Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans owing to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, we reveal a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (∼0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes owing to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potential for phenotypic innovation in young genes compared to older genes, a process under natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

Identification of an immune-related gene prognostic index for predicting prognosis, immunotherapeutic efficacy, and candidate drugs in amyotrophic lateral sclerosis

Rationale and objectives

Considering the great insufficiency in the survival prediction and therapy of amyotrophic lateral sclerosis (ALS), it is fundamental to determine an accurate survival prediction for both the clinical practices and the design of treatment trials. Therefore, there is a need for more accurate biomarkers that can be used to identify the subtype of ALS which carries a high risk of progression to guide further treatment.

Methods

The transcriptome profiles and clinical parameters of a total of 561 ALS patients in this study were analyzed retrospectively by analysis of four public microarray datasets. Based on the results from a series of analyses using bioinformatics and machine learning, immune signatures are able to be used to predict overall survival (OS) and immunotherapeutic response in ALS patients. Apart from other comprehensive analyses, the decision tree and the nomogram, based on the immune signatures, were applied to guide individual risk stratification. In addition, molecular docking methodology was employed to screen potential small molecular to which the immune signatures might response.

Results

Immune was determined as a major risk factor contributing to OS among various biomarkers of ALS patients. As compared with traditional clinical features, the immune-related gene prognostic index (IRGPI) had a significantly higher capacity for survival prediction. The determination of risk stratification and assessment was optimized by integrating the decision tree and the nomogram. Moreover, the IRGPI may be used to guide preventative immunotherapy for patients at high risks for mortality. The administration of 2MIU IL2 injection in the short-term was likely to be beneficial for the prolongment of survival time, whose dosage should be reduced to 1MIU if the long-term therapy was required. Besides, a useful clinical application for the IRGPI was to screen potential compounds by the structure-based molecular docking methodology.

Conclusion

Ultimately, the immune-derived signatures in ALS patients were favorable biomarkers for the prediction of survival probabilities and immunotherapeutic responses, and the promotion of drug development.

Opportunities and challenges for the use of common controls in sequencing studies

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Investigating the biochemical effects of heat stress and sample quenching approach on the metabolic profiling of abalone (Haliotis iris)

INTRODUCTION

Ocean temperatures have been consistently increasing due to climate change, and the frequency of heatwave events on shellfish quality is a growing concern worldwide. Typically, shellfish growing areas are in remote or difficult to access locations which makes in-field sampling and sample preservation of shellfish heat stress difficult. As such, there is a need to investigate in-field sampling approaches that facilitate the study of heat stress in shellfish.

OBJECTIVES

This study aims to apply a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach to examine molecular mechanisms of heat stress responses in shellfish using abalone as a model, and compare the effects of different quenching protocols on abalone metabolic profiles.

METHODS

Twenty adult Haliotis iris abalone were exposed to two temperatures (14 °C and 24 °C) for 24 h. Then, haemolymph and muscle tissues of each animal were sampled and quenched with 4 different protocols (liquid nitrogen, dry ice, cold methanol solution and normal ice) which were analyzed via GC-MS for central carbon metabolites.

RESULTS

The effects of different quenching protocols were only observed in muscle tissues in which the cold methanol solution and normal ice caused some changes in the observed metabolic profiles, compared to dry ice and liquid nitrogen. Abalone muscle tissues were less affected by thermal stress than haemolymph. There were 10 and 46 compounds significantly influenced by thermal stress in muscle and haemolymph, respectively. The changes of these metabolite signatures indicate oxidative damage, disturbance of amino acid and fatty acid metabolism, and a shift from aerobic metabolism to anaerobic pathways.

CONCLUSIONS

The study provided insights into the heat response of abalone, which could be useful for understanding the effects of marine heatwaves and summer mortality events on abalone. Dry ice appeared to be a suitable protocol, and safer in-field alternative to liquid nitrogen, for quenching of abalone tissues.