Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

SARS-CoV-2 Papain-like Protease Negatively Regulates the NLRP3 Inflammasome Pathway and Pyroptosis by Reducing the Oligomerization and Ubiquitination of ASC.

The interaction of viruses with hosts is complex, especially so with the antiviral immune systems of hosts, and the underlying mechanisms remain perplexing. Infection with SARS-CoV-2 may result in cytokine syndrome in the later stages, reflecting the activation of the antiviral immune response. However, viruses also encode molecules to negatively regulate the antiviral immune systems of hosts to achieve immune evasion and benefit viral replication during the early stage of infection. It has been observed that the papain-like protease (PLP) encoded by coronavirus could negatively regulate the host's IFNß innate immunity. In this study, we first found that eight inflammasome-related genes were downregulated in CD14+ monocytes from COVID-19 patients. Subsequently, we observed that SARS-CoV-2 PLP negatively regulated the NLRP3 inflammasome pathway, inhibited the secretion of IL-1ß, and decreased the caspase-1-mediated pyroptosis of human monocytes. The mechanisms for this may arise because PLP coimmunoprecipitates with ASC, reduces ASC ubiquitination, and inhibits ASC oligomerization and the formation of ASC specks. These findings suggest that PLP may inhibit strong immune defenses and provide the maximum advantage for viral replication. This research may allow us to better understand the flex function of CoV-encoding proteases and provide a new perspective on the innate immune responses against SARS-CoV-2 and other viruses.

Insights into the Evolution and Host Adaptation of the Monkeypox Virus from a Codon Usage Perspective: Focus on the Ongoing 2022 Outbreak.

The exceptionally widespread outbreak of human monkeypox, an emerging zoonosis caused by the monkeypox virus (MPXV), with more than 69,000 confirmed cases in 100 non-endemic countries since 2022, is a major public health concern. Codon usage patterns reflect genetic variation and adaptation to new hosts and ecological niches. However, detailed analyses of codon usage bias in MPXV based on large-scale genomic data, especially for strains responsible for the 2022 outbreak, are lacking. In this study, we analyzed codon usage in MPXV and its relationship with host adaptation. We confirmed the ongoing outbreak of MPXVs belonging to the West Africa (WA) lineage by principal component analysis based on their codon usage patterns. The 2022 outbreak strains had a relatively low codon usage bias. Codon usage of MPXVs was shaped by mutation and natural selection; however, different from past strains, codon usage in the 2022 outbreak strains was predominantly determined by mutation pressure. Additionally, as revealed by the codon adaptation index (CAI), relative codon deoptimization index (RCDI), and similarity index (SiD) analyses, the codon usage patterns of MPXVs were also affected by their hosts. In particular, the 2022 outbreak strains showed slightly but significantly greater adaptation to many primates, including humans, and were subjected to stronger selection pressure induced by hosts. Our results suggest that MPXVs contributing to the 2022 outbreak have unique evolutionary features, emphasizing the importance of sustained monitoring of their transmission and evolution.

Cross-Modality Person Re-Identification via Local Paired Graph Attention Network.

Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a graph to learn the relevance of pedestrian images of distinct modalities to narrow the gap between IR modality and RGB modality, but they omit the correlation between IR image and RGB image pairs. In this paper, we propose a novel graph model called Local Paired Graph Attention Network (LPGAT). It uses the paired local features of pedestrian images from different modalities to build the nodes of the graph. For accurate propagation of information among the nodes of the graph, we propose a contextual attention coefficient that leverages distance information to regulate the process of updating the nodes of the graph. Furthermore, we put forward Cross-Center Contrastive Learning (C3L) to constrain how far local features are from their heterogeneous centers, which is beneficial for learning the completed distance metric. We conduct experiments on the RegDB and SYSU-MM01 datasets to validate the feasibility of the proposed approach.

A Standardized Framework for Better Understanding of Phenotypic Differences within Bacterial Phyla Based on Protein Domain.

We propose a standardized framework to classify target species based on their protein domains, which can be utilized in different contexts, like eukaryotes and prokaryotes. In this study, by applying the framework to the bacterial kingdom as an implementation example and comparing the results with the current taxonomy standards at the phylum level, we came to the conclusion that the sequence of domains rather than the content of domains in a protein and the presence of one domain rather than the number of occurrences of one domain play more important roles in deciding bacterial phenotypes as well as matching the current taxonomy. In addition, the comparison also helps us to better focus on the species that conflict with the current phylum category, as well as to further investigate their phenotypic or genotypic differences. IMPORTANCE A 3-step framework was designed which can be applied to clustering species based on their protein domains, and different candidate models are proposed in each step for better adaptation of various scenarios. We show its implementation for the bacterial kingdom as an example, which helps us to find the most appropriate model combination that will best reflect the relationship between domains and phenotypes in this context. In addition, identifying species that are distant in the results but should be closely related phylogenetically can help us to focus on the mismatch for better understanding of their key phenotypic or genotypic differences.

Metagenomic analysis of the microbiome of the upper reproductive tract: combating ovarian cancer through predictive, preventive, and personalized medicine.

Purpose: We investigated whether ovarian cancer could alter the genital microbiota in a specific way with clinical values. Furthermore, we proposed how such changes could be envisioned in a paradigm of predictive, preventive, and personalized medicine (PPPM). Methods: The samples were collected using cotton swabs from the cervical, uterine cavity, fallopian tubes, and ovaries of patients subjected to the surgical procedures for the malignant/benign lesions. All samples were then analyzed by metagenomic shotgun sequencing. The distribution patterns and characteristics of the microbiota in the reproductive tract of subjects were analyzed and were interpreted in relation to the clinical outcomes of the subjects. Results: While the ovarian cancer was able to alter the genital microbiota, the bacteria were the dominant microorganisms in all samples across all cohorts in the study (median 99%). The microbiota of the upper female reproductive tract were mainly from the cervical, identified by low bacterial biomass and high bacterial diversity. Ovarian cancer had a distinct microbiota signature. The tubal ligation affects its microbial distribution. There were no different species on the surface of platinum-sensitive ovarian tissues compared to samples from platinum-resistant patients. Conclusion: The ovarian cancer-induced changes in microbiota magnify the potential of microbiota as a biotherapeutic modality in the treatment of ovarian cancer in this study and very likely for several malignancies and other conditions. Our findings demonstrated, for the first time, that microbiota could be dissected and applied in more specific fashion based on a predictive, preventive, and personalized medicine (PPPM) model in the treatment of ovarian cancer. Utilizing microbiota portfolio in a PPPM system in ovarian cancer would provide a unique opportunity to a clinically intelligent and novel approach in the treatment of ovarian cancer as well as several other conditions and malignancies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00286-1.

A comprehensive analysis of Usutu virus (USUV) genomes revealed lineage-specific codon usage patterns and host adaptations.

The Usutu virus (USUV) is an emerging arbovirus virus maintained in the environment of Afro-Eurasia via a bird-mosquito-bird enzootic cycle and sporadically infected other vertebrates. Despite primarily asymptomatic or mild symptoms, humans infected by USUV can develop severe neurological diseases such as meningoencephalitis. However, no detailed study has yet been conducted to investigate its evolution from the perspective of codon usage patterns. Codon usage choice of viruses reflects the genetic variations that enable them to reconcile their viability and fitness toward the external environment and new hosts. This study performed a comprehensive evolution and codon usage analysis of USUVs. Our reconstructed phylogenetic tree confirmed that the circulation viruses belong to eight distinct lineages, reaffirmed by principal component analysis based on codon usage patterns. We also found a relatively small codon usage bias and that natural selection, mutation pressure, dinucleotide abundance, and evolutionary processes collectively shaped the codon usage of the USUV, with natural selection predominating over the others. Additionally, a complex interaction of codon usage between the USUV and its host was observed. This process could have enabled USUV to adapt to various hosts and vectors, including humans. Therefore, the USUV may possess a potential risk of cross-species transmission and subsequent outbreaks. In this respect, further epidemiologic surveys, diversity monitoring, and pathogenetic research are warranted.

Single-Cell Transcriptomic Profiling of MAIT Cells in Patients With COVID-19.

Background: Mucosal-associated invariant T (MAIT) cells are considered to participate of the host immune response against acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection; however, single-cell transcriptomic profiling of MAIT cells in patients with COVID-19 remains unexplored. Methods: We performed single-cell RNA sequencing analyses on peripheral MAIT cells from 13 patients with COVID-19 and 5 healthy donors. The transcriptional profiles of MAIT cells, together with assembled T-cell receptor sequences, were analyzed. Flow cytometry analysis was also performed to investigate the properties of MAIT cells. Results: We identified that differentially expressed genes (DEGs) of MAIT cells were involved in myeloid leukocyte activation and lymphocyte activation in patients with COVID-19. In addition, in MAIT cells from severe cases, more DEGs were enriched in adaptive cellular and humoral immune responses compared with those in moderate cases. Further analysis indicated that the increase of cell cytotoxicity (killing), chemotaxis, and apoptosis levels in MAIT cells were consistent with disease severity and displayed the highest levels in patients with severe disease. Interestingly, flow cytometry analysis showed that the frequencies of pyroptotic MAIT cells, but not the frequencies of apoptotic MAIT cells, were increased significantly in patients with COVID-19, suggesting pyroptosis is one of leading causes of MAIT cell deaths during SARS-CoV-2 infection. Importantly, there were more clonal expansions of MAIT cells in severe cases than in moderate cases. Conclusions: The results of the present study suggest that MAIT cells are likely to be involved in the host immune response against SARS-CoV-2 infection. Simultaneously, the transcriptomic data from MAIT cells provides a deeper understanding of the immune pathogenesis of the disease.

Characterization of Burkholderia cepacia Complex Core Genome and the Underlying Recombination and Positive Selection.

Recombination and positive selection are two key factors that play a vital role in pathogenic microorganisms' population adaptation and diversification. The Burkholderia cepacia complex (Bcc) represents bacterial species with high similarity, which can cause severe infections among cases suffering from the chronic granulomatous disorder and cystic fibrosis (CF). At present, no genome-wide study has been carried out focusing on investigating the core genome of Bcc associated with the two evolutionary forces. The general characteristics of the core genome of Bcc species remain scarce as well. In this study, we explored the core orthologous genes of 116 Bcc strains using comparative genomic analysis and studied the two adaptive evolutionary forces: recombination and positive selection. We estimated 1005 orthogroups consisting entirely of single copy genes. These single copy orthologous genes in some Cluster of Orthologous Groups (COG) categories showed significant differences in the comparison of several evolutionary properties, and the encoding proteins were relatively simple and compact. Our findings showed that 5.8% of the core orthologous genes strongly supported recombination; in the meantime, 1.1% supported positive selection. We found that genes involved in protein synthesis as well as material transport and metabolism are favored by selection pressure. More importantly, homologous recombination contributed more genetic variation to a large number of genes and largely maintained the genetic cohesion in Bcc. This high level of recombination between Bcc species blurs their taxonomic boundaries, which leads Bcc species to be difficult or impossible to distinguish phenotypically and genotypically.

Genome-based classification of Burkholderia cepacia complex provides new insight into its taxonomic status.

BACKGROUND: Accurate classification of different Burkholderia cepacia complex (BCC) species is essential for therapy, prognosis assessment and research. The taxonomic status of BCC remains problematic and an improved knowledge about the classification of BCC is in particular needed. METHODS: We compared phylogenetic trees of BCC based on 16S rRNA, recA, hisA and MLSA (multilocus sequence analysis). Using the available whole genome sequences of BCC, we inferred a species tree based on estimated single-copy orthologous genes and demarcated species of BCC using dDDH/ANI clustering. RESULTS: We showed that 16S rRNA, recA, hisA and MLSA have limited resolutions in the taxonomic study of closely related bacteria such as BCC. Our estimated species tree and dDDH/ANI clustering clearly separated 116 BCC strains into 36 clusters. With the appropriate reclassification of misidentified strains, these clusters corresponded to 22 known species as well as 14 putative novel species. CONCLUSIONS: This is the first large-scale and systematic study of the taxonomic status of the BCC and could contribute to further insights into BCC taxonomy. Our study suggested that conjunctive use of core phylogeny based on single-copy orthologous genes, as well as pangenome-based dDDH/ANI clustering would provide a preferable framework for demarcating closely related species. REVIEWER: This article was reviewed by Dr. Xianwen Ren.