Rubisco is the primary entry point for carbon into the biosphere. However, rubisco is widely regarded as inefficient leading many to question whether the enzyme can adapt to become a better catalyst. Through a phylogenetic investigation of the molecular and kinetic evolution of Form I rubisco we uncover the evolutionary trajectory of rubisco kinetic evolution in angiosperms. We show that is among the 1% of slowest-evolving genes and enzymes on Earth, accumulating one nucleotide substitution every 0.9 My and one amino acid mutation every 7.2 My. Despite this, rubisco catalysis has been continually evolving toward improved CO/O specificity, carboxylase turnover, and carboxylation efficiency. Consistent with this kinetic adaptation, increased rubisco evolution has led to a concomitant improvement in leaf-level CO assimilation. Thus, rubisco has been slowly but continually evolving toward improved catalytic efficiency and CO assimilation in plants.
This paper presents a comprehensive and updated overview of inborn errors of immunity (IEIs), focusing on the optimal treatment strategies. IEIs or primary immunodeficiencies (PIDs) are a heterogeneous group of approximately 500 disorders, classified into ten categories according to the affected component of the immune system. The clinical presentation varies, based on the type of the disorder and the patient's age.
The advent of genetic medicines and advanced diagnostics has revolutionized the treatment landscape for rare diseases and, with over 10,000 identified conditions affecting millions globally, has the potential to improve many lives. Despite this progress, only 5% of rare diseases have FDA-approved therapies, highlighting a significant unmet need. This article examines the critical need for optimizing the regulatory environment to support the development and approval of gene therapies for rare and ultrarare diseases, which often face unique challenges due to their complexity in the midst of a rapidly evolving field.
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas.
Omics techniques, such as proteomics, contain crucial data for understanding biological processes, but they remain underutilized due to their high dimensionality. Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. Despite this potential, machine learning has seen limited adoption in the field of proteomics.
Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture, Sino-Dutch Joint Laboratory of Horticultural Genomics, Beijing, China. Electronic address:
The Solanaceae family contains many agriculturally important crops, including tomato, potato, pepper, and tobacco, as well as others with growing potential, such as the orphan crops groundcherry, wolfberry, and pepino. Research progress varies greatly among these species, with model crops like tomato far ahead, which limits the broader agricultural application of other solanaceous species. Here, we constructed the interspecies pan-genome for the Solanaceae family and identified distinct patterns of gene retention.
