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Biology subjects

Hou, M.

Publications and source records attributed to Hou, M..

At least 37 records · Page 2Linked to original sources

Resolving Paradoxes in Molecular Evolution: The Integrated WF-Haldane (WFH) Model of Genetic Drift

Genetic drift, the random changes in frequencies of neutral variants, is the fundamental force of molecular evolution. Under-estimation of genetic drift is a major cause of mis-conclusions on evolution. However, the standard Wright-Fisher (WF) model of random sampling in population of size N only partially defines genetic drift with 1/N or 1/Ne (Ne being a function of varying N's). In parallel, JBS Haldane proposed the branching process for genetic drift (Haldane 1927), whereby each gene copy is transmitted to K descendants with the mean and variance of E(K) and V(K). Genetic drift in its essence is V(K), i.e., the variance in transmission success, or V(K)/N when averaged over the population. Under the general WF models (i.e., the standard model as well as the many modifications), paradoxes have emerged. They include: i) Most curiously, genetic drift may often become stronger as N becomes larger at the ecological time scale, opposite to the model; ii) Sex chromosomes experience drift differently even with the same normalized N; iii) Genetic drift operates on advantageous mutations depends on V(K) but not on N; iv) Irresolution and paradoxes emerge in multi-copy gene systems, including diploidy, viruses and others, whereby evolution progresses both within and between individuals (Wang, et al. 2024). We now show that the integration of the WF and Haldane (i.e., WFH) model can resolve these paradoxes. Most important, the WFH model can fully define genetic drift in molecular evolution.

evolutionary biology↗

Platelet-specific TGFβ1 deficiency aggravates atherosclerosis, vascular inflammation, and hypercholesterolemia in mice

Atherosclerosis involves inflammatory and thrombotic mechanisms, to which both platelets and transforming growth factor {beta} (TGF{beta}) contribute. The effect of platelet-derived TGF{beta} on atherosclerosis is, however, unknown and therefore investigated. Murine platelet-selective TGF{beta}-deficiency (plt-TGF{beta}-/-) was created by a Pf4-Cre approach, and an atherosclerotic mouse model was established by functional abrogation of Ldlr and 10-15 weeks of a high-fat diet in plt-TGF{beta}-/- mice and their non-plt-TGF{beta}-/- littermates. En face Oil Red O staining of the aorta showed more atherosclerotic lesion formation in plt-TGF{beta}-/- mice, with significant increases in both lesion size and lesion coverage of the total aortic area. Cryosections of the aortic root confirmed the aggravation of atherogenesis. Platelet-derived TGF{beta} deficiency increased circulating platelets and plasma levels of total cholesterol, LDL-cholesterol, and triglycerides after a 10 or 15 week high-fat diet period. RNA sequencing and proteomic analyses of the aorta showed signs of CD4+ T effector cell and macrophage activation in plt-TGF{beta}-/- mice. In conclusion, platelet-specific TGF{beta} deficiency aggravates atherosclerosis, via increasing arterial inflammation and plasma levels of cholesterol. Our findings demonstrate that platelet-derived TGF{beta} is prominently athero-protective. Key pointsO_LIPlatelet-specific transforming growth factor {beta} (TGF{beta}) deficiency markedly enhances atherosclerosis in a high-fat diet-fed murine model. C_LIO_LIPlatelet TGF{beta} deficiency aggravates hyperlipidemia, with further elevations of total cholesterol, LDL-cholesterol, and triglycerides. C_LI

cell biology↗

Transcriptome-wide analysis of the function of Ded1 in translation preinitiation complex assembly in a reconstituted in vitro system

We have developed a deep sequencing-based approach, Rec-Seq, that allows simultaneous monitoring of ribosomal 48S pre-initiation complex (PIC) formation on every mRNA in the translatome in an in vitro reconstituted system. Rec-Seq isolates key early steps in translation initiation in the absence of all other cellular components and processes. Using this approach we show that the DEAD-box ATPase Ded1 promotes 48S PIC formation on the start codons of >1000 native mRNAs, most of which have long, structured 5-untranslated regions (5UTRs). Remarkably, initiation measured in Rec-Seq was enhanced by Ded1 for most mRNAs previously shown to be highly Ded1-dependent by ribosome profiling of ded1 mutants in vivo, demonstrating that the core translation functions of the factor are recapitulated in the purified system. Our data do not support a model in which Ded1acts by reducing initiation at alternative start codons in 5UTRs and instead indicate it functions by directly promoting mRNA recruitment to the 43S PIC and scanning to locate the main start codon. We also provide evidence that eIF4A, another essential DEAD-box initiation factor, is required for efficient PIC assembly on almost all mRNAs, regardless of their structural complexity, in contrast to the preferential stimulation by Ded1 of initiation on mRNAs with long, structured 5UTRs.

biochemistry↗

Relationships between age, fMRI correlates of familiarity and familiarity-based memory performance under single and dual task conditions

Using fMRI, we investigated the effects of age and divided attention on the neural correlates of familiarity and their relationship with memory performance. At study, word pairs were visually presented to young and older participants under the requirement to make a relational judgment on each pair. Participants were then scanned while undertaking an associative recognition test under single and dual (auditory tone detection) task conditions. The test items comprised studied, rearranged (words from different studied pairs) and new word pairs. fMRI familiarity effects were operationalized as greater activity elicited by studied pairs incorrectly identified as rearranged than by correctly rejected new pairs. The reverse contrast was employed to identify novelty effects. Behavioral familiarity estimates were equivalent across age groups and task conditions. Robust fMRI familiarity effects were identified in several regions, including medial and superior lateral parietal cortex, dorsal medial and left lateral prefrontal cortex, and bilateral caudate. fMRI novelty effects were identified in the anterior medial temporal lobe. Both familiarity and novelty effects were age-invariant and did not vary according to task condition. In addition, the familiarity effects correlated positively with a behavioral estimate of familiarity strength irrespective of age. These findings extend a previous report from our laboratory, and converge with prior behavioral reports, in demonstrating that the factors of age and divided attention have minimal impact on behavioral and neural estimates of familiarity.

neuroscience↗

Protein multiple conformations prediction using multi-objective evolution algorithm

MotivationWith the breakthrough of AlphaFold2 and the publication of AlphaFold DB, the protein structure prediction has made remarkable progress, which may further promote many potential applications of proteomics in all areas of life. However, it should be noted that AlphaFold2 models tend to represent only a single static structure, and accurately predicting multiple conformations remains a challenge. Therefore, it is essential to develop methods for predicting multiple conformations, which enable us to gain knowledge of multiple conformational states and the broader conformational landscape to better understand the mechanism of action. ResultsIn this work, we proposed a multiple conformational states folding method using the distance-based multi-objective evolutionary algorithm framework, named MultiSFold. First, a multi-objective energy landscape with multiple competing constraints generated by deep learning is constructed. Then, an iterative modal exploration and exploitation strategy based on multi-objective optimization, geometric optimization and structural similarity clustering is designed to perform conformational sampling. Finally, the final population is generated using a loop-specific perturbation strategy to adjust the spatial orientations. MultiSFold was compared with state-of-the-art methods on a developed benchmark testset containing 81 proteins with two representative conformational states. Based on the proposed metric, the success ratio of MultiSFold predicting multiple conformations was 70.4% while that of AlphaFold2 was 9.88%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to produce conformations spanned the range between two experimental structures. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate that the TM-score of MultiSFold is 2.97% and 7.72% higher than that of AlphaFold2 and RoseTTAFold, respectively, supporting our hypothesis that multiple competing optimization objectives can further assist conformational search to improve prediction accuracy.

bioinformatics↗

BATF relieves hepatic steatosis by inhibiting PD1 and promoting energy metabolism

The rising prevalence of nonalcoholic fatty liver disease (NAFLD) has become a global health threat that needs to be addressed urgently. Basic leucine zipper ATF-like transcription factor (BATF) is commonly thought to be involved in immunity, but its effect on lipid metabolism is not clear. Here, we investigated the function of BATF in hepatic lipid metabolism. BATF alleviated high-fat diet (HFD)-induced hepatic steatosis and inhibited elevated programmed cell death protein (PD)1 expression induced by HFD. A mechanistic study confirmed that BATF regulated fat accumulation by inhibiting PD1 expression and promoting energy metabolism. PD1 antibodies alleviated hepatic lipid deposition. In conclusion, we identified the regulatory role of BATF in hepatic lipid metabolism and that PD1 is a target for alleviation of NAFLD. This study provides new insights into the relationship between BATF, PD1 and NAFLD.

molecular biology↗

A Hidden Proportional Feedback Mechanism Underlies Enhanced Dynamic Performance and Noise Rejection in Sensor-Based Antithetic Integral Control

Efficient regulation of cellular processes is essential for both endogenous and synthetic biological processes. The design of biomolecular feedback controllers that achieve robust and timely regulation is the subject of considerable research at the interface between synthetic biology and control theory. Integral feedback controllers, known for their ability to confer the property of Robust Perfect Adaptation (RPA), are increasingly becoming common features in biological control design. Antithetic integral feedback (AIF) controllers, in particular, have enabled effective chemical reaction realizations of integral controllers that deliver RPA in both deterministic and stochastic settings. This paved the way to experimental implementations of integral controllers in bacterial and mammalian cells. While AIF controllers deliver favorable adaptation properties, they do not necessarily lead to good transient performance or noise reduction properties and may in some cases lead to increased overshoot or cell-to-cell variability. These limitations are commonly circumvented by augmenting new circuitry that realize proportional or derivative feedback mechanisms to enhance dynamic and noise rejection features without affecting the AIF controllers adaptation properties. In this paper, we report that a sensor-based variant of the basic AIF motif exhibits favorable transient dynamic properties and (as reported elsewhere) reduced noise variance. We show that these features are attributed to a "hidden" proportional feedback component that is inherent in the controller structure and that such mechanism is solely responsible for the controllers underlying enhanced dynamic performance and noise rejection properties. This sensor-based AIF controller hence offers a minimal biomolecular realization of Proportional-Integral (PI) control, whereby both integral and proportional feedback mechanisms are achieved through a single actuation reaction.

synthetic biology↗

The effects of age on neural correlates of recollection: transient versus sustained fMRI effects

Prior fMRI findings in young adults indicate that recollection-sensitive neural regions dissociate according to the time courses of their respective recollection effects. Here, we examined whether such dissociations are also evident in older adults. Young and older participants encoded a series of word-object image pairs, judging which of the denoted objects was the smaller. At test, participants first judged whether a test word was old or new. For items judged old, they were required to recall the associated image and hold it in mind across a variable delay period. A post-delay cue denoted which of three judgments should be made on the retrieved image. Older adults demonstrated significantly lower associative memory performance than young adults. Replicating prior findings, transient recollection effects were identified in the left hippocampus, medial prefrontal cortex and posterior cingulate, while sustained effects were widespread across left lateral cortex and were also evident in the bilateral striatum. With the exception of those in the left insula, all effects were age-invariant. These findings add to the evidence that recollection-related BOLD effects in different neural regions can be temporally dissociated. Additionally, the findings suggest that both transient and sustained recollection effects are largely stable across much of the healthy adult lifespan.

neuroscience↗

The landscape of m1A modification and its posttranscriptional regulatory functions in primary neurons

Cerebral ischaemia-reperfusion injury, during which neurons undergo oxygen-glucose deprivation/reoxygenation (OGD/R), is a notable pathological process in many neurological diseases. N1-methyladenosine (m1A) is an RNA modification that can affect gene expression and RNA stability. The m1A landscape and potential functions of m1A modification in neurons remain poorly understood. We explored RNA (mRNA, lncRNA, and circRNA) m1A modification in normal and OGD/R-treated neurons and the effect of m1A on diverse RNAs. We investigated the m1A landscape in primary neurons, identified m1A-modified RNAs, and found that OGD/R increased the number of m1A RNAs. m1A modification might also affect the regulatory mechanisms of noncoding RNAs, e.g., lncRNA-RBP interactions and circRNA translation. We showed that m1A modification mediates the circRNA/lncRNA-miRNA-mRNA ceRNA mechanism and that 3UTR methylation of mRNAs can hinder miRNA-mRNA binding. Three methylation patterns were identified, and genes with different patterns had intrinsic mechanisms with potential m1A-regulatory specificity. Systematic analysis of the m1A landscape in normal and OGD/R neurons lays a critical foundation for understanding RNA methylation and provides new perspectives and a theoretical basis for treating and developing drugs for OGD/R pathology-related diseases.

neuroscience↗

Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues

With the breakthrough of AlphaFold2, nearly all single-domain protein structures can be built at experimental resolution. However, accurate modelling of full-chain structures of multidomain proteins, particularly all relevant conformations for those with multiple states remain challenging. In this study, we develop a multidomain protein assembly method, M-SADA, for assembling multiple conformational states. In M-SADA, a multiple population-based evolutionary algorithm is proposed to sample multiple conformational states under the guidance of multiple energy functions constructed by combining homologous and analogous templates with inter-domain distances predicted by deep learning. On a developed benchmark dataset containing 72 multidomain proteins with multiple conformational states, the performance of M-SADA is significantly better than that of AlphaFold2 on multiple conformational states modelling, where 29/72 (40.3%) of proteins can be assembled with a TM-score >0.90 for highly distinct conformational states with M-SADA while AlphaFold2 does so in only 2/72 (2.8%) of proteins. Furthermore, M-SADA is tested on a developed benchmark dataset containing 296 multidomain proteins with single conformational state, and results show that the average TM-score of M-SADA on the best models is 0.913, which is 5.2% higher than that of AlphaFold2 models (0.868).

bioinformatics↗

Retrieving the near-complete genome of a threatened bird from wild frozen samples

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Collecting samples from frozen dead animals in the wild provides an alternative approach to obtaining high-quality nucleic acids. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) of a recent death found in the mountainous region in Gansu, China. We generated a near-complete genome assembly (OTswu) having only 18 gaps scattering in 8 out of the 40 assembled chromosomes. Unlikely most other bird genome assemblies, OTswu contains complete chromosome models (2n = 80). We demonstrated that the great bustard genome likely retained the ancestral avian karyotype. We also characterized the DNA methylation landscapes of OTswu which are strongly correlated with GC content and gene expression. Our phylogenomic analysis suggested Otidiformes and Musophagiformes were sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard was found the lowest among the four available Otidiformes genomes, possibly due to population declines during past glacial periods. As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriching for metabolism. Finally, we identified an extremely young evolutionary stratum on the sex chromosome, a rare case among Neoaves. Together, our study combining long-read sequencing and RNA-seq technology provides a working strategy for conducting multi-omic analyses for threatened species by retrieving high-quality nucleic acids from dead animals frozen in the wild.

genomics↗

Cortical thickness, gray matter volume and cognitive performance: a cross-sectional study of the moderating effects of age on their inter-relationships

In a sample comprising younger, middle-aged and older cognitively healthy adults (N = 375), we examined associations between mean cortical thickness, gray matter volume (GMV) and performance in four cognitive domains - memory, speed, fluency and crystallized intelligence. In almost all cases, the associations were moderated significantly by age, with the strongest associations in the older age group. An exception to this pattern was identified in a younger adult sub-group aged less than 23 yrs, when a negative association between cognitive performance and cortical thickness was identified. Other than for speed, all associations between structural metrics and performance in specific cognitive domains were fully mediated by mean cognitive ability. Cortical thickness and GMV explained unique fractions of the variance in mean cognitive ability, speed and fluency. In no case, however, did the amount of variance jointly explained by the two metrics exceed 7% of the total variance. These findings suggest that cortical thickness and GMV are distinct correlates of domain-general cognitive ability, that the strength and, for cortical thickness, the direction of these associations is moderated by age, and that these structural metrics offer only limited insights into the determinants of individual differences in cognitive performance across the adult lifespan.

neuroscience↗

Macrophage migrates on alternate non-adhesive surfaces

Macrophages migrate across tissues upon immune demand, but their motility on heterogeneous substrates remains unclear. Protein-repelling reagents, e.g., poly(ethylene) glycol (PEG), are routinely employed to resist cell adhering and migrating. Contrary to this perception, we discovered a unique locomotion of macrophages in vitro that they overcome non-adhesive PEG gaps to reach adhesive regions in a mesenchymal mode. Adhesion to adhesive regions was a prerequisite for macrophages to perform further locomotion on the PEG regions, or else they kept a suspended round shape. Podosomes were found highly enriched on the PEG region, which supported macrophage migration. Myosin IIA played a negative role in macrophage motility. Moreover, a developed cellular Potts model reproduced the experimental observations. These findings uncovered a new migratory behavior on non-adhesive surfaces in macrophages. One-Sentence SummaryMacrophages can migrate across non-adhesive surfaces that are absolute boundaries for other cell types.

cell biology↗

Dual-Resolving of Lipid Positional and Geometric Isomers of C=C Bonds via Bifunctional Photocycloaddition-Photoisomerization Reaction System and Mass Spectrometry

The biological functions of lipids largely depend on their chemical structures. The position and configuration of C=C bonds are two of the essential attributes that determine the structures of unsaturated lipids. However, simultaneous identification of both attributes remains challenging. Here, we developed a bifunctional visible-light-activated photocycloaddition-photoisomerization reaction system, which enabled the dual-resolving of the positional and geometric isomerism of C=C bonds in lipids when combined with liquid chromatographic mass spectrometry. The dual-pathway reaction mechanism was demonstrated by experiments and density functional theory calculations. Based on this bifunctional reaction system, a workflow of deep structural lipidomics was established, and allowed the revealing of unique patterns of cis-trans-isomers in bacteria, as well as the tracking of C=C positional isomers changes in mouse brain ischemia. This study not only offers a powerful tool for deep lipid structural biology, but also provides a new paradigm for developing the multifunctional visible-light-induced reaction.

biochemistry↗

Structural analogue-based protein structure domain assembly assisted by deep learning

MotivationWith the breakthrough of AlphaFold2, the protein structure prediction problem has made a remarkable progress through end-to-end deep learning techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of full-chain model can be further improved by domain assembly assisted by deep learning. ResultsIn this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database (MPDB) was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the physics-based force field and an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled full-chain models of 20 human multi-domain proteins using individual domain models independently predicted by AlphaFold2, where the SADA full-chain models obtained a 4.8% higher average TM-score than full-chain models directly predicted by AlphaFold2 and fewer computing resources were required. In addition, we also find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. AvailabilityThe SADA web server are freely available at http://zhanglab-bioinf.com/SADA.

bioinformatics↗

The runaway evolution of SARS-CoV-2 leading to the highly evolved Delta strain

In new epidemics after the host shift, the pathogens may experience accelerated evolution driven by novel selective pressures. When the accelerated evolution enters a positive feedback loop with the expanding epidemics, the pathogens runaway evolution may be triggered. To test this possibility in COVID-19, we analyze the extensive databases and identify 5 major waves of strains, one replacing the previous one in 2020 - 2021. The mutations differ entirely between waves and the number of mutations continues to increase, from 3-4 to 21-31. The latest wave is the Delta strain which accrues 31 new mutations to become highly prevalent. Interestingly, these new mutations in Delta strain emerge in multiple stages with each stage driven by 6 - 12 coding mutations that form a fitness group. In short, the evolution of SARS-CoV-2 from the oldest to the youngest wave, and from the earlier to the later stages of the Delta wave, is a process of acceleration with more and more mutations. The global increase in the viral population size (M(t), at time t) and the mutation accumulation (R(t)) may have indeed triggered the runaway evolution in late 2020, leading to the highly evolved Alpha and then Delta strain. To suppress the pandemic, it is crucial to break the positive feedback loop between M(t) and R(t), neither of which has yet to be effectively dampened by late 2021. New waves beyond Delta, hence, should not be surprising.

evolutionary biology↗

Construct a variable-length fragment library for de novo protein structure prediction

Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this work, we developed a variable-length fragment library (VFlib). In VFlib, a master structure database was first constructed from the Protein Data Bank through sequence clustering. The Hidden Markov Model (HMM) profile of each protein in the master structure database was generated by HHsuite, and the secondary structure of each protein was calculated by DSSP. For the query sequence, the HMM-profile was first constructed. Then, variable-length fragments were retrieved from the master structure database through dynamically variable-length profile-profile comparison. A complete method for chopping the query HMM-profile during this process was proposed to obtain fragments with increased diversity. Finally, secondary structure information was used to further screen the retrieved fragments to generate the final fragment library of specific query sequence. The experimental results obtained with a set of 120 nonredundant proteins showed that the global precision and coverage of the fragment library generated by VFlib were 55.04% and 94.95% at the RMSD cutoff of 1.5 [A], respectively. Compared to the benchmark method of NNMake, the global precision of our fragment library had increased by 62.89% with equivalent coverage. Furthermore, the fragments generated by VFlib and NNMake were used to predict structure models through fragment assembly. Controlled experimental results demonstrated that the average TM-score of VFlib was 16.00% higher than that of NNMake.

bioinformatics↗

Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

Gene transcription and protein translation are two key steps of the "central dogma". It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose Ribosome Calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e., context-dependent coding transcripts, CDCTs), suggesting that, contrary to conventional wisdom, a transcripts coding ability should be modeled as a continuous spectrum with a context-dependent nature.

bioinformatics↗