The formation water of a deep aquifer (853 m of depth) used for geological storage of natural gas was sampled to assess the mono-aromatic hydrocarbons attenuation potential of the indigenous microbiota. The study of bacterial diversity suggests that Firmicutes and, in particular, sulphate-reducing bacteria (Peptococcaceae) predominate in this microbial community. The capacity of the microbial community to biodegrade toluene and m- and p-xylenes was demonstrated using a culture-based approach after several hundred days of incubation. In order to reveal the potential for biodegradation of these compounds within a shorter time frame, an innovative approach named the solution hybrid selection method, which combines sequence capture by hybridization and next-generation sequencing, was applied to the same original water sample. The bssA and bssA-like genes were investigated as they are considered good biomarkers for the potential of toluene and xylene biodegradation. Unlike a PCR approach which failed to detect these genes directly from formation water, this innovative strategy demonstrated the presence of the bssA and bssA-like genes in this oligotrophic ecosystem, probably harboured by Peptococcaceae. The sequence capture by hybridization shows significant potential to reveal the presence of genes of functional interest which have low-level representation in the biosphere.
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http://dx.doi.org/10.1111/1751-7915.12426 | DOI Listing |
Nat Commun
December 2024
School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results.
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December 2024
Oncology Bioinformatics, Genentech, South San Francisco, CA, USA.
Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins.
View Article and Find Full Text PDFNat Commun
December 2024
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Hodgkin Reed-Sternberg (HRS) cells of classic Hodgkin lymphoma (cHL), like many solid tumors, elicit ineffective immune responses. However, patients with cHL are highly responsive to PD-1 blockade, which largely depends on HRS cell-specific retention of MHC class II and implicates CD4 T cells and additional MHC class I-independent immune effectors. Here, we utilize single-cell RNA sequencing and spatial analysis to define shared circulating and microenvironmental features of the immune response to PD-1 blockade in cHL.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Innovation Center for Diagnostics and Treatment of Thalassemia, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Despite the well-documented mutation spectra of β-thalassemia, the genetic variants and haplotypes of globin gene clusters modulating its clinical heterogeneity remain incompletely illustrated. Here, a targeted long-read sequencing (T-LRS) is demonstrated to capture 20 genes/loci in 1,020 β-thalassemia patients. This panel permits not only identification of thalassemia mutations at 100% of sensitivity and specificity, but also detection of rare structural variants (SVs) and single nucleotide variants (SNVs) in modifier genes/loci.
View Article and Find Full Text PDFJ Phys Chem Lett
December 2024
Macao Institute of Materials Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China.
The powerful data processing and pattern recognition capabilities of machine learning (ML) technology have provided technical support for the innovation in computational chemistry. Compared with traditional ML and deep learning (DL) techniques, transformers possess fine-grained feature-capturing abilities, which are able to efficiently and accurately model the dependencies of long-sequence data, simulate complex and diverse chemical spaces, and explore the computational logic behind the data. In this Perspective, we provide an overview of the application of transformer models in computational chemistry.
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