Biology subjects

Huang, H.-H.

Publications and source records attributed to Huang, H.-H..

11 recordsLinked to original sources

bioRxiv2026

Divergent scalp-to-region distance alteration patterns in autism spectrum disorders, Parkinson's disease and Alzheimer's disease

Brain stimulation is increasingly recognized as an effective and important therapeutic intervention for many brain diseases. Distance between the scalp and other brain regions is a pivotal variable for neurostimulation planning and the development of new techniques, but alterations in the distance between the scalp and other regions in brain diseases are largely unknown. In this study, we developed an automatic pipeline to calculate scalp-to-region distance (SRD) values from T1 MR images and applied it to a total of 1382 participants, including patients with autism spectrum disorder (ASD), Parkinsons disease (PD), Alzheimers disease (AD), and cognitively normal controls (CNs). Cloud points were uniformly sampled on the automatically extracted scalp surface and cortex surface, on which the point-wise distance maps were generated. The brain was then coregistered with the BCI-DNI atlas, and SRD value for each brain region was extracted. Analysis of covariance (ANCOVA) was performed for SRD in each brain region, with age and sex as covariates. Compared with CNs, ASD patients showed widespread SRD decreases across the brain with prominent involvement of the frontal lobe, especially the orbitofrontal cortex and adjacent regions. In contrast, in AD patients, significantly increased SRD values were observed in various regions of the frontal gyrus. No significant SRD alteration was found in PD patients after correction. The automatic SRD calculation pipeline and the different patterns of SRD alterations in these diseases might be helpful for future neurostimulation planning in clinical practice. HighlightsO_LIAutomatic pipeline enables scalp-to-region distance (SRD) measurement, facilitates brain stimulation planning. C_LIO_LIASD patients show widespread SRD decreases, especially in the orbitofrontal cortex and adjacent regions. C_LIO_LIAD patients present increased SRD in the frontal gyrus and decreased SRD in the parahippocampal gyrus. C_LI

neuroscience↗

bioRxiv2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPTs power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research. Author SummaryWe developed a novel fine mapping method called FM-GPT, to identify putatively causal genes from correlated noise influencing a wide range of human traits and diseases with potentially mixed outcome types (e.g., binary, count or continuous). The rapid expansion of large-scale phenomic datasets has shifted the single-trait genetic studies to phenome-wide analyses, enabling the study of genetic architecture across many related traits simultaneously. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified causal genes. When applied to the UK Biobank data, FM-GPT greatly narrowed down the set size of putatively causal genes compared to other methods. The tool identified genes with pleiotropic effects on regional cortical thickness that regulate neuronal morphology and cortical organization across the cerebral cortex. It also identified genes that influence multiple medical conditions spanning the circulatory, metabolic, digestive, respiratory, and genitourinary systems. Among these conditions, two major axes of variation emerged, revealing a potential trade-off in gene regulation between immune and metabolic functions. This work provides a clearer picture of shared biological mechanisms across traits and diseases, advancing translational research and the understanding of comorbidity.

bioinformatics↗

bioRxiv2024

Deletions Rate-Limit Breast and Ovarian Cancer Initiation

Optimizing prevention and early detection of cancer requires understanding the number, types and timing of driver mutations. To quantify this, we exploited the elevated cancer incidence and mutation rates in germline BRCA1 and BRCA2 (gBRCA1/2) carriers. Using novel statistical models, we identify genomic deletions as the likely rate-limiting mutational processes, with 1-3 deletions required to initiate breast and ovarian tumors. gBRCA1/2-driven hereditary and sporadic tumors undergo convergent evolution to develop a similar set of driver deletions, and deletions explain the elevated cancer risk of gBRCA1/2-carriers. Orthogonal mutation timing analysis identifies deletions of chromosome 17 and 13q as early, recurrent events. Single-cell analyses confirmed deletion rate differences in gBRCA1/2 vs. non-carrier tumors as well as cells engineered to harbor gBRCA1/2. The centrality of deletion-associated chromosomal instability to tumorigenesis shapes interpretation of the somatic evolution of non-malignant tissue and guides strategies for precision prevention and early detection.

cancer biology↗

bioRxiv2023

Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections

Single-cell genetic and epigenetic analyses parse the brains billions of neurons into thousands of "cell-type" clusters, each residing in different brain structures. Many of these cell types mediate their unique functions by virtue of targeted long-distance axonal projections to allow interactions between specific cell types. Here we have used Epi-Retro-Seq to link single cell epigenomes and associated cell types to their long-distance projections for 33,034 neurons dissected from 32 different source regions projecting to 24 different targets (225 source [->]target combinations) across the whole mouse brain. We highlight uses of this large data set for interrogating both overarching principles relating projection cell types to their transcriptomic and epigenomic properties and for addressing and developing specific hypotheses about cell types and connections as they relate to genetics. We provide an overall synthesis of the data set with 926 statistical comparisons of the discriminability of neurons projecting to each target for every dissected source region. We integrate this dataset into the larger, annotated BICCN cell type atlas composed of millions of neurons to link projection cell types to consensus clusters. Integration with spatial transcriptomic data further assigns projection-enriched clusters to much smaller source regions than afforded by the original dissections. We exemplify these capabilities by presenting in-depth analyses of neurons with identified projections from the hypothalamus, thalamus, hindbrain, amygdala, and midbrain to provide new insights into the properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription factor binding motifs, and neurotransmitter usage.

neuroscience↗

bioRxiv2022

Incoherent merger network for robust ratiometricgene expression response

A ratiometric response gives an output that is proportional to the ratio between the magnitudes of two inputs. Ratio computation has been observed in nature and is also needed in the development of smart probiotics and organoids. Here, we achieve ratiometric gene expression response in bacteria E. coli with the incoherent merger network. In this network, one input molecule activates expression of the output protein while the other molecule activates an intermediate protein that enhances the outputs degradation. When degradation rate is first order and faster than dilution, the output responds linearly to the ratio between the input molecules levels over a wide range with R2 close to 1. Response sensitivity can be quantitatively tuned by varying the outputs translation rate. Furthermore, ratiometric responses are robust to global perturbations in cellular components that influence gene expression because such perturbations affect the output through an incoherent feedforward loop. This work demonstrates a new molecular signal processing mechanism for multiplexed sense-and-respond circuits that are robust to intra-cellular context.

synthetic biology↗

bioRxiv2022

Effects of substrate on shrimp growth, water quality and bacterial community in the biofloc system nursing Penaeus vannamei postlarvae at a salinity of 5{per thousand}

This study aimed to investigate the effects of substrate on water quality, shrimp growth and bacterial community in the biofloc system with a salinity of 5{per thousand}. Two treatments, biofloc system with (sB) or without (nB) addition of elastic solid packing filler (nylon) as substrate, were set up. Penaeus vannamei postlarvae (PL, ~ stage 15) were stocked at a density of 4000 PL m-3 in each treatment with triplicates for a 28-days culture experiment, taking glucose as carbon source (C:N 15:1). Results showed that the survival rate (96.3{+/-}3.6%), FCR (0.76{+/-}0.06) and productivity (1.54{+/-}0.12 kg m-3) in sB treatment were significantly better than those in nB treatment (81.0{+/-}7.1%, 0.98{+/-}0.08 and 1.14{+/-}0.09 kg m-3, P<0.05). All water parameters were in the recommended ranges. Substrate showed significant effect on TAN, TSS, turbidity, biofloc volume, pH and carbonate alkalinity (P < 0.05). Actinobacteria (4.0-22.7%), Bacteroidetes (10.4-33.5%), Firmicutes (0.2-11.2%), Planctomycetes (4.0-14.9%) and Proteobacteria (29.4-59.0%) were the most dominant phyla for both treatments. However, the bacterial community in sB treatment showed to be significantly different from that in nB treatment (Jaccard distance 0.94{+/-}0.01, P=0.001). Substrate showed significant effects on Shannon, Heip, Pielou and Simpson index, as well as relative abundances of Actinobacteria, Bacteroidetes and Planctomycetes (P < 0.05). The results suggested that addition of substrate affected the shrimp growth, water quality and bacterial community in the biofloc system nursing P. vannamei PL with a 5{per thousand} salinity.

zoology↗

bioRxiv2022

Bacterial composition and inferring function profiles in the biofloc system rearing Litopenaeus vannamei postlarvae at a low salinity

This study aimed to investigate the bacterial composition and inferring function profiles in the biofloc system rearing Litopenaeus vannamei postlarvae (PL) at a low salinity condition. PL (~ stage 15) were stocked in four parallel tanks filled in water with a salinity of 5.0{per thousand} at a density of 4000 individuals per m3 for a 28-days culture experiment, during which glucose was added as carbon source with a C:N of 20:1. At the end of experiment, water was sampled from each tank and pooled to extract microbial DNA for high-throughput sequencing of V3-V4 region of 16S rRNA gene. Results showed that the bacterial community at 28 d was dominated by phyla of Proteobacteria (45.8%), Bacteroidetes (21.1%), Planctomycetes (13.5%), Chlamydiae (10.3%) and Firmicutes (6.8%). A proportion of 81% inferring KEGG functions of this bacterial community associated with metabolism. Among functions relating to nitrogen metabolism, 48.5% were involved in the conversion of ammonia to glutamate, but the proportion of those involved in transformation among ammonia, nitrite and nitrate was 18.0% in total, inferring higher protein-synthesis but lower inorganic nitrogen-transformation capacities of the bacterial community. At the same time (28 d), high levels of total nitrogen (231.3{+/-}6.0 mg L-1) and biofloc (127.0{+/-}63.0 mL L-1), but low concentrations of ammonia (0.04{+/-}0.01 mg L-1), nitrite (0.2{+/-}0.1 mg L-1) and nitrate (12.9{+/-}2.5 mg L-1) were observed. The results supply a novel insight for understanding the function of bacterial community in the biofloc system nursing L. vannamei PL at a low salinity.

systems biology↗

bioRxiv2021

Effects of carbon to nitrogen ratio (C:N) on water quality and growth performance of Litopenaeus vannamei (Boone, 1931) in the biofloc system with a salinity of 5%

This study aimed to investigate the effects of carbon to nitrogen ratio (C:N) on the water quality and shrimp growth performance during the grow-out culture of Litopenaeus vannamei in the biofloc system under a low salinity condition. Three biofloc treatments with an C:N (contained in the inputted feed and carbon source with the assumption that 75% of the feed nitrogen is excreted) of 8:1 (CN8), 16:1 (CN16) and 24:1 (CN24), respectively, were designed to stocking shrimp juveniles (~ 0.8 g) at a density of 270 individuals m-3, for a 63-days culture experiment at a salinity of about 5{per thousand}. Results showed that in CN8 treatment, the levels of pH (6.9{+/-}0.1), carbonate alkalinity (104.0{+/-}2.8mg L-1 CaCO3), biofloc volume (4.8{+/-}0.9mL L-1) and TSS (327.4{+/-}24.4mg L-1) were significantly lower than those in the other two treatments ([≥]7.6{+/-}0.3, [≥]157.6{+/-}21.6mg L-1 CaCO3, [≥]24.1{+/-}3.7mL L-1 and [≥]508.1{+/-}32.3mg L-1, P<0.05); whereas the levels of TAN (7.1{+/-}0.9mg L-1), nitrite (14.0{+/-}3.6mg L-1) and nitrate (77.0{+/-}5.0mg L-1) were significantly higher than those in the other treatments ([≤]2.0{+/-}0.6mg L-1, [≤]4.9{+/-}3.1mg L-1 and [≤]14.7{+/-}5.9mg L-1, P<0.05). The zootechnical parameters of shrimp were not significantly different between three treatments (P>0.05), except that the survival rates in CN16 treatment (96.8{+/-}2.0%) and CN24 treatment (93.7{+/-}4.2%) were significantly higher than that of CN8 treatment (81.5{+/-}6.4%, P<0.05). The results indicated that an inputted C:N higher than 16:1 was suitable for the biofloc system with a low salinity of 5{per thousand}, with an optimal inferred C:N range of 18.5-21.0:1 for water quality and growth performance.

zoology↗

bioRxiv2020

dCas9 regulator to neutralize competition in CRISPRi circuits

CRISPRi-mediated gene repression allows simultaneous control of many genes. However, despite highly specific sgRNA-promoter binding, multiple sgRNAs still interfere with one another by competing for dCas9. We created a dCas9 regulator that adjusts dCas9 concentration based on sgRNAs demand, mitigating competition in CRISPRi-based logic gates. The regulators performance is demonstrated on both single-stage and layered CRISPRi logic gates and in two common E. coli strains. When a competitor sgRNA causes between two and ~25 fold-change in a logic gates input/output response without dCas9 regulator, the response is essentially unchanged when the regulator is used. The dCas9 regulator thus enables concurrent and independent operation of multiple sgRNAs, thereby supporting independent control of multiple genes.

synthetic biology↗

bioRxiv2018

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A genes expression level is not only determined by the intended regulators, but also largely dictated by changes in ribosome availability imparted by activation or repression of other genes. To address this problem, we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOIs mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOIs translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

synthetic biology↗

bioRxiv2016

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuits behavior. Here, we create a library of genetic activation cascades in bacteria E. coli, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to non-regulatory interactions among genes that reshape circuits behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site (RBS) and DNA copy number to restore the cascades intended monotonically increasing response. Our results demonstrate how unintended circuits behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

synthetic biology↗