Compendium of causative genes and their encoded proteins for common monogenic disorders

Alzheimer's disease as an auto-innate immune pathology with potential cell trans-differentiation and enhanced trained immunity in 3xTg-AD mouse model

BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder characterized by memory impairment. Neuroinflammatory processes, mediated by glial and immune cells, contribute to neuronal damage. Emerging evidence implicates innate immune mechanisms, including trained immunity and cell trans-differentiation, in AD pathogenesis, though their roles remain unclear.ObjectiveTo investigate transcriptomic changes in the 3xTg-AD mouse model, focusing on trained immunity and cell trans-differentiation in disease mechanisms.MethodsRNA-sequencing was performed on brain tissue (cortex plus hippocampus) from 11-month-old female 3xTg-AD and wild-type mice (n = 3/group). Differentially expressed genes (fold change > 1.5, p < 0.05) were identified and followed by bioinformatics and knowledge-based transcriptomic profiling. Public AD datasets were also analyzed.Results3xTg-AD mice exhibited 316 upregulated and 412 downregulated genes. Downregulated genes included those for blood-brain barrier protein, while upregulated genes related to cerebrospinal fluid. Increased expression of proinflammatory markers, as well as genes related to cell differentiation, proliferation, activation, and adhesion. Upregulation of genes associated with cell migration and trans-differentiation suggests a potential role for inflammation and cellular plasticity. Additionally, genes involved in inflammasome pathways, immunometabolism, and trained immunity were upregulated. Mechanistically, these genes were modulated by knockdown of trained immunity promoter SET-7, overexpression of trained immunity inhibitor IL-37, and knockout of inflammasome genes IL-1 receptor, caspase-1, and pattern recognition receptor CD36.ConclusionsThe finding underscore the potential role of trained immunity and cell trans-differentiation in AD, revealing a mechanistic framework in which danger-associated molecular patterns drive innate immune responses, inflammasome activation, and cell plasticity contribute to AD, offering therapeutic targets for neuroinflammation and cellular reprograming.

Model-guided design of microRNA-based gene circuits supports precise dosage of transgenic cargoes into diverse primary cells

In a therapeutic context, supraphysiological expression of transgenes can compromise engineered phenotypes and lead to toxicity. To ensure a narrow range of transgene expression, we developed a single-transcript, microRNA-based incoherent feedforward loop called compact microRNA-mediated attenuator of noise and dosage (ComMAND). We experimentally tuned the ComMAND output profile, and we modeled the system to explore additional tuning strategies. By comparing ComMAND to two-gene implementations, we demonstrate the precise control afforded by the single-transcript architecture, particularly at low copy numbers. We show that ComMAND tightly regulates transgene expression from lentiviruses and precisely controls expression in primary human T cells, primary rat neurons, primary mouse embryonic fibroblasts, and human induced pluripotent stem cells. Finally, ComMAND effectively sets levels of the clinically relevant transgenes frataxin (FXN) and fragile X messenger ribonucleoprotein 1 (Fmr1) within a narrow window. Overall, ComMAND is a compact tool well suited to precisely specify the expression of therapeutic cargoes. A record of this paper's transparent peer review process is included in the supplemental information.

The triple code model for advancing research in rare and undiagnosed diseases beyond the base pairs

Rare and undiagnosed diseases pose significant challenges for understanding their mechanisms, diagnosis, and treatment. The Triple Code Model, an integrative paradigm described here, considers the combined influence of the genetic code, epigenetic code, and nuclear structure (an emerging code), as fundamental biochemical mechanisms underlying many rare diseases. Studies demonstrate dysfunctional membrane and cytoplasmic signals instruct the epigenome to ultimately impact the 3D structure and dynamics of the nucleus, highlighting their close interrelationships. Consequently, this model offers a holistic perspective on rare and undiagnosed diseases by moving beyond a solely genetic view. We propose that this integrated framework will efficiently guide rare disease research by taking it 'Beyond the Base Pairs,' leading to improved diagnostics and personalized treatments.

Barriers in Translating Existing Nutrigenetics Insights to Precision Nutrition for Cardiometabolic Health in Ethnically Diverse Populations

BACKGROUND

Cardiometabolic diseases pose a significant threat to global public health, with a substantial majority of cardiovascular disease mortality (more than three-quarters) occurring in low- and middle-income countries. There have been remarkable advances in recent years in identifying genetic variants that alter disease susceptibility by interacting with dietary factors. Despite the remarkable progress, several factors need to be considered before the translation of nutrigenetics insights to personalised and precision nutrition in ethnically diverse populations. Some of these factors include variations in genetic predispositions, cultural and lifestyle factors as well as socio-economic factors.

SUMMARY

This review aimed to explore the factors that need to be considered in bridging the gap between existing nutrigenetics insights and the implementation of personalised and precision nutrition across diverse ethnicities. Several factors might influence variations among individuals with regard to dietary exposures and metabolic responses, and these include genetic diversity, cultural and lifestyle factors as well as socio-economic factors. A multi-omics approach involving disciplines such as metabolomics, epigenetics, and the gut microbiome might contribute to improved understanding of the underlying mechanisms of gene-diet interactions and the implementation of precision nutrition although more research is needed to confirm the practicality and effectiveness of this approach. Conducting gene-diet interaction studies in diverse populations is essential and studies utilising large sample sizes are required as this improves the power to detect interactions with minimal effect sizes. Future studies should focus on replicating initial findings to enhance reliability and promote comparison across studies. Once findings have been replicated in independent samples, dietary intervention studies will be required to further strengthen the evidence and facilitate their application in clinical practice.

KEY MESSAGES

Nutrigenetics has a potential role to play in the prevention and management of cardiometabolic diseases. Conducting gene-diet interaction studies in diverse populations is essential giving the genetic diversity and variations in dietary patterns. Integrating data from disciplines such as metabolomics, epigenetics, and the gut microbiome could help in early identification of individuals at risk of cardiometabolic diseases as well as the implementation of precise dietary interventions for preventing and managing cardiometabolic diseases.

Model-guided design of microRNA-based gene circuits supports precise dosage of transgenic cargoes into diverse primary cells

To realize the potential of engineered cells in therapeutic applications, transgenes must be expressed within the window of therapeutic efficacy. Differences in copy number and other sources of extrinsic noise generate variance in transgene expression and limit the performance of synthetic gene circuits. In a therapeutic context, supraphysiological expression of transgenes can compromise engineered phenotypes and lead to toxicity. To ensure a narrow range of transgene expression, we design and characterize Compact microRNA-Mediated Attenuator of Noise and Dosage (ComMAND), a single-transcript, microRNA-based incoherent feedforward loop. We experimentally tune the ComMAND output profile, and we model the system to explore additional tuning strategies. By comparing ComMAND to two-gene implementations, we highlight the precise control afforded by the single-transcript architecture, particularly at relatively low copy numbers. We show that ComMAND tightly regulates transgene expression from lentiviruses and precisely controls expression in primary human T cells, primary rat neurons, primary mouse embryonic fibroblasts, and human induced pluripotent stem cells. Finally, ComMAND effectively sets levels of the clinically relevant transgenes FMRP1 and FXN within a narrow window. Together, ComMAND is a compact tool well-suited to precisely specify expression of therapeutic cargoes.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

DNA and RNA base editors can correct the majority of pathogenic single nucleotide variants

The majority of human genetic diseases are caused by single nucleotide variants (SNVs) in the genome sequence. Excitingly, new genomic techniques known as base editing have opened efficient pathways to correct erroneous nucleotides. Due to reliance on deaminases, which have the capability to convert A to I(G) and C to U, the direct applicability of base editing might seem constrained in terms of the range of mutations that can be reverted. In this evaluation, we assess the potential of DNA and RNA base editing methods for treating human genetic diseases. Our findings indicate that 62% of pathogenic SNVs found within genes can be amended by base editing; 30% are G>A and T>C SNVs that can be corrected by DNA base editing, and most of them by RNA base editing as well, and 29% are C>T and A>G SNVs that can be corrected by DNA base editing directed to the complementary strand. For each, we also present several factors that affect applicability such as bystander and off-target occurrences. For cases where editing the mismatched nucleotide is not feasible, we introduce an approach that calculates the optimal substitution of the deleterious amino acid with a new amino acid, further expanding the scope of applicability. As personalized therapy is rapidly advancing, our demonstration that most SNVs can be treated by base editing is of high importance. The data provided will serve as a comprehensive resource for those seeking to design therapeutic base editors and study their potential in curing genetic diseases.

How T118M peripheral myelin protein 22 predisposes humans to Charcot-Marie-Tooth disease

Data from gnomAD indicate that a missense mutation encoding the T118M variation in human peripheral myelin protein 22 (PMP22) is found in roughly one of every 75 genomes of western European lineage (1:120 in the overall human population). It is unusual among PMP22 variants that cause Charcot-Marie-Tooth (CMT) disease in that it is not 100% penetrant. Here, we conducted cellular and biophysical studies to determine why T118M PMP22 predisposes humans to CMT, but with only incomplete penetrance. We found that T118M PMP22 is prone to mistraffic but differs even from the WT protein in that increased expression levels do not result in a reduction in trafficking efficiency. Moreover, the T118M mutant exhibits a reduced tendency to form large intracellular aggregates relative to other disease mutants and even WT PMP22. NMR spectroscopy revealed that the structure and dynamics of T118M PMP22 resembled those of WT. These results show that the main consequence of T118M PMP22 in WT/T118M heterozygous individuals is a reduction in surface-trafficked PMP22, unaccompanied by formation of toxic intracellular aggregates. This explains the incomplete disease penetrance and the mild neuropathy observed for WT/T118M CMT cases. We also analyzed BioVU, a biobank linked to deidentified electronic medical records, and found a statistically robust association of the T118M mutation with the occurrence of long and/or repeated episodes of carpal tunnel syndrome. Collectively, our results illuminate the cellular effects of the T118M PMP22 variation leading to CMT disease and indicate a second disorder for which it is a risk factor.

Amplicon sequencing-based carrier screening for 170 monogenic disorders among children with abnormal LC-MS/MS results

BACKGROUND

Next-generation sequencing (NGS) has been suggested as a second-tier diagnostic test for newborn screening, which could help identify the carrier status of hundreds monogenic disorders with wider spectrum and earlier stage.

METHODS

Among the 1087 children (age from 27 min to 14 years old) underwent liquid chromatography-tandem mass spectrometry (LC-MS/MS), 290 individuals who had at least one abnormal value of LC-MS/MS measurements were sent for amplicon sequencing-based carrier screening (targeting 141 genes for 170 monogenic disorders). Multiplex polymerase chain reaction was used for amplicon capture and library preparation, the NextSeq 500 NGS platform (Illumina PE150) was used for sequencing. The identified clinical significant variants were further validated by Sanger sequencing.

RESULTS

Only 89 children carry none of clinical significant variants, other 201 individuals carry 1-4 variants in 63 genes (132 types; 317 in total: 171 pathogenic, 37 likely pathogenic, 29 variants of unknown significance, and 80 disease-associated functional polymorphisms). Besides the three missing samples with 4 variants, 91.1 % of identified variants (285 variants in 54 genes) were completely validated by Sanger sequencing. The most common genetic variants were in UGT1A1, GJB2, PAH, G6PD, and SLC25A13 (top 5 genes), which corresponding to Gilbert/Crigler-Najjar symdrome (n = 89), autosomal recessive hearing loss type 1A (n = 58), phenylketonuria (n = 12), glucose-6-phosphate dehydrogenease deficiency (n = 11) and Citrin deficiency (n = 9). More than 42 children present higher phenylalanine in LC-MS/MS, but only 12 of them were identified to carry clinical significant variants in PAH gene.

CONCLUSION

The amplicon sequencing-based carrier screening in our study could further clarify the abnormal LC-MS/MS results, which could also discover more monogenic disorders uncovered by LC-MS/MS screening.

What Can RNA-Based Therapy Do for Monogenic Diseases?

The use of RNA-based approaches to treat monogenic diseases (i.e., hereditary disorders caused by mutations in single genes) has been developed on different fronts. One approach uses small antisense oligonucleotides (ASOs) to modulate RNA processing at various stages; namely, to enhance correct splicing, to stimulate exon skipping (to exclude premature termination codon variants), to avoid undesired messenger RNA (mRNA) transcript degradation via the nonsense-mediated decay (NMD) pathway, or to induce mRNA degradation where they encode toxic proteins (e.g., in dominant diseases). Another approach consists in administering mRNA, which, like gene therapy, is a mutation-agnostic approach with potential application to any recessive monogenic disease. This is simpler than gene therapy because instead of requiring targeting of the nucleus, the mRNA only needs to be delivered to the cytoplasm. Although very promising (as demonstrated by COVID-19 vaccines), these approaches still have potential for optimisation, namely regarding delivery efficiency, adverse drug reactions and toxicity.

Predicting the functional impact of KCNQ1 variants with artificial neural networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.