bioRxiv Science⌕ Search

Biology subjects

Cheng, Z.

Publications and source records attributed to Cheng, Z..

At least 55 records · Page 3Linked to original sources

Genome Mining and Characterization of a Heme-Dependent Enzyme Catalyzing Intermolecular Nitrogen-Nitrogen Bond Formation in Hydrazinosuccinic Acid Biosynthesis

Nitrogen-nitrogen (N-N) bond-forming enzymes are rare but play vital roles in both primary and secondary metabolism. Guided by a nitric oxide synthase (NOS)-based genome mining strategy, we report the discovery and characterization of a new heme-dependent enzyme system that catalyzes intermolecular N-N bond formation. Using both in vivo and in vitro reconstitution approaches, we demonstrated that a protein complex, comprising a heme enzyme and a 2[4Fe-4S] ferredoxin partner, mediates the coupling of the -amine group of L-aspartate with inorganic nitrogen oxide species, such as nitrite or nitric oxide, to generate hydrazinosuccinic acid, a key biosynthetic precursor in several natural product pathways. Structural modeling and site-directed mutagenesis suggest a plausible catalytic mechanism involving the formation of a reactive nitrogen intermediate, potentially a heme-bound nitrene species. These findings reveal a new family of N-N bond-forming biocatalysts that leverage inorganic nitrogen sources, offering valuable tools for genome mining and the synthetic biology.

biochemistry↗

Deconvolution of multiplexed peptidoform mass spectra enables high-resolution profiling of complex protein modification patterns

Combinatorial patterns of post-translational modifications (PTMs), like the histone code, play critical roles in regulating protein functionality. However, in bottom-up proteomics, isobaric modification peptidoforms (IMPs) frequently co-elute and co-fragment during liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), resulting in multiplexed mass spectra that cannot be properly analyzed by conventional protein identification and quantification methods. Existing solutions are either limited to highly narrow scenarios or unavailable for public use. Here, we introduce PTMdecoder, a robust and versatile software tool for discriminating and quantifying IMPs by deconvoluting their multiplexed MS/MS spectra and ion chromatograms without requiring a spectral library of individual peptidoforms. Tested on 34 samples of synthesized IMPs, PTMdecoder achieved an overall sensitivity of 95.7% for IMP identification and root mean square errors mostly <0.05 for quantification. In human histone samples, it detected and quantified 36.7-44.8% more IMPs than conventional methods, providing a higher-resolution view of histone modification patterns.

bioinformatics↗

Extracellular Ca2+-Sensing Receptor (CaSR) Regulates Hypothalamic Function to Control Energy and Skeletal Metabolism in Mice

Introductory paragraphThe extracellular Calcium-sensing receptor (CaSR) regulates cellular responsiveness to physiological changes in ionized calcium (Ca2+) concentrations. The CaSR is expressed in the brain, including in hypothalamic growth hormone stimulating GHRH and anorectic POMC neurons that control growth and energy homeostasis. We embryonically deleted the Casr gene in neurons to create NeuronCaSR-/- mice to delineate the role of this receptor in regulating growth, skeletal development, and energy metabolism. NeuronCaSR-/- mice had reduced size, weight and bone mass compared to littermate controls, with a dysregulated growth hormone axis. They also showed increased adiposity and circulating leptin levels, leptin resistance, and decreased glucose tolerance, along with reduced expression of the anorectic precursor peptide POMC and secondary increases in the expression of the anorectic peptide AgRP in the hypothalamus of NeuronCaSR-/- mice. Knockdown of CaSR in adult mice specifically in the hypothalamic arcuate nucleus, where GHRH, POMC and AgRP neurons reside, also resulted in increased body weight, adiposity, leptin resistance, and glucose intolerance, and reduced bone mass. Together these data suggest that neuronal CaSR critically regulates energy and skeletal metabolism and body growth by modulating hypothalamic function, representing a new paradigm for central integration of calcaemic activities with body function.

physiology↗

A Tandem Repeat Atlas for the Genome of Inbred Mouse Strains: A Genetic Variation Resource

Tandem repeats (TRs) are a significant source of genetic variation in the human population; and TR alleles are responsible for over 60 human genetic diseases and for inter-individual differences in many biomedical traits. Therefore, we utilized long-read sequencing and state of the art computational programs to produce a database with 2,528,854 TRs covering 39 inbred mouse strains. As in humans, murine TRs are abundant and were primarily located in intergenic regions. However, there were important species differences: murine TRs did not have the extensive number of repeat expansions like those associated with human repeat expansion diseases and they were not associated with transposable elements. We demonstrate by analysis of two biomedical phenotypes, which were identified over 40 years ago, that this TR database can enhance our ability to characterize the genetic basis for trait differences among the inbred strains.

genetics↗

Increasing Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival

BackgroundCancer progression is often accompanied by dedifferentiation and acquisition of stem cell-like properties (stemness). In prostate cancer (PCa), lineage plasticity and therapy resistance remain major clinical challenges, yet a unified quantitative transcriptomic framework connecting stemness, androgen receptor (AR) signaling, castration resistance, and disease progression across the PCa continuum is lacking. MethodsWe integrated 87,339 transcriptomic profiles from 33 preclinical and clinical datasets spanning the PCa continuum from normal prostate and treatment-naive primary PCa (Pri-PCa) to PCa treated with neoadjuvant ADT (nADT) and metastatic castration-resistant PCa (mCRPC), with single-cell RNA-seq analyses encompassing 115,197 cells. Cancer stemness was quantified using a transcriptome-derived mRNA-based Stemness Index (mRNAsi; hereafter Stemness), and a 12-gene PCa-Stem signature was developed to capture PCa-specific stemness. Stemness, PCa-Stem, canonical AR activity (c_AR-A), castration-reprogrammed AR activity (cr_AR-A), RB1-loss, PTEN-loss, and MYC activity signatures were quantified across cohorts. Functional validation included MYC inhibition and representative PCa-Stem signature gene depletion in PCa models. Clinical prognostic significance was evaluated in independent patient cohorts. ResultsThe Stemness score and c_AR-A increased concordantly during early prostate tumorigenesis but diverged with PCa progression: as Gleason grade increased, c_AR-A declined while Stemness continually increased. mCRPC exhibited the highest Stemness and lowest c_AR-A, a pattern recapitulated in Pten/Rb1/Trp53-deficient mouse models. Global Stemness increased progressively across the PCa continuum, was enriched in aggressive PAM50-LumB and PCS1 subtypes, associated with proliferative and lineage plasticity programs, and predicted poor patient survival. The newly derived 12-gene PCa-Stem signature provided a PCa-specific molecular representation of Stemness and tracked disease progression and poor patient survival. Network analyses identified a coordinated mitotic regulatory program linking MYC activity, RB1-loss, cr_AR-A, and the PCa-Stem signature. Spatial and single-cell transcriptomic analyses localized the PCa-Stem program to lineage plasticity-related epithelial cells and demonstrated progressive expansion of PCa-Stem epithelial cells during PCa progression. Functional perturbation of representative PCa-Stem signature genes, as well as genetic and pharmacological MYC inhibition, consistently suppressed Stemness-associated phenotypes in diverse PCa models. ConclusionsCancer Stemness quantitatively captures PCa aggressiveness, lineage plasticity, treatment resistance, disease progression, and poor patient survival. cr_AR-A, RB1 loss, and MYC activation cooperate to reinforce the high-Stemness state and therapy resistance in mCRPC. Collectively, our work establishes a trajectory-integrated transcriptomic framework defining cancer Stemness as a quantifiable molecular and clinical determinant of PCa aggressiveness, lineage plasticity, disease progression, therapy resistance and patient survival.

cancer biology↗

DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases led to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieved much higher sequence coverages. DiNovo also showed great potential as a powerful complement or alternative to database search for peptide identification with quality control.

bioinformatics↗

Adaptive pangenomic remodeling in the Azolla cyanobiont amid a transient microbiome

Plants fix nitrogen in concert with diverse microbial symbionts, often recruiting them from the surrounding environment each generation. Vertical transmission of a microbial symbiont from parent to offspring can produce extreme evolutionary consequences, including metabolic codependence, genome reduction, and synchronized life cycles. One of the few examples of vertical transmission of N-fixing symbionts occurs in Azolla ferns, which maintain an obligate mutualism with the cyanobacterium Trichormus azollae--but the genomic consequences of this interaction, and whether the symbiosis involves other vertically transmitted microbial partners, are currently unknown. We generated high-coverage metagenomes across the genus Azolla and reconstructed metagenome assembled genomes to investigate whether a core microbiome exists within Azolla leaf cavities, and how the genomes of T. azollae diverged from their free-living relatives. Our results suggest that T. azollae is the only consistent symbiont across all Azolla accessions, and that other bacterial groups are transient or facultative associates. Pangenomic analyses of T. azollae indicate extreme pseudogenization and gene loss compared to free-living relatives--especially in defensive, stress-tolerance, and secondary metabolite pathways--yet the key functions of nitrogen fixation and photosynthesis remain intact. Additionally, differential codon bias and intensified (rather than relaxed) selection on photosynthesis, intracellular transport, and carbohydrate metabolism genes suggest ongoing evolution in response to the unique conditions within Azolla leaf cavities. These findings highlight how genome erosion and shifting selection pressures jointly drive the evolution of this unique mutualism, while broadening the taxonomic scope of genomic studies on vertically transmitted symbioses.

microbiology↗

Circular RNA Circ-Cdr1as modulates Macrophage phenotype and Cardiac Reparative Function by Circ-Cdr1as-miR-7-Klf4 pathway

BackgroundMechanisms of macrophage switching from pro-inflammatory to anti-inflammatory phenotypes are not well understood. Circular RNAs (circRNAs), a new class of non-coding RNAs, are implicated in immune modulation. We recently identified circ-cdr1as as a regulator of macrophage phenotype in bone marrow derived macrophages (BMDM), however, their role in immunomodulation during cardiovascular injury remains unknown. MethodsCell-specific expression levels of circ-cdr1as was determined in a mouse hearts post-myocardial infarction (MI). Circ-cdr1as was overexpressed in fluorescently labeled BMDMs and injected into the ischemic myocardium immediately following MI. Effect of AAV9-mediated systemic delivery of circ-Cdr1as on post-MI cardiac function and structure was determined. Downstream mechanisms were studied using gain and loss of function strategies. ResultsCardiac cell specific expression analysis showed significant downregulation of circ-cdr1as only in macrophages and cardiomyocytes. Overexpression of circ-cdr1as in BMDMs, injected into the ischemic myocardium retained their anti-inflammatory phenotype and significantly improved left ventricular (LV) functions and reduced infarct size. Systemic delivery of AAV9-circ-cdr1as showed similar cardiac reparative activity. Mechanistically, circ-cdr1as directly binds and sponge microRNA-7 and increases the expression of target KLF4. Loss and gain of function studies show that modulation of miR-7 and KLF recapitulates macrophage phenotypic changes. ConclusionsCirc-cdr1as plays a crucial role in regulating the anti-inflammatory phenotype of macrophages through modulation of miR-7 and its target gene KLF4. Therefore, circ-cdr1as holds potential as an anti-inflammatory regulator in tissue inflammation post-cardiac injury. Novelty and SignificanceO_ST_ABSWhat is Known?C_ST_ABSO_LIDespite continued research in elucidating mechanisms involved in cardiovascular disease and benefits of approved guideline-based therapies, the leading cause of deaths worldwide continues to be cardiovascular diseases (CVDs) with an increasing incidence of heart failure. C_LIO_LIAdvances in high-throughput RNA sequencing (RNA-seq) allowed the identification of novel transcripts such as microRNAs (miRNA), long non-coding RNAs (lncRNAs), and circular RNAs (circRNA). circular RNAs have recently emerged as promising candidates for targeted therapy due to their circular structure that confers resistance to exonucleases, their capability to regulate gene expression by modulating miRNA activity, sequester proteins by acting as protein sponges, C_LIO_LISeveral studies identified circRNAs to be differentially expressed following myocardial infarction (MI) and to play a role in immunity by contributing to the process of macrophage polarization, appropriate activation of macrophages when exposed to LPS, and inhibition of macrophage biogenesis. However, there are currently no published studies into the role of circular RNAs in the regulation of macrophage plasticity during cardiac injury. C_LI What New information Does This Article Contribute?O_LIWe provide evidence that circ-cdr1as expression is downregulated in the heart 3 days post MI and specifically in cardiomyocytes and macrophages. C_LIO_LIOur study provides promising evidence that overexpression of circ-cdr1as may be cardioprotective by reducing cardiomyocyte apoptosis, enhancing angiogenesis, limiting infarct size, increasing percentage of anti-inflammatory macrophages, and overall preserving post-MI cardiac function. C_LIO_LIMechanistically, we identified a reciprocal relationship between circ-cdr1as and miR-7 at 3 days post-MI and in naive, pro-, and anti-inflammatory macrophages indicating circ-cdr1as role as a miRNA sponge. This suggests that circ-cdr1as/miR-7/Klf4 play a crucial role in cardiac injury and macrophage phenotype. C_LI

cell biology↗

Microglial plasticity governed by state-specific enhancer landscapes

Single-cell transcriptomic studies have identified distinct microglial subpopulations with shared and divergent gene signatures across development, aging and disease. Whether these microglial subsets represent ontogenically separate lineages of cells, or they are manifestations of plastic changes of microglial states downstream of some converging signals is unknown. Furthermore, despite the well-established role of enhancer landscapes underlying the identity of microglia, to what extent histone modifications and DNA methylation regulate microglial state switches at enhancers have not been defined. Here, using genetic fate mapping, we demonstrate the common embryonic origin of proliferative-region-associated microglia (PAM) enriched in developing white matter, and track their dynamic transitions into disease-associated microglia (DAM) and white matter-associated microglia (WAM) states in disease and aging contexts, respectively. This study links spatiotemporally discrete microglial states through their transcriptomic and epigenomic plasticity, while revealing state-specific histone modification profiles that govern state switches in health and disease.

neuroscience↗

A Murine Database of Structural Variants Enables the Genetic Architecture of a Spontaneous Murine Lymphoma to be Characterized

A more complete map of the pattern of genetic variation among inbred mouse strains is essential for characterizing the genetic architecture of the many available mouse genetic models of important biomedical traits. Although structural variants (SVs) are a major component of genetic variation, they have not been adequately characterized among inbred strains due to methodological limitations. To address this, we generated high-quality long-read sequencing data for 40 inbred strains; and designed a pipeline to optimally identify and validate different types of SVs. This generated a database for 40 inbred strains with 573,191SVs, which included 10,815 duplications and 2,115 inversions, which also has 70 million SNPs and 7.5 million insertions/deletions. Analysis of this SV database identified an SV that is one component of a bi-genic model for lymphoma susceptibility in SJL mice, which provides mechanistic insight into the genetic basis for susceptibility to murine (and potentially human) lymphomas.

genetics↗

Enzyme-independent functions of HDAC3 in the adult heart

The cardioprotective effects of histone deacetylase (HDAC) inhibitors (HDIs) are at odds with the deleterious effects of HDAC depletion. Here, we use HDAC3 as a prototype HDAC to address this contradiction. We show that adult-onset cardiac-specific depletion of HDAC3 in mice causes cardiac hypertrophy and contractile dysfunction on a high-fat diet (HFD), excluding developmental disruption as a major reason for the contradiction. Genetically abolishing HDAC3 enzymatic activity without affecting its protein level does not cause cardiac dysfunction on HFD. HDAC3 depletion causes robust downregulation of lipid oxidation/bioenergetic genes and upregulation of antioxidant/anti-apoptotic genes. In contrast, HDAC3 enzyme activity abolishment causes much milder changes in far fewer genes. The abnormal gene expression is cardiomyocyte-autonomous and can be rescued by an enzyme-dead HDAC3 mutant but not by an HDAC3 mutant ({Delta}33-70) that lacks interaction with the nuclear-envelope protein lamina-associated polypeptide 2{beta} (LAP2{beta}). Tethering LAP2{beta} to the HDAC3 {Delta}33-70 mutant restored its ability to rescue gene expression. Finally, HDAC3 depletion, not loss of HDAC3 enzymatic activity, exacerbates cardiac contractile functions upon aortic constriction. These results suggest that the cardiac function of HDAC3 in adults is not attributable to its enzyme activity, which has implications for understanding the cardioprotective effects of HDIs.

physiology↗

Direct Feature Identification from Raman Spectra and Precise Data-driven Classification of Phytopathogens at Single Conidium-Species Level

Conidia can cause outbreaks of fungal plant diseases, resulting in significant losses in crop yield and economy. Traditional diagnosis of phytopathogenic conidia based on morphology and molecular biology is time-consuming, labor-intensive, and often fails to differentiate fungal conidia. To overcome these challenges, a new classification approach was developed in this study by integrating Raman spectroscopy with data-driven modeling. Eight fungal species were selected and characterized using Raman spectra. Three characteristic Raman wavenumbers at 1003 to 1005 cm-1, 1153 to 1157 cm-1, and 1515 to 1522 cm-1 shared a consistent pattern across species and could be attributed to carotenoids. Clustering of the Raman spectra using principal component analysis (PCA) showed substantial overlap, indicating inaccurate classification of conidia. Three data-driven models, support vector machines (SVMs), decision trees (DTs), and eXtreme Gradient Boosting Forest (XGBoost) were trained with three categories of features (number of peaks, maximum peak, and curve roughness) identified within eight characteristic wavenumber ranges, The optimal SVM, DT, and XGBoost determined by hyperparameter tuning achieved prediction precision of 0.88, 0.88, and 0.96, respectively. PCA-XGBoost trained by feeding principal components of PCA to XGBoost achieved prediction precision of 0.94, suggesting that features extracted from the raw datasets outperformed those extracted with PCA in terms of data-driven classification. This study has demonstrated the great potential of Raman spectroscopy combined with data-driven modeling for classification of phytopathogenic conidia.

microbiology↗

D-lactate derived from intestinal bacteria drives lysine D-lactylation to modulate transcription in liver cells

Emerging evidence indicates that the accumulated D-lactate (D-la) from intestinal bacteria induces diverse effects in human intestinal injury diseases. However, its regulatory mechanism and functional consequence remain unclear. Here, we show that the secreted D-la induces lysine D-lactylation (K-Dla) to modulate transcription in host cells. We uncover K-Dla in HepG2 cells stimulated by D-la from E. coli secretions. Then, we confirm the occurrence of D-la-driven K-Dla by four orthogonal approaches. We further validate that the existence of K-Dla in organisms by detecting the mouse liver and human intestinal tissue. Besides, we demonstrate a SCOT1-catalyzed biosynthetic pathway of D-lactyl-CoA, serving as a key donor for K-Dla. Next, we identify the K-Dla landscape with 2895 sites and further suggest the regulation of histone K-Dla in transcription by MNase ChIP-seq and RNA-seq assays. Finally, we show the potential effect of K-Dla on inhibiting the hepatocellular carcinoma cell proliferation. In summary, we report that the secreted D-la can induce K-Dla to regulate transcription, providing a new insight into the function of D-la secreted by gut microbiota and the effect of protein chemical modifications induced by bacterial secretions.

molecular biology↗

YTHDF1 Facilitates Lung Adenocarcinoma Progression via Promotion of EEF1G Translation in a m6A-Dependent Manner

Lung adenocarcinoma (LUAD) is a malignant tumor with high morbidity and mortality worldwide, and overall survival rates for LUAD patients remain unimproved. RNA modification is a key process in post-transcriptional gene regulation in epigenetics, with N6-methyladenosine (m6A) being a common RNA modification. The molecular mechanisms of LUAD are unclear, but evidence suggests that m6A RNA methylation plays a significant role. This study aimed to clarify the role of YTHDF1 in LUAD development and pathogenesis. These findings confirmed that YTHDF1, a m6A reader protein, is highly expressed in LUAD tissues and is correlated with tumor differentiation and TNM stage. The results of functional loss experiments in LUAD cell lines revealed that downregulating YTHDF1 inhibits proliferation, migration, and invasion and induces apoptosis, with opposite effects observed upon YTHDF1 upregulation. In vivo, YTHDF1 knockout suppressed LUAD xenograft growth. RNA-seq, MeRIP-seq, RIP-seq, and bioinformatics analyses identified EEF1G as a downstream target of YTHDF1 in LUAD, and high expression of EEF1G was confirmed. The interaction between YTHDF1 and EEF1G was validated through RIP-qPCR, Co-IP and Co-IF assays. The overexpression of EEF1G in LUAD cells partially counteracts the tumor suppression induced by YTHDF1 silencing, and the knockdown of EEF1G has the opposite effect, further confirming the regulatory relationship. In summary, this study describes a novel YTHDF1/EEF1G regulatory pathway in which YTHDF1 promotes LUAD progression by recognizing and binding to the m6A-modified mRNA of EEF1G, accelerating its translation, suggesting that YTHDF1 may be a potential biomarker and therapeutic target.

cancer biology↗

Boundaries in the eyes: measure event segmentation during naturalistic video watching using eye tracking

During naturalistic information processing, individuals spontaneously segment their continuous experiences into discrete events, a phenomenon known as event segmentation. Traditional methods for assessing this process, which include subjective reports and neuroimaging techniques, often disrupt real-time segmentation or are costly and time intensive. Our study investigated the potential of measuring event segmentation by recording and analyzing eye movements while participants view naturalistic videos. We collected eye movement data from healthy young adults as they watched commercial films (N=104), or online Science, Technology, Engineering, and Mathematics (STEM) educational courses (N=44). We analyzed changes in pupil size and eye movement speed near event boundaries and employed inter-subject correlation analysis (ISC) and hidden Markov models (HMM) to identify patterns indicative of event segmentation. We observed that both the speed of eye movements and pupil size dynamically responded to event boundaries, exhibiting heightened sensitivity to high-strength boundaries. Our analyses further revealed that event boundaries synchronized eye movements across participants. These boundaries, can be effectively identified by HMM, yielded higher within-event similarity values and aligned with human-annotated boundaries. Importantly, HMM-based event segmentation metrics responded to experimental manipulations and predicted learning outcomes. This study provided a comprehensive computational framework for measuring event segmentation using eye-tracking. With the widespread accessibility of low-cost eye-tracking devices, the ability to measure event segmentation from eye movement data promises to deepen our understanding of this process in diverse real-world settings.

neuroscience↗

Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in the App(NL-G-F) knock-in mouse model of the earliest phase of Alzheimer's disease

Genome-wide association studies (GWAS) have identified a transcriptional network of Alzheimers disease (AD) risk genes that are primarily expressed in microglia and are associated with AD pathology. However, traditional short-read sequencers have limited our ability to fully characterize how GWAS variants exert their effects on gene expression regulation or alternative splicing in response to the pathology, particularly resulting in inaccurate detection of splicing. To address this gap, we utilized long-read RNA-sequencing (RNA-seq) in the AppNL-G-F knock-in mouse model to identify changes in splicing and novel transcript isoforms in response to amyloid-{beta}. We show that long-read RNA-seq can recapitulate the expected induction of microglial expressed risk genes such as Trem2 in response to amyloid-{beta} at 9 months of age associated with ageing-dependent deficiencies in spatial short-term memory in the AppNL-G-F knock-in mice. Our results not only identified novel splicing events and transcript isoform abundance in genes associated with AD, but also revealed the complex regulation of gene expression through splicing in response to amyloid plaques. Surprisingly, the regulation of alternative splicing in response to amyloid was seen in genes previously not identified as AD risk genes, expressed in microglia, neurons and oligodendrocytes, and included genes such as Syngr1 that modulate synaptic physiology. We saw alternative splicing in genes such as Ctsa, Clta, Dennd2a, Irf9 and Smad4 in mice in response to amyloid, and the orthologues of these genes also showed transcript usage changes in human AD brains. Our data suggests a model whereby induction of AD risk gene expression associated with microglial proliferation and activation is concomitant with alternative splicing in a different class of genes expressed by microglia and neurons, which act to adapt or preserve synaptic activity in response to amyloid-{beta} during early stages of the disease. Our study provides new insights into the mechanisms and effects of the regulation of genes associated with amyloid pathology, which may ultimately enable better disease diagnosis, and improved tracking of disease progression. Additionally, our findings identify new therapeutic avenues for treatment of AD.

genetics↗

Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications

Natural products are an excellent source of therapeutics and are often discovered through the process of genome mining, where genomes are analyzed by bioinformatic tools to determine if they have the biosynthetic capacity to produce novel or active compounds. Recently, several tools have been reported for predicting natural product bioactivities from the sequence of the biosynthetic gene clusters that produce them. These tools have the potential to accelerate the rate of natural product drug discovery by enabling the prioritization of novel biosynthetic gene clusters that are more likely to produce compounds with therapeutically relevant bioactivities. However, these tools are severely limited by a lack of training data, specifically data pairing biosynthetic gene clusters with activity labels for their products. There are many reports of natural product biosynthetic gene clusters and bioactivities in the literature that are not included in existing databases. Manual curation of these data is time consuming and inefficient. Recent developments in large language models and the chatbot interfaces built on top of them have enabled automatic data extraction from text, including scientific publications. We investigated how accurate ChatGPT is at extracting the necessary data for training models that predict natural product activity from biosynthetic gene clusters. We found that ChatGPT did well at determining if a paper described discovery of a natural product and extracting information about the products bioactivity. ChatGPT did not perform as well at extracting accession numbers for the biosynthetic gene cluster or producers genome although using an altered prompt improved accuracy.

bioinformatics↗

Building Predictive Understanding of Microbial Ecology by Bridging Microbial Growth Kinetics and Microbial Population Dynamics

Modeling microbial communities can provide predictive insights into microbial ecology, but current modeling approaches suffer from inherent limitations. In this study, a novel modeling approach was proposed to address those limitations based on the intrinsic connection between the growth kinetics of guilds and the dynamics of individual microbial populations. To implement the modeling approach, 466 samples from four full-scale activated sludge systems were retrieved from the literature. The raw samples were processed using a data transformation method that not only increased the dataset size by three times but also enabled quantification of population dynamics. Most of the 42 family-level core populations showed overall dynamics close to zero within the sampling period, explaining their resilience to environmental perturbation. Bayesian networks built with environmental factors, perturbation, historical abundance, population dynamics, and mechanistically derived microbial kinetic parameters classified the core populations into heterotrophic and autotrophic guilds. Topological data analysis was applied to identify keystone populations and their time-dependent interactions with other populations. The data-driven inferences were validated directly using the Microbial Database for Activated Sludge (MiDAS) and indirectly by predicting population abundance and community structure using artificial neural networks. The Bray-Curtis similarity between predicted and observed communities was significantly higher with microbial kinetic parameters than without parameters (0.70 vs. 0.66), demonstrating the accuracy of the modeling approach. Implemented based on engineered systems, this modeling approach can be generalized to natural systems to gain predictive understandings of microbial ecology.

microbiology↗