Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.

MOTIVATION: Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. RESULTS: To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study. AVAILABILITY AND IMPLEMENTATION: Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.

Dynamics of rhizosphere microbial structure and function associated with the biennial bearing of moso bamboo.

Moso bamboo (Phyllostachys edulis) is a valuable nontimber forestry product with a biennial cycle, producing abundant bamboo shoots within one year (on-year) and few shoots within the following year (off-year). Moso bamboo plants undergo clonal reproduction, resulting in similar genetic backgrounds. However, the number of moso bamboo shoots produced each year varies. Despite this variation, the impact of soil nutrients and the root microbiome on the biennial bearing of moso bamboo is poorly understood. We collected 139 soil samples and determined 14 major physicochemical properties of the rhizosphere, rhizoplane, and bulk soil in different seasons (i.e., the growing and deciduous seasons) and different years (i.e., on- and off-years). Based on 16S rRNA and metagenomic sequencing, major variations were found in the rhizospheric microbial composition during different seasons and years in the moso bamboo forest. Environmental driver analysis revealed that essential nutrients (i.e., SOC, TOC, TN, P, and NH4+) were the main drivers of the soil microbial community composition and were correlated with the on- and off-year cycles. Moreover, 19 MAGs were identified as important biomarkers that could distinguish on- and off-years. We found that both season and year influenced both the microbial community structure and functional pathways through the biosynthesis of nutrients that potentially interact with the moso bamboo growth rhythm, especially the on-year root-associated microbiome, which had a greater abundance of specific nutrients such as gibberellins and vitamin B6. This work provides a dynamic perspective of the differential responses of various on- and off-year microbial communities and enhances our understanding of bamboo soil microbiome biodiversity and stability.

Identification of a Novel Chitinase from Bacillus paralicheniformis: Gene Mining, Sequence Analysis, and Enzymatic Characterization.

In this study, a novel strain for degrading chitin was identified as Bacillus paralicheniformis HL37, and the key chitinase CH1 was firstly mined through recombinant expression in Bacillus amyloliquefaciens HZ12. Subsequently, the sequence composition and catalytic mechanism of CH1 protein were analyzed. The molecular docking indicated that the triplet of Asp526, Asp528, and Glu530 was a catalytic active center. The enzymatic properties analysis revealed that the optimal reaction temperature and pH was 65 °C and 6.0, respectively. Especially, the chitinase activity showed no significant change below 55 °C and it could maintain over 60% activity after exposure to 85 °C for 30 min. Moreover, the optimal host strain and signal peptide were obtained to enhance the expression of chitinase CH1 significantly. As far as we know, it was the first time finding the highly efficient chitin-degrading enzymes in B. paralicheniformis, and detailed explanations were provided on the catalytic mechanism and enzymatic properties on CH1.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles.

BACKGROUND: Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. METHODS: To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements. RESULTS: Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. CONCLUSIONS: Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy. KEYPOINTS: DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.