The identification of human-herpesvirus protein–protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

The dysregulation of human genes and proteins following SARS-CoV-2 infection significantly impacts the clinical symptoms and prognosis of COVID-19, particularly in immunocompromised individuals such as hematological tumor patients. Despite this, a comprehensive multi-omics understanding of human host immune responses remains incomplete. Here, we conducted a multi-omics analysis of 89 peripheral blood samples (RNA sequencing) and 98 serum samples (proteome mass spectrometry) from 52 COVID-19 patients, including hematological tumor patients and non-tumor individuals. By integrating transcriptomic, proteomic, and interactome data, we compared differentially expressed genes (DEGs) and proteins (DEPs) across infection stages and clinical outcomes to gain insights into the mechanisms of SARS-CoV-2 infection. Our analysis revealed distinct and overlapping transcriptomic and proteomic responses to SARS-CoV-2 infection. DEGs were predominantly associated with innate immune responses and viral processes, while DEPs were linked to actin cytoskeleton organization and protein kinase regulation. Notably, DEGs and DEPs often exhibited opposing regulatory patterns, suggesting post-transcriptional and post-translational mechanisms. Tumor patients showed more severe proteomic perturbations, with a higher proportion of DEPs functioning as key hub proteins in cellular networks. Network-based drug repositioning identified potential therapeutic targets, including HSPA8, SRC, STAT1, APOE, and APP. Clinical analysis indicated that long COVID patients experienced more severe coagulation abnormalities, immunosuppression, and myocardial injury, while acutely deceased patients exhibited abnormally activated immune responses. Our study provides a comprehensive resource for understanding the molecular mechanisms of SARS-CoV-2 infection in hematological tumor patients. By integrating multi-omics data, we highlight the importance of proteomic changes in disease progression and identify potential therapeutic targets for COVID-19 and long COVID.

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19. IMPORTANCE: A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

The microbiological diagnosis of infection for hematological malignancy patients receiving chemotherapy or allogeneic hematopoietic stem cell transplantation (allo-HSCT) patients relies primarily on standard microbial culture, especially blood culture, which has many shortcomings, such as having low positive rates, being time-consuming and having a limited pathogenic spectrum. In this prospective observational self-controlled test accuracy study, blood, cerebrospinal fluid (CSF), and bronchoalveolar lavage fluid (BALF) samples were collected from chemotherapy or allo-HSCT patients with clinical symptoms of infections who were hospitalized at Peking University First Hospital. Possible pathogens were detected by the method based on recombinant mannan-binding lectin (MBL) magnetic bead enrichment (M1 method) and simultaneously by a standard method. The analytical sensitivity of M1 method was close to that of standard culture method. Besides, the turn-around time of M1-method was significantly shorter than that of standard culture method. Moreover, the M1 method also added diagnostic value through the detection of some clinically relevant microbes missed by the standard method. M1 method could significantly increase the detection efficiency of pathogens (including bacteria and fungi) in immunocompromised patients. Key points: • The detection results of M1-method had a high coincidence rate with that of standard method • M1 method detected many pathogens which had not been found by standard clinic method

Hematologic malignancies cause significant morbidity/mortality in both children and young adults (CYAs) as well as older adults (OAs). Yet their biological underpinnings remain inadequately understood. Here, we analyzed clinical and genomic disparities between CYAs and OAs in various hematologic malignancies. We found substantial differences in clinical features such as patient sex, ethnicity, metastasis rates, and tumor subtypes. Genomically, most CYA hematologic malignancies indicated lower mutational burden. Subsequently, we identified differentially mutated genes (DMGs) with varying mutation rates between CYAs and OAs, noting fewer mutations in CYAs for most genes such as TP53, TET2, and DNMT3A. In contrast, several DMGs (i.e., NRAS, KRAS, SMARCA4, ID3, PTPN11, WT1, and KIT) were overrepresented in CYAs. We further investigated human protein interacting partners of these identified DMGs that were highly mutated in CYAs/OAs, respectively, and found significant differences in network topological and functional roles. Notably, CYA malignancies demonstrated extensive copy number alterations (CNAs) and more driver gene fusions. In particular, four CNA differential genes (i.e., ARID1B, MYB, TP53, and ESR1) were overrepresented as amplifications and deletions in CYAs and OAs, respectively. Ultimately, we demonstrated a landscape comparative view of clinically actionable genetic events in CYAs and OAs, providing clues for age-related personalized treatment.