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Biology subjects

Niu, P.

Publications and source records attributed to Niu, P..

7 recordsLinked to original sources

Foxp4+ Mandibular Skeletal Stem Cells Orchestrate Bone/Tooth Development and Regeneration

The identity of stem/progenitor cells that drive mandible/tooth development and regeneration remains poorly understood. Here, we show that cranial neural crest-derived Foxp4+ mesenchymal cells condense to form Meckels cartilage, and simultaneously promote mandible development and tooth germ formation. In postnatal mandibles, Foxp4+ cells localize in the endosteum, periosteum and dental pulp, which undergo osteogenesis and odontogenesis under steady state, as well as chondrogenesis during fracture repair. Importantly, transplantation of embryonic or adult Foxp4+CD200+CD105- mandibular skeletal stem cells (mdSSCs) generate ectopic bone and tooth. In contrast, genetic ablation of Foxp4+ cells severely impairs mandible/tooth development and delays fracture healing. Mechanistically, we find that primary cilia critically regulate mdSSC maintenance and differentiation into bone and tooth. FOXP4+PDPN+CADM1+ mdSSCs also exist in adult human mandible, which generate ectopic bone and intact tooth upon transplantation into immunodeficient mice. Taken together, we identify Foxp4+ mdSSCs with potential therapeutic applications in regenerating mandibular bone and tooth.

cell biology↗

Rumen transfaunation between low- and high-methane-yielding dairy cows reveals asymmetric microbiome reconstitution patterns: a pilot study

BackgroundThis study investigated rumen microbiome reconstitution and methane (CH4) emissions following a complete exchange of rumen contents between low- and high-CH4-yielding Norwegian Red dairy cows. Twenty cows were screened for CH4 yield, and two low and two high emitters were selected for rumen cannulation and content swap. Total rumen contents were swapped after complete evacuation and washing of both the rumen and omasum. Rumen samples were collected twice in weeks -1, 1, 3, and 7 for fermentation analysis, metagenomics, and metaproteomics, and at week 8 CH4 production was measured. ResultsPrior to the swap, low and high emitters produced 21.2 {+/-} 0.7 and 26.3 {+/-} 1.4 g CH4/kg dry-matter intake (DMI), respectively. Eight weeks after swap, CH4 yields were 12.7 {+/-} 0.3 and 28.9 {+/-} 0.3 g CH4/kg DMI, respectively, showing that the CH4 phenotype of each cow was maintained. Analysis of metagenome-derived 16S rRNA gene sequences showed that low emitters gradually re-established their original microbial community, whereas high emitters retained donor-like microbiota. Metaproteomic mapping suggested higher expression of Prevotella-associated succinate-propionate pathway enzymes in low emitters at week 7, though these differences were modest. ConclusionThese findings suggest that host factors influence CH4 output and microbial reconstitution, with low emitters restoring their native microbiome while high emitters retained a donor-associated community yet continued to emit high CH4. Results should be interpreted with caution given the small sample size (n = 2 per phenotype) and require confirmation in larger studies. ImportanceReducing enteric methane from cattle requires understanding whether the rumen microbiome or the host animal is the primary driver of methane output. We exchanged the entire rumen contents between low- and high-methane-yielding dairy cows and measured methane production alongside metagenomic and metaproteomic profiling over two months. Despite receiving each others microbiomes, each cows methane phenotype persisted--low emitters stayed low and high emitters stayed high. Microbiome reconstitution was asymmetric: low emitters restored their original microbial community, while high emitters retained the donor microbiota. Methanogen communities did not differ between phenotypes, pointing to host-level rather than microbial-level control of methane yield. These pilot findings suggest that breeding for favorable host traits may be essential for lasting methane reduction, and that microbiome transfer alone is unlikely to shift an animals methane phenotype. Larger studies are needed to confirm these observations.

microbiology↗

Intricate microbiome differences observed in lactating cows across methane intensity phenotypes

Methane emissions from ruminants can be expressed through several metrics as total production, yield, or intensity, each reflecting distinct aspects of energy utilization. Among these, methane intensity defined as grams of methane emitted per kilograms of energy-corrected milk, directly links emissions to productive efficiency; however, the microbial mechanisms underlying variation in this trait remain unclear. Here, we applied genome-resolved metagenomics and metatranscriptomics to characterize rumen microbial identity, functional potential, and transcriptional activity in lactating cows differing in methane intensity while sharing breed and diet. Microbial community composition and diversity were comparable across phenotypes. However, rumen microbial gene expression revealed marked functional divergence. The rumen content of low-methane intensity cows showed enriched transcription of fructan-degrading carbohydrate-active enzymes and butyrate-forming pathways, primarily encoded by RUG440 (Atopobiaceae) and Sodaliphilus-affiliated metagenome-assembled genomes. These functions suggest a fructan-butyrate metabolic axis supported by potential cross-feeding between primary degraders and butyrate producers. Conversely, the high-methane intensity rumen exhibited greater transcription of pectin-degrading carbohydrate-active enzymes, mainly carried by Prevotella metagenome-assembled genomes, suggesting methyl-ester hydrolysis and methanol release. Despite higher methanogenesis transcript levels in high-methane intensity cows, total methane production did not differ between groups. Together, these findings reveal two contrasting functional configurations of the rumen microbiome in Norwegian Red dairy cattle: a fructan-butyrate-oriented metabolism in low-methane intensity cows and a pectin-methanol-oriented metabolism in high-methane intensity counterparts. This study provides genome-resolved, multi-omic evidence that microbial carbohydrate specialization and fermentation routing contribute to methane intensity phenotypes in dairy cows, offering mechanistic insights for improving ruminant climate efficiency.

microbiology↗

Sensitivity analysis of genome-scale metabolic flux prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with Transcription Factor (TF)-gene regulatory network (TRN), where the TRN is modeled via Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction considering potential model uncertainty in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty when learning from given transcriptomic expression data and analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yield of interest. The obtained sensitivity analyses can provide a useful guidance for Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and effectively achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed simulation experiments to demonstrate the effectiveness of our developed sensitivity analysis strategy and its potential to effectively guide OED. ACM Reference FormatPuhua Niu, Maria J. Soto, Shuai Huang, Byung-Jun Yoon, Edward R. Dougherty,, Francis J. Alexander, Ian Blaby, Xiaoning Qian. 2018. Sensitivity analysis of genome-scale metabolic flux prediction. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (CNB-MAC 2022). ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX

bioinformatics↗

TRIMER: Transcription Regulation Integrated with MEtabolic Regulation

Advances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMERs applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.

bioinformatics↗

Orthogonal genome-wide screenings in bat cells identify MTHFD1 as a target of broad antiviral therapy

Bats are responsible for the zoonotic transmission of several major viral diseases including the 2003 SARS outbreak and the ongoing COVID-19 pandemic. While bat genomic sequencing studies have revealed characteristic adaptations of the innate immune system, functional genomic studies are urgently needed to provide a foundation for the molecular dissection of the tolerance of viral infections in bats. Here we report the establishment and screening of genome-wide RNAi library and CRISPR library for the model megabat, Pteropus Alecto. We used the complementary RNAi and CRISPR libraries to interrogate Pteropus Alecto cells for infection with two different viruses, mumps virus and Influenza A virus, respectively. Screening results converged on the endocytosis pathway and the protein secretory pathway as required for both viral infections. Additionally, we revealed a general dependence of the C-1-tetrahydrofolate synthase gene, MTHFD1, for viral replication in bat cells as well as in human cells. MTHFD1 inhibitor carolacton potently blocked replication of several RNA viruses including SARS-CoV-2. Our studies provide a resource for systematic inquiry into the genetic underpinnings of bat biology and a potential target for developing broad spectrum antiviral therapy.

microbiology↗

Mutations, Recombination and Insertion in the Evolution of 2019-nCoV

BackgroundThe 2019 novel coronavirus (2019-nCoV or SARS-CoV-2) has spread more rapidly than any other betacoronavirus including SARS-CoV and MERS-CoV. However, the mechanisms responsible for infection and molecular evolution of this virus remained unclear. MethodsWe collected and analyzed 120 genomic sequences of 2019-nCoV including 11 novel genomes from patients in China. Through comprehensive analysis of the available genome sequences of 2019-nCoV strains, we have tracked multiple inheritable SNPs and determined the evolution of 2019-nCoV relative to other coronaviruses. ResultsSystematic analysis of 120 genomic sequences of 2019-nCoV revealed co-circulation of two genetic subgroups with distinct SNPs markers, which can be used to trace the 2019-nCoV spreading pathways to different regions and countries. Although 2019-nCoV, human and bat SARS-CoV share high homologous in overall genome structures, they evolved into two distinct groups with different receptor entry specificities through potential recombination in the receptor binding regions. In addition, 2019-nCoV has a unique four amino acid insertion between S1 and S2 domains of the spike protein, which created a potential furin or TMPRSS2 cleavage site. ConclusionsOur studies provided comprehensive insights into the evolution and spread of the 2019-nCoV. Our results provided evidence suggesting that 2019-nCoV may increase its infectivity through the receptor binding domain recombination and a cleavage site insertion. One Sentence SummaryNovel 2019-nCoV sequences revealed the evolution and specificity of betacoronavirus with possible mechanisms of enhanced infectivity.

microbiology↗