-19. However, the policy-associated opportunities and other challenges are largely untouched, but warrant attention of and prompt reactions by the policy makers, healthcare providers, public health officials and other stakeholders. The associated benefits are quick reach of herd immunity, boost of economy and businesses activities and increase in social activities. At this moment, we must embrace the policy change, effectively mitigate its associated problems and timely and effectively maximize its associated benefits. 

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery. 

ABSTRACT Although Wnt signaling is clearly important for the intestinal epithelial homeostasis, the relevance of various sources of Wnt ligands themselves remains incompletely understood. Blocking the release of Wnt in distinct stromal cell types suggests obligatory functions of several stromal cell sources and yields different observations. The physiological contribution of epithelial Wnt to tissue homeostasis remains unclear. We show here that blocking epithelial Wnts affects colonic Reg4+ epithelial cell differentiation and impairs colonic epithelial regeneration after injury in mice. Single-cell RNA analysis of intestinal stroma showed that the majority of Wnt-producing cells were contained in transgelin (Tagln+) and smooth muscle actin α2 (Acta2+) expressing populations. We genetically attenuated Wnt production from these stromal cells using Tagln-Cre and Acta2-CreER drivers, and found that blockage of Wnt release from either epithelium or Tagln+ and Acta2+ stromal cells impaired colonic epithelial healing after chemical-induced injury. Aggregated blockage of Wnt release from both epithelium and Tagln+ or Acta2+ stromal cells drastically diminished epithelial repair, increasing morbidity and mortality. These results from two uncharacterized stromal populations suggested that colonic recovery from colitis-like injury depends on multiple Wnt-producing sources. 

Paneth cells are the primary source of C-type lysozyme, a b-1,4-N-acetylmuramoylhydrolase that enzymatically processes bacterial cell walls. Paneth cells are normally present in human cecum and ascending colon, but are rarely found in descending colon and rectum; Paneth cell metaplasia in this region and aberrant lysozyme production are hallmarks of inflammatory bowel disease (IBD) pathology. Here, we examined the impact of aberrant lysozyme production in colonic inflammation. Targeted disruption of Paneth cell lysozyme (Lyz1) protected mice from experimental colitis. Lyz1-deficiency diminished intestinal immune responses to bacterial molecular patterns and resulted in the expansion of lysozyme-sensitive mucolytic bacteria, including Ruminococcus gnavus, a Crohn’s disease-associated pathobiont. Ectopic lysozyme production in colonic epithelium suppressed lysozyme-sensitive bacteria and exacerbated colitis. Transfer of R. gnavus into Lyz1
/
hosts elicited a type 2 immune response, causing epithelial reprograming and enhanced anti-colitogenic capacity. In contrast, in lysozyme-intact hosts, processed R. gnavus drove pro-inflammatory responses. Thus, Paneth cell lysozyme balances intestinal anti- and pro-inflammatory responses, with implications for IBD.