Biology subjects

Chau, G.

Publications and source records attributed to Chau, G..

5 recordsLinked to original sources

bioRxiv2026

Constrained Diffusion as a Paradigm for Evolution

A foundational question in computational biology is how to utilize data to describe the forces driving evolution. Here, we view evolution as a novel diffusion process constrained by many biological, physical, and environmental factors affecting organism viability at any given time. We introduce DiffEvol, a framework that models evolution as constrained diffusion over a discrete genotype space. Using real-world genomic sequence data alone, DiffEvol estimates complex evolutionary constraints by inverting the diffusion dynamics to recover a constrained subspace representing the viable genotype manifold, as well as its evolution over time. Applied to SARS-CoV-2 sequence data from 2020-2024, DiffEvol reconstructs constraint functions that recapitulate known viral fitness trends, including a pronounced "phase transition" that occurred following the widespread adoption of the SARS-CoV-2 vaccine. Our constraint subspace representation of the data characterizes such features and trends more clearly. This framework could be used not only to improve forecasting of emergent pathogenic strains, but also to produce more accurate reverse time analyses of their evolutionary dynamics to help identify ancestral variants and the forces having shaped a pathogens evolutionary trajectory. More generally, this formulation provides a method for linking observed sequence mutations to an evolving fitness landscape. Thus, constrained subspace diffusion offers a mathematical language for evolutionary dynamics in any system where random variation interacts with slowly-changing structural or global constraints, and can be applied to more complex evolutionary phenomena such as vaccine resistance, viral escape, and protein evolution.

bioinformatics↗

bioRxiv2025

Mechanism of age-related accumulation of mitochondrial DNA mutations in human blood

One of the strongest signatures of aging is an accumulation of mutant mitochondrial DNA (mtDNA) heteroplasmy. Here we investigate the mechanism underlying this phenomenon by calling mtDNA sequence, abundance, and heteroplasmic variation in human blood using whole genome sequences from [~]750,000 individuals. Our analyses reveal a simple, two-step mechanism: first, individual cells randomly accumulate low levels of "cryptic" mtDNA mutations; then, when a cell clone proliferates, the cryptic mtDNA variants are carried as passenger mutations and become detectable in whole blood. Four lines of evidence support this model: (1) the mutational spectrum of age-accumulating mtDNA variants is consistent with a well-established model of mtDNA replication errors, (2) these mutations are found primarily at low levels of heteroplasmy and do not show evidence of positive selection, (3) high mtDNA mutation burden tends to co-occur in samples harboring somatic driver mutations for clonal hematopoiesis (CH), and (4) nuclear GWAS reveals that germline variants predisposing to CH (such as those near TERT, TCL1A, and SMC4) also increase mtDNA mutation burden. We propose that the high copy number and high mutation rate of mtDNA make it a particularly sensitive blood-based marker of CH. Importantly, our work helps to mechanistically unify three prominent signatures of aging: common germline variants in TERT, clonal hematopoiesis, and observed mtDNA mutation accrual.

genetics↗

bioRxiv2024

Dissociating Contributions of Theta and Alpha Oscillations from Aperiodic Neural Activity in Human Visual Working Memory

While visual working memory (WM) is strongly associated with reductions in occipitoparietal alpha (8-12 Hz) power, the role of frontal midline theta (4-7 Hz) power is less clear, with both increases and decreases widely reported. Here, we test the hypothesis that this theta paradox can be explained by non-oscillatory, aperiodic neural activity dynamics. Because traditional time-frequency analyses of electroencephalography (EEG) data conflate oscillations and aperiodic activity, event-related changes in aperiodic activity can manifest as task-related changes in apparent oscillations, even when none are present. Reanalyzing EEG data from two visual WM experiments (n = 74, of either sex), and leveraging spectral parameterization, we found systematic changes in aperiodic activity with WM load, and we replicated classic alpha, but not theta, oscillatory effects after controlling for aperiodic changes. Aperiodic activity decreased during WM retention, and further flattened over the occipitoparietal cortex with an increase in WM load. After controlling for these dynamics, aperiodic-adjusted alpha power decreased with increasing WM load. In contrast, aperiodic-adjusted theta power appeared to increase during WM retention, but because aperiodic activity reduces more, it falsely appears as though theta "oscillatory" power (e.g., total band power) is reduced. Furthermore, only a minority of participants (31/74) had a detectable degree of theta oscillations. These results offer a potential resolution to the theta paradox where studies show contrasting power changes. Additionally, we have identified novel aperiodic dynamics during human visual WM. Significance statementWorking Memory (WM) is our ability to hold information in mind without it being present in our external environment. Years of research focused on oscillatory brain dynamics to discover the mechanisms of WM. Here, we specifically look at oscillatory and non-oscillatory, aperiodic activity as measured with scalp EEG to test their significance in supporting WM. We challenge earlier findings regarding theta oscillations with our analysis approach, while replicating alpha oscillation findings. Furthermore, aperiodic activity is found to be involved in WM, over frontal regions in a task-general manner, and over anterior regions this activity is reduced with an increase in the number of remembered items. Thus, we have identified novel aperiodic dynamics during human visual WM.

neuroscience↗

bioRxiv2024

GUIDE deconstructs genetic architectures using association studies

Genome-wide association studies have revealed that the genetic architecture of most complex traits is characterized by a large number of distinct effects scattered across the genome. Functional enrichment analyses help provide some biological interpretation of associated variants but more work is needed to further translate GWAS hits into meaningful biological insights. Thus, we set out to leverage the genetic association results from many traits with a view to identifying the set of modules, or latent factors, that mediate these associations. The identification of such modules may aid in disease classification as well as the elucidation of complex disease mechanisms. We propose a method, Genetic Unmixing by Independent Decomposition (GUIDE), to estimate a set of statistically independent latent factors that best express the patterns of association across many traits. The resulting latent factors not only have desirable mathematical properties, such as sparsity and a higher variance explained for the latent factors that are significantly associated with a given trait, but are also able to single out and prioritize key biological features or pathophysiological mechanisms underlying a given trait or disease. Moreover, we show that these latent factors can isolate biological pathways as well as epidemiological and environmental influences that compose the genetic architecture of complex traits.

genetics↗

bioRxiv2022

Decoding Motor Plans Using a Closed-Loop Ultrasonic Brain-Machine Interface

Brain-machine interfaces (BMIs) can be transformative for people living with chronic paralysis. BMIs translate brain signals into computer commands, bypassing neurological impairments and enabling people with neurological injury or disease to control computers, robots, and more with nothing but thought. State-of-the-art BMIs have already made this future a reality in limited clinical trials. However, high performance BMIs currently require highly invasive electrodes in the brain. Device degradation limits longevity to about 5 years. Their field of view is small, restricting the number, and type, of applications possible. The next generation of BMI technology should include being longer lasting, less invasive, and scalable to sense activity from large regions of the brain. Functional ultrasound neuroimaging is a recently developed technique that meets these criteria. In this present study, we demonstrate the first online, closed-loop ultrasonic brain-machine interface. We used 2 Hz real-time functional ultrasound to measure the neurovascular activity of the posterior parietal cortex in two nonhuman primates (NHPs) as they performed memory-guided movements. We streamed neural signals into a classifier to predict the intended movement direction. These predictions controlled a behavioral task in real-time while the NHP did not produce overt movements. Both NHPs quickly succeeded in controlling up to eight independent directions using the BMI. Furthermore, we present a simple method to "pretrain" the BMI using data from previous sessions. This enables the BMI to work immediately from the start of a session without acquiring extensive additional training data. This work establishes, for the first time, the feasibility of an ultrasonic BMI and prepares for future work on a next generation of minimally invasive BMIs that can restore function to patients with neurological, physical, or even psychiatric impairments.

neuroscience↗