Neurogenomic Signatures of Successes and Failures in Life-History Transitions in a Key Insect Pollinator

Life-history transitions require major reprogramming at the behavioral and physiological level. Mating and reproductive maturation are known to trigger changes in gene transcription in reproductive tissues in a wide range of organisms, but we understand little about the molecular consequences of a failure to mate or become reproductively mature, and it is not clear to what extent these processes trigger neural as well as physiological changes. In this study, we examined the molecular processes underpinning the behavioral changes that accompany the major life-history transitions in a key pollinator, the bumblebee Bombus terrestris. We compared neuro-transcription in queens that succeeded or failed in switching from virgin and immature states, to mated and reproductively mature states. Both successes and failures were associated with distinct molecular profiles, illustrating how development during adulthood triggers distinct molecular profiles within a single caste of a eusocial insect. Failures in both mating and reproductive maturation were explained by a general up-regulation of brain gene transcription. We identified 21 genes that were highly connected in a gene coexpression network analysis: nine genes are involved in neural processes and four are regulators of gene expression. This suggests that negotiating life-history transitions involves significant neural processing and reprogramming, and not just changes in physiology. These findings provide novel insights into basic life-history transitions of an insect. Failure to mate or to become reproductively mature is an overlooked component of variation in natural systems, despite its prevalence in many sexually reproducing organisms, and deserves deeper investigation in the future

Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services

Acidithiobacillus ferrooxidans metabolism: from genome sequence to industrial applications

<p>Abstract</p> <p>Background</p> <p><it>Acidithiobacillus ferrooxidans </it>is a major participant in consortia of microorganisms used for the industrial recovery of copper (bioleaching or biomining). It is a chemolithoautrophic, γ-proteobacterium using energy from the oxidation of iron- and sulfur-containing minerals for growth. It thrives at extremely low pH (pH 1–2) and fixes both carbon and nitrogen from the atmosphere. It solubilizes copper and other metals from rocks and plays an important role in nutrient and metal biogeochemical cycling in acid environments. The lack of a well-developed system for genetic manipulation has prevented thorough exploration of its physiology. Also, confusion has been caused by prior metabolic models constructed based upon the examination of multiple, and sometimes distantly related, strains of the microorganism.</p> <p>Results</p> <p>The genome of the type strain <it>A. ferrooxidans </it>ATCC 23270 was sequenced and annotated to identify general features and provide a framework for <it>in silico </it>metabolic reconstruction. Earlier models of iron and sulfur oxidation, biofilm formation, quorum sensing, inorganic ion uptake, and amino acid metabolism are confirmed and extended. Initial models are presented for central carbon metabolism, anaerobic metabolism (including sulfur reduction, hydrogen metabolism and nitrogen fixation), stress responses, DNA repair, and metal and toxic compound fluxes.</p> <p>Conclusion</p> <p>Bioinformatics analysis provides a valuable platform for gene discovery and functional prediction that helps explain the activity of <it>A. ferrooxidans </it>in industrial bioleaching and its role as a primary producer in acidic environments. An analysis of the genome of the type strain provides a coherent view of its gene content and metabolic potential.</p