While cancer now impacts the health and well-being of more of the human population than ever before, the exponential rise in antimicrobial resistant (AMR) bacterial infections means AMR is predicted to become one of the greatest future threats to human health. It is therefore vital that novel therapeutic strategies are developed that can be used in the treatment of both cancer and AMR infections. Whether the target of a therapeutic agent be inside the cell or in the cell membrane, it must either interact with or cross this phospholipid barrier to elicit the desired cellular effect. Here we summarise findings from published research into the phospholipid membrane composition of bacterial and cancer cell lines and biological samples from cancer patients. These data not only highlight key differences in the membrane composition of these biological samples, but also the methods used to elucidate and report the results of this analogous research between the microbial and cancer fields.
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http://dx.doi.org/10.1039/d1sc03597e | DOI Listing |
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFAoB Plants
January 2025
Plant Evolutionary Ecology, Faculty of Biological Sciences, Goethe University Frankfurt, Max-von-Laue-Str. 13, 60438 Frankfurt am Main, Germany.
Local adaptation is a common phenomenon that helps plant populations to adjust to broad-scale environmental heterogeneity. Given the strong effect of forest management on the understorey microenvironment and often long-term effects of forest management actions, it seems likely that understorey herbs may have locally adapted to the practiced management regime and induced environmental variation. We investigated the response of and to forest management using a transplant experiment along a silvicultural management intensity gradient.
View Article and Find Full Text PDFPeerJ
January 2025
Departamento de genética, ecologia e evolução, Laboratório de biologia integrativa, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Background: The angiotensin-converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) are central human molecules in the SARS-CoV-2 virus-host interaction. Evidence indicates that may influence expression. This study aims to determine whether ACE1, ACE2, and TMPRSS2 mRNA expression levels, along with the ACE1 Alu 287 bp polymorphism (rs4646994), contribute to the severity and mortality of COVID-19.
View Article and Find Full Text PDFAssessments of genetic diversity, structure, history, and effective population size ( ) are critical for the conservation of imperiled populations. The lesser prairie-chicken () has experienced declines due to habitat loss, degradation, and fragmentation in addition to substantial population fluctuations with unknown effects on genetic diversity. Our objectives were to: (i) compare genetic diversity across three temporally discrete sampling periods (2002, 2007-2010, and 2013-2014) that are characterized by low or high population abundance; (ii) examine genetic diversity at lek and lek cluster spatial scales; (ii) identify potential bottlenecks and characterize genetic structure and relatedness; and (iii) estimate the regional .
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.
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