Background: Alzheimer’s disease (AD) is one of the most popular tauopathies. Neurofibrillary tangles and senile plaques are widely recognized as the pathological hallmarks of AD, which are mainly composed of tau and β-amyloid (Aβ) respectively. Recent failures of drugs targeting Aβ have led scientists to scrutinize the crucial impact of tau in neurodegenerative diseases. Mutated or abnormal phosphorylated tau protein loses affinity with microtubules and assembles into pathological accumulations. The aggregation process closely correlates to two amyloidogenic core of PHF6 (³⁰⁶VQIVYK³¹¹) and PHF6* (²⁷⁵VQIINK²⁸⁰) fragments. Moreover, tau accumulations display diverse morphological characteristics in different diseases, which increases the difficulty of providing a unifying neuropathological criterion for early diagnosis.

Results: This review mainly summarizes atomic-resolution structures of tau protein in the monomeric, oligomeric and fibrillar states, as well as the promising inhibitors designed to prevent tau aggregation or disaggregate tau accumulations, recently revealed by experimental and computational studies. We also systematically sort tau functions, their relationship with tau structures and the potential pathological processes of tau protein.

Conclusion: The current progress on tau structures at atomic level of detail expands our understanding of tau aggregation and related pathology. We discuss the difficulties in determining the source of neurotoxicity and screening effective inhibitors. We hope this review will inspire new clues for designing medicines against tau aggregation and shed light on AD diagnosis and therapies.

Background: The advancement of genomics has progressed in lightning speed over the past two decades. Numerous large-scale genome sequencing initiatives were announced, along with the rise of the holistic precision medicine approach. However, the field of genomic medicine has now come to a bottleneck since genomic-phenomic interactions are not fully comprehended due to the complexity of the human systems biology and environmental influence, hence the emergence of human phenomics.

Results: This review attempts to provide an overview of the potential advantages of investigating the human phenomics of indigenous populations and the challenges ahead.

Conclusion: We believe that the indigenous populations serve as an ideal model to excavate our understanding of genomic-environmental-phenomics interactions.

Background: Genome-wide association studies (GWAS) have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases, including cancer. However, the functional interpretation of these non-coding variants remains a significant challenge in the post-GWAS era. Alternative polyadenylation (APA) plays an essential role in post-transcriptional regulation for most human genes. By employing different poly(A) sites, genes can either shorten or extend the 3′-UTRs that contain cis-regulatory elements such as miRNAs or RNA-binding protein binding sites. Therefore, APA can affect the mRNA stability, translation, and cellular localization of proteins. Population-scale studies have revealed many inherited genetic variants that potentially impact APA to further influence disease susceptibility and phenotypic diversity, but systematic computational investigations to delineate the connections are in their earliest states.

Results: Here, we discuss the evolving definitions of the genetic basis of APA and the modern genomics tools to identify, characterize, and validate the genetic influences of APA events in human populations. We also explore the emerging and surprisingly complex molecular mechanisms that regulate APA and summarize the genetic control of APA that is associated with complex human diseases and traits.

Conclusion: APA is an intermediate molecular phenotype that can translate human common non-coding variants to individual phenotypic variability and disease susceptibility.

Background: DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes. The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.

Results: In this review, the main high-throughput detection technologies are summarized, and then the three trends of computational estimation of DNA methylation levels were analyzed, especially the expanding of the methylation data with lower coverage. Furthermore, the detection methods of differential methylation patterns for sequencing and array data were presented.

Conclusions: More and more research indicated the great importance of DNA methylation changes across different diseases, such as cancers. Although a lot of enormous progress has been made in understanding the role of DNA methylation, only few methylated genes or functional elements serve as clinically relevant cancer biomarkers. The bottleneck in DNA methylation advances has shifted from data generation to data analysis. Therefore, it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.

Background: Anthrax is a zoonotic infectious disease caused by Bacillus anthracis. Investigating the spatiotemporal characteristics of anthrax and the impact of meteorological factors on the incidence of anthrax is helpful for the prevention and control of anthrax.

Methods: At first, we applied the Granger causality test to explore the spatiotemporal characteristics of anthrax transmission between the counties and cities of Gannan Tibetan Autonomous Prefecture, Gansu Province of China. Then, we constructed three generalized linear models to analyze the impact of meteorological factors on the monthly number of anthrax cases in Gannan Tibetan Autonomous Prefecture. Finally, we developed an easy-to-use online web server that integrates the above functions.

Results: This study developed an online service website (ASTM Anthrax in Gannan, Zhang Lab) for the analysis and visualization of anthrax, which not only can investigate the correlation of anthrax among different regions in Gannan Tibetan Autonomous Prefecture, but also can analyze the correlation between meteorological factors and the number of anthrax cases.

Conclusions: Our study not only explored spatiotemporal characteristics of anthrax transmission, but also analyzed the impact of seven meteorological factors on the monthly number of anthrax cases. Meanwhile, the online service website which integrates the above functions is useful for the prevention and control of anthrax.

Background: Renal cell carcinoma (RCC) is among the top adult cancers worldwide, with a challenging management due to lack of early diagnosis, therapy resistance, and diverse molecular background. Aberrant DNA methylation has been associated with RCC development due to transcription deregulation. We discovered potential DNA methylation-based biomarkers for stage I RCC in Caucasian population from The Cancer Genome Atlas (TCGA) database.

Methods: Patients’ clinical, methylation beta-value, and mRNA expression data were retrieved. Differential methylation and expression analysis were conducted to obtain differentially methylated CpG-gene pairs. Inversely correlated CpG-gene pairs between their expression and methylation levels were selected using Pearson’s correlation test and then screened for any recorded somatic mutations. Their biomarker capacities were analyzed using the Kaplan-Meier and receiver operating characteristic analysis, followed by protein network and functional enrichment analysis.

Results: We obtained differentially methylated CpGs in clear cell (KIRC) and papillary RCC (KIRP) but not chromophobe RCC (KICH). Six inversely correlated CpG-gene pairs with no reported cancer-associated mutations were selected. Prognostic values were found in ATXN1 and RFTN1 for KIRC, along with GRAMD1B and TM4SF19 for KIRP, while diagnostic values were found in VIM and RFTN1 for KIRC, along with TNFAIP6 and TM4SF19 for KIRP. Both subtypes showed enrichment of immune and metabolism-related pathways.

Conclusion: We discovered novel potential DNA methylation-based prognostic and diagnostic markers for early-stage RCC in Caucasian population. Validation by wet laboratory analysis and adjustments for confounding variables might be needed, considering our study limitation to specific race.

Background: A novel data-driven Boolean model, namely, the fundamental Boolean model (FBM), has been proposed to draw genetic regulatory insights into gene activation, inhibition, and protein decay, published in 2018. This novel Boolean model facilitates the analysis of the activation and inhibition pathways. However, the novel model does not handle the situation well, where genetic regulation might require more time steps to complete.

Methods: Here, we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others. We denoted this extension model as the temporal fundamental Boolean model (TFBM) and related networks as the temporal fundamental Boolean networks (TFBNs). The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.

Results: We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets, which are in the form of short-time series. The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia, and each sample has three time points: 0 hour (before GC treatment), 6/8 hours (after GC treatment) and 24 hours (after GC treatment).

Conclusion: We conclude that the proposed TFBM unlocks their predecessor’s limitation, i.e., FBM, that could help pharmaceutical agents identify any side effects on clinic-related data. New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.