For more than two decades, researchers have identified hundreds of genes that increase the risk of autism spectrum disorder (ASD). Yet multiple fundamental questions have remained unanswered: among them, how do mutations in these genes lead directly to changes in brain development and how can that knowledge be translated into more effective therapies?
In a landmark study published in Science, scientists have taken a major step toward answering both questions. The findings are the result of more than a decade of work. By building the largest-ever molecular interaction map of autism, the team revealed how hundreds of genes and dozens of mutations converge within a surprisingly small number of shared protein networks, hurdling a major roadblock to the development of new precision medicines.
Rather than focusing only on the genes linked to autism, the researchers mapped the proteins encoded by those genes and discovered exactly how individual disease-causing mutations can rewire the molecular machinery of the developing brain. The work uncovers an entirely new layer of disease biology that can be targeted therapeutically and provides a framework for designing medicines that directly address a wide range of underlying molecular causes of autism.
BACKGROUND: We aimed to characterize the genetic architecture of resistant hypertension (rHTN), which affects up to 18% of hypertensive individuals and increases cardiovascular disease risk. METHODS: We conducted a genome-wide association study on rHTN, defined as use of 3 or more concomitant antihypertensive drugs for at least 6 months without reaching blood pressure target (in the 3-drug case), comparing it to controlled hypertension (cHTN), in which persons on 1 or 2 antihypertensives for at least 6 months reach target BP after 30 days of therapy initiation. The study included 23 508 rHTN cases and 24 393 cHTN controls, identified through drug prescription and blood pressure data from Iceland (deCODE), the UK (UK Biobank), and the US (eMERGE).
People with fibromyalgia experience systemic muscular and skeletal pain, fatigue, and poor sleep. Fibromyalgia often coincides with other conditions including myalgic encephalomyelitis/chronic fatigue syndrome, psychiatric disorders, and some autoimmune diseases. Although researchers suspect that fibromyalgia is strongly influenced by genetics, so far, scientists have not pinpointed responsible genes, complicating research into the disease and treatment options.
As a result of the unclear disease mechanism of fibromyalgia, Kevin Hackshaw, a rheumatologist at the University of Texas at Austin, said that some physicians don’t take the condition seriously. “I would say greater than 50 percent of patients that eventually come to us for treatment are really individuals who have experienced rejection from other physicians along the way, believing that fibromyalgia is not real, is made up…and is kind of a wastebasket term, and it is quote unquote ‘all in your head,’” he said.
Recently, a study led by an international team of researchers tackled the complexity of gene associations in fibromyalgia with a meta-analysis of several genome-wise association studies encompassing 2.5 million people. In a study published in Nature Medicine, the researchers presented 26 loci associated with fibromyalgia risk.1 The researchers also found strong associations with genes involved in nervous system function and pain processing.
Among participants with EUR-like genomes, SNP heritability (h2SNP) estimated for the Big Five traits using linkage disequilibrium score regression (LDSC)34 ranged from 4.8% (s.e. = 0.2%) for agreeableness to 9.3% (s.e. = 0.3%) for extraversion (Table 1, Supplementary Table 4 and Supplementary Note 3). Importantly, these SNP heritability estimates from GWAS meta-analysis index genetic effects that are consistent across contributing cohorts. To allow for variability in genetic effects across cohorts, we conducted a random effects meta-analysis of cohort-specific h2SNP estimates, which indicated an average h2SNP of 8.6% (s.e. = 0.6%) across traits (ranging from 7.4% for agreeableness to 10.6% for extraversion; Table 1 and Supplementary Table 21), with significant variability across cohorts (mean τ = 3.6%). Random response error by the participants cannot systematically relate to their genome35,36. Accordingly, we found that personality measures with greater reliability (lower random response error) tended to be more heritable (b = 6.7%, s.e. = 0.7%; Extended Data Fig. 2). In this analysis, the expected h2SNP for a measure of typical (median) reliability (α = 0.81) ranged from 9.3% for agreeableness (s.e. = 0.6%) to 13.3% for extraversion (s.e. = 0.6%), and h2SNP completely disattenuated for measurement error ranged from 10.8% for agreeableness (s.e. = 0.9%) to 15.8% for extraversion (s.e. = 0.9%; Table 1).
To further characterize the generalizability of genetic associations with personality, we examined the concordance of genetic signal across geography, age, veteran status, measurement instrument and reporter perspective (Table 1 and Extended Data Fig. 3). Genetic effects were similar but not identical across four western country clusters (USA, continental Europe, Nordic and UK–Australia, mean rg = 0.86, mean s.e. = 0.15), three age groups (young (≤25 years), middle (25–64 years) and older (65 years and older), mean rg = 0.80, mean s.e. = 0.18), between the Million Veteran Program and other, primarily non-veteran cohorts (mean rg = 0.82, mean s.e. = 0.04), and across five personality measurement instruments (mean rg = 0.85, mean s.e. = 0.07). Additional characterization of genetic architecture across measurement instruments using genomic structural equation modelling37 confirmed that genetic effects plausibly operate at the level of broad cross-instrument latent factors, with only one locus showing significantly heterogenous effects across measurement instruments (Supplementary Tables 22 – 24 and Extended Data Fig. 4). Notably, genetic associations with agreeableness were less consistent across cohorts (Table 1), explaining in part why agreeableness exhibited lower heritability than other traits in the meta-analytic GWAS. In the Estonian Biobank, in which the personality of the participants was assessed both by their self-report (n = 73,983) and by reports by close others (n = 20,269), we found strong genetic overlap between rater perspectives (mean rg = 0.84, mean s.e. = 0.12), indicating that the genetic architecture of personality is not an epiphenomenon of self-perception. In sex-stratified analyses of neuroticism in the UK Biobank cohort, X-chromosome-linked h2SNP did not differ between male individuals (n = 168,989; h2SNP, X = 0.23%; s.e. = 0.04%) and female individuals (n = 198,139; h2SNP, X = 0.18%; s.e. = 0.03%; Pdifference = 0.33). The dosage compensation ratio (\(\hat{{m{\gamma }}}\) = 1.26, s.e. = 0.30) was intermediate between no compensation (0.5) and full compensation (2.0) but was estimated relatively imprecisely. Genetic effects were correlated near-unity across sex (rg = 0.96; 95% confidence interval (CI) = 0.81–1.10).
Biological follow-up of GWAS signals indicated that enriched gene sets intersected across the Big Five (mean enrichment rank-order ρ = 0.72; Extended Data Fig. 5), providing evidence for trait-overlapping molecular and cellular systems in personality neurobiology despite only modest genetic correlations (Fig. 1f). Consistent with theories of personality development that emphasize the prefrontal cortex38,39, genetic associations for each Big Five trait, except for agreeableness, were enriched in genes expressed in the prefrontal cortex (among these, top lead SNPs implicate RCE1, FOXP2 and SEMA6D, indicated in Fig. 1; Supplementary Tables 25–34). All traits demonstrated strong enrichment in protein-truncating variant-intolerant gene sets specifically expressed in neurons (such as ARNTL, TCF4 and NEGR1; Fig. 1).
All known life on earth utilizes the same genetic alphabet, consisting of four letters. Now, researchers at University of California San Diego have demonstrated that one of biology’s most essential enzymes can accurately read and transcribe an expanded, eight-letter genetic alphabet. The findings provide important evidence that cells can process synthetic genetic information using their natural molecular machinery, advancing a long-standing goal in synthetic biology to expand the language of DNA. It could also allow scientists to custom-engineer biological systems that perform functions or produce compounds not found in nature.
The study focused on RNA polymerase, the enzyme responsible for reading DNA and producing RNA — the first step in gene expression. Using biochemical experiments and high-resolution cryo-electron microscopy that can zoom down to smaller than the width of a single atom, researchers captured detailed structural snapshots showing how RNA polymerase from Escherichia coli ( E. coli ) bacteria recognizes and incorporates two synthetic base pairs, genetic letters that are not found in nature. These snapshots revealed that the enzyme recognizes synthetic DNA letters through the same biochemical and structural signals as natural base pairs, helping explain how expanded genetic information can be faithfully transcribed. In another related study, the same researchers reported that RNA polymerase can also recognize another pair of synthetic base pairs without hydrogen bonds to hold them together, published in PNAS.
This work has implications beyond basic biology. Previous studies have used expanded genetic alphabets to create synthetic DNA molecules capable of recognizing liver cancer cells. By revealing how RNA polymerase accurately reads and transcribes these non-natural DNA letters, the new study provides a molecular foundation for future technologies that use expanded genetic codes, including new diagnostics, therapeutics and engineered biological systems.
In a new study published in the prestigious Nature Communications journal, medical researchers used patient-derived brain cells grown in the lab, combined with advanced imaging and artificial intelligence, to rapidly test approved drugs and pinpoint those that improve brain cell health.
The research focused on Sanfilippo syndrome, a devastating childhood dementia that causes progressive loss of memory, behavior and physical abilities. There are currently no widely available treatments.
Sanfilippo syndrome is one of more than 100 genetic disorders that together affect 1 in 2900 children in Australia and half of all children with dementia die by the age of ten.
A research team from The Hong Kong University of Science and Technology (HKUST) has developed an innovative “one-pot” testing platform, known as TEMPO, that could transform highly sensitive nucleic acid testing from a laboratory-based procedure into a simple, single-step test that can be performed at home. With a single reaction tube, users can obtain results comparable to those of professional laboratory tests in as little as 30 minutes. The breakthrough has the potential to bridge the technological gap between existing rapid tests and laboratory-based nucleic acid diagnostics, offering a more convenient solution for future infectious disease surveillance and genetic screening.
TEMPO, short for “Thermodynamically Encoded Molecular Programming for One-Pot Diagnostics,” was jointly developed by a research team led by Hsing I-Ming, a professor in the Department of Chemical and Biological Engineering at HKUST, and researchers from The Chinese University of Hong Kong. The platform has already demonstrated applications in the detection of a range of viral infections, including influenza, COVID-19 and HIV, while also opening new possibilities for the rapid screening of hereditary diseases.
The study, titled “Thermodynamically programmed one-pot CRISPR platform for point-of-care SNP genotyping,” is published in the journal Nature Communications.
Living organisms experience millions of signals transferred every second between cells, tissues, organs, and external environmental stimuli. Fine-tuned responses at various degrees and scales within the human body are central to the homeostatic mechanism that copes with potentially harmful environmental perturbations, including pathogens, smoking, and drugs, and interacts with the genetic background arising from spontaneous somatic mutations and numerous germline variants. Thus, a holistic view of homeostatic mechanisms through the study of genomic and epigenetic aberrations is needed to understand the core of cancer biology and the pathophysiological features of cancer during oncogenesis and tumor progression.
A multi-omics study is a data-driven scientific investigation that analyzes a range of high-dimensional datasets at multiple levels and scales to reveal the complexity of cells and their environment. Such type of study can provide novel frameworks to untangle biological phenomena or models to test certain hypotheses using various datasets. In cancer research, a paradigm shift toward multi-omics approaches has been achieved with the recent development of high-throughput technologies in genomics and transcriptomics, increasing effort in large-scale research collaboration, and advancement of computational algorithms (Basu et al., 2013; Berns and Bernards, 2012; Cancer Genome Atlas Network, 2012b; Gentles and Gallahan, 2011; Whitehurst et al., 2007). Together with advances in genomics and transcriptomics, proteomics is emerging as a prominent field to elucidate the dynamics of gene activity. Large-scale proteomic research, such as that promoted by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), has uncovered the ubiquitous link of biomolecules to the environment and disease status (Gillette et al., 2020; Krug et al., 2020; Mertins et al., 2016; Mun et al., 2019; Zhang et al., 2016). Such a transition has extensively deepened our knowledge on the function of driver genes and proteins and has provided a comprehensive understanding of the signaling networks occurring between cells, tissues, organs, and the entire organism. Multi-omics approaches have been applied to numerous clinical studies for better identification of clinical subtypes or drug resistance, prediction of effective combination therapies, and identification of predictive biomarkers to increase the response rate to targeted treatments.
In this review, we introduce the concept of multi-omics approaches in cancer research and provide useful resources for this. We focus on some of the clinical and basic science studies that have benefited from the use of a multi-omics approach to uncover novel concepts and properties. We also discuss some of the challenges connected to multi-omics approaches and how this relatively young field of study can have a positive impact on cancer research.
Join us on Patreon! / michaellustgartenphd.
Discount Links/Affiliates:
Blood testing (where I get the majority of my labs, for those who blood test with Quest): https://www.ultalabtests.com/partners… those who blood test with LabCorp: https://www.anrdoezrs.net/click-10161… At-Home Metabolomics: https://www.iollo.com?ref=michael-lus… Use Code: CONQUERAGING At Checkout Clearly Filtered Water Filter: https://get.aspr.app/SHoPY Epigenetic, Telomere Testing: https://trudiagnostic.com/?irclickid=… Use Code: CONQUERAGING NAD+ Quantification: https://www.jinfiniti.com/intracellul… Use Code: ConquerAging At Checkout Oral Microbiome: https://www.bristlehealth.com/?ref=mi… Enter Code: ConquerAging SiphoxHealth Blood Testing (ApoB, GrimAge): https://siphoxhealth.com/mlustgarten Green Tea: https://www.ochaandco.com/?ref=fqbtflod Use Code: ML10OFF Diet Tracking: https://shareasale.com/r.cfm?b=139013… If you’d like to support the channel, you can do that with the website, Buy Me A Coffee: https://www.buymeacoffee.com/mlhnrca Conquer Aging Or Die Trying Merch! https://my-store-d4e7df.creator-sprin…
Blood Testing Essentials (Biological Age, CVD-Risk, Kidney Health and Function):
PhenoAge (Biological Age): https://www.ultalabtests.com/partners…
Measure the Bortz biomarkers: https://www.ultalabtests.com/partners…
Calculate your biological age using the Bortz clock: https://www.longevity-tools.com/human…