Biology subjects

Chou, L.

Publications and source records attributed to Chou, L..

5 recordsLinked to original sources

bioRxiv2026

Manipulating Excitation-Inhibition Balance by Temporal Interference Brain Stimulation at Different Frequencies

Temporal interference brain stimulation (TIBS) is a non-invasive neuromodulation approach that can reach deep brain targets by delivering two kilohertz-frequency currents through scalp electrodes, producing a low-frequency amplitude envelope where the fields intersect. Conventional deep brain stimulation suppresses its targets at 130 Hz, and TIBS studies of epilepsy have adopted the same range. However, which neurons TIBS recruits at different frequencies has never been measured. Here, we recorded from genetically defined populations in the mouse hippocampus across envelope frequencies from 10 to 130 Hz, using cell-type-specific fiber photometry, retrograde viral labelling, and immunohistochemistry. A 10 Hz envelope drives both glutamatergic pyramidal neurons and GABAergic interneurons. From 20 Hz onward pyramidal activity falls below baseline while interneuron activity keeps rising, and the two separate maximally at 100 Hz. Retrogradely labelled cortical neurons projecting to CA2 respond weakly and show no frequency dependence, placing the switch inside the local circuit, and c-fos co-staining identifies parvalbumin interneurons as the population recruited at 100 Hz. Overall, this study provides the first in vivo cell-type-resolved evidence for envelope-frequency-dependent neuromodulation and shows that TIBS envelope frequency is a tunable parameter for excitation-inhibition balance. These findings can guide the choice of envelope frequency in the clinical research of TIBS.

neuroscience↗

bioRxiv2026

Environmental stress and phenotypic tradeoff modulate the adaptive potential of novel coding sequences for de novo gene birth

Novel protein-coding genes can emerge de novo from ancestrally noncoding sequences and promote adaptation to environmental stresses. Previous work proposes that pervasive translation of lowly expressed open reading frames (ORFs) in noncoding regions creates a rich reservoir of 'proto-genes,' of which subsequent acquisition of gene-like properties, such as increased expression, may be favored or purged by natural selection depending on their phenotypic impact. However, whether and how environmental conditions affect the phenotypic impact of proto-genes remains unclear. Here, we experimentally simulated proto-gene evolution in Saccharomyces cerevisiae by individually increasing the expression of nearly a thousand de novo ORFs with prior evidence of native translation under osmotic and endoplasmic-reticulum stress and in control environments. High-throughput phenotyping revealed that growth effects of increased expression varied strongly across environments for de novo ORFs. A follow-up screen across 22 diverse environments revealed a robust positive correlation between environmental stress severity and the mean growth effects of increased de novo ORF expression. At the individual level, 5.4% of tested de novo ORFs conferred beneficial phenotypes in at least one environment, and 83.3% of these also caused deleterious effects elsewhere, revealing widespread phenotypic tradeoffs. We demonstrate that increased expression of the de novo translated ORF YLR112W results in increased growth in the presence of rapamycin through general dampening of the growth-repressing transcriptomic response induced by this drug. Together, these findings demonstrate that stress severity shapes the phenotypic consequences of increased proto-gene expression and shed light on tradeoffs and transcriptome remodeling as mechanisms underlying such environmental dependency.

evolutionary biology↗

bioRxiv2021

Species boundaries in the Agrobacterium tumefaciens complex and multi-level modular evolution of their antibacterial type VI secretion system and tumor-inducing plasmids

BackgroundMany bacterial taxa are species complexes and uncertainties regarding the organization of their genetic diversity challenge research efforts. We utilized Agrobacterium tumefaciens, a taxon known for its phytopathogenicity and applications in transformation, as a study system and devised strategies for investigating genome diversity and evolution of species complexes. ResultsWe utilized 35 genome assemblies to achieve a comprehensive and balanced sampling of A. tumefaciens. Our confident inference of gene content and core-genome phylogeny supported a quantitative guideline for delineating 12 species and allowed for robust investigations of genes critical in fitness and ecology. For the type VI secretion system (T6SS) involved in interbacterial competition and thought to be conserved, we detected multiple losses and one horizontal gene transfer. For the tumor-inducing plasmids (pTi) and pTi-encoded type IV secretion system (T4SS) that are essential for agrobacterial phytopathogenicity, we uncovered novel diversity and hypothesized their involvement in shaping this species complex. Intriguingly, for both T6SS and T4SS, genes encoding structural components are highly conserved, whereas extensive diversity exists for genes encoding effectors and other proteins. ConclusionsWe demonstrated that the combination of a phylogeny-guided sampling scheme and an emphasis on high-quality assemblies provides a cost-effective approach for robust analysis in evolutionary genomics. Our strategies for multi-level investigations at scales that range from whole-genomes to intragenic domains and phylogenetic depths of between- and within-species are applicable to other bacteria. Finally, modularity observed in the molecular evolution of genes and domains is useful for inferring functional constraints and informing experimental works.

genomics↗

bioRxiv2019

Diversified RACE Sampling on Data Streams Applied to Metagenomic Sequence Analysis

The rise of whole-genome shotgun sequencing (WGS) has enabled numerous breakthroughs in large-scale comparative genomics research. However, the size of genomic datasets has grown exponentially over the last few years, leading to new challenges for traditional streaming algorithms. Modern petabyte-sized genomic datasets are difficult to process because they are delivered by high-throughput data streams and are difficult to store. As a result, many traditional streaming problems are becoming increasingly relevant. One such problem is the task of constructing a maximally diverse sample over a data stream. In this regime, complex sampling procedures are not possible due to the overwhelming data generation rate. In theory, the best diversity sampling methods are based on a simple greedy algorithm that compares the current sequence with a large pool of sampled sequences and decides whether to accept or reject the sequence. While these methods are elegant and optimal, they are largely confined to the theoretical realm because the greedy procedure is too slow in practice. While there are many methods to identify common elements in data streams efficiently, fast and memory-efficient diversity sampling remains a challenging and fundamental data streaming problem with few satisfactory solutions. In this work, we bridge the gap with RACE sampling, an online algorithm for diversified sampling. Unlike random sampling, which samples uniformly, RACE selectively accepts samples from streams that lead to higher sequence diversity. At the same time, RACE is as computationally efficient as random sampling and avoids pairwise similarity comparisons between sequences. At the heart of RACE lies an efficient lookup array constructed using locality-sensitive hashing (LSH). Our theory indicates that an accept/reject procedure based on LSH lookups is sufficient to obtain a highly diverse subsample. We provide rigorous theoretical guarantees for well-known biodiversity indices and show that RACE can nearly double the Shannon and Simpson indices of a genetic sample in practice, all while using the same resources as random sampling. We also compare RACE against Diginorm and coreset-based diversity sampling methods and find that RACE is faster and more memory efficient. Our algorithm is straightforward to implement, easy to parallelize, and fast enough to keep pace with the overwhelming data generation rates. We expect that as DNA sequence data streams become more mainstream and faster, RACE will become an essential component for many applications.1

bioinformatics↗

bioRxiv2019

Cell-free transcriptional regulation via nucleic-acid-based transcription factors

Cells execute complex transcriptional programs by deploying distinct protein regulatory assemblies that interact with cis-regulatory elements throughout the genome. Using concepts from DNA nanotechnology, we synthetically recapitulated this feature in cell-free gene networks actuated by T7 RNA polymerase (RNAP). Our approach involves engineering nucleic-acid hybridization interactions between a T7 RNAP site-specifically functionalized with single-stranded DNA (ssDNA), templates displaying cis-regulatory ssDNA domains, and auxiliary nucleic-acid assemblies acting as artificial transcription factors (TFs). By relying on nucleic-acid hybridization, de novo regulatory assemblies can be computationally designed to emulate features of protein-based TFs, such as cooperativity and combinatorial binding, while offering unique advantages such as programmability, chemical stability, and scalability. We illustrate the use of nucleic-acid TFs to implement transcriptional logic, cascading, feedback, and multiplexing. This framework will enable rapid prototyping of increasingly complex in vitro genetic devices for applications such as portable diagnostics, bio-analysis, and the design of adaptive materials.

synthetic biology↗