Biology subjects

Ning, K.

Publications and source records attributed to Ning, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

A precise IDMS-based method for absolute quantification of phytohemagglutinin, a major antinutritional component in common bean

Phytohemagglutinin (PHA), a natural tetramer comprising PHA-E and PHA-L subunits that preferentially bind to red and white blood cells, respectively, constitutes a significant antinutritional and allergenic factor in common bean seeds. The accurate measurement of PHA content is a prerequisite for ensuring food safety inspections and facilitating genetic improvements in common bean cultivars with reduced PHA levels. Currently, mainstream methods for PHA quantification involve hemagglutination assays and immunodetection, but these methods often require fresh animal blood and lack specificity and accuracy. In this study, we present a novel LC-MS/MS-based method for PHA quantification, leveraging the advantages of isotope dilution mass spectrometry (IDMS). Two signature peptides each for PHA-E and PHA-L, along with a common signature peptide, were identified and employed for quantification, allowing differentiation between PHA-E and PHA-L subunits. The incorporation of amino acid analysis-isotope dilution mass spectrometry (AAA-IDMS) enabled precise determination of the synthetic signature peptides purity during measurement, enhancing metrological accuracy. In addition, the TCA-acetone protocol was established as the optimized method for total protein extraction from dry bean seeds. Quantitative analysis of PHA-E and PHA-L subunits in six common bean varieties using the developed method demonstrated excellent linearity (r > 0.999), sensitivity (limit of detection and quantitation as low as 2.32 ng/mg and 7.73 ng/mg, respectively), recovery (94.18-104.47%), and repeatability (relative standard deviation < 3.45%). This method has the potential to serve as a standard for measuring PHA contents in common beans and other agricultural products containing PHA.

biochemistry↗

bioRxiv2023

Deciphering the Biosynthetic Potential of Microbial Genomes Using a BGC Language Processing Neural Network Model

Microbial secondary metabolites are usually synthesized by colocalized genes termed biosynthetic gene clusters (BGCs). A large portion of BGCs remain undiscovered in microbial genomes and metagenomes, representing a pressing challenge in unlocking the full potential of natural product diversity. In this work, we propose BGC-Prophet, a language model based on the transformer encoder that captures the distant location-dependent relationships among biosynthetic genes, allows accurately and efficiently identifies known BGCs and extrapolates novel BGCs among the microbial universe. BGC-Prophet is the first ultrahigh-throughput (UHT) method that is several orders of magnitude faster than existing tools such as DeepBGC, enabling pan-phylogenetic screening and whole-metagenome screening of BGCs. By analyzing 85,203 genomes and 9,428 metagenomes, new insights have been obtained about the diversity of BGCs on genomes from the majority of bacterial and archaeal lineages. The profound enrichment of BGCs in microbes after important geological events have been revealed: Both the Great Oxidation and Cambrian Explosion events led to a surge in BGC diversity and abundance, particularly in polyketides. These findings suggest that it is a general but constantly evolving approach for microbes to produce secondary metabolites for their adaptation in the changing environment. Taken together, BGC-Prophet enables accurate and fast detection of BGCs on a large scale, holds great promise for expanding BGC knowledge, and sheds light on the evolutionary patterns of BGCs for possible applications in synthetic biology. HighlightsO_LIBGC-Prophet shows superior performance to existing tools in terms of accuracy and speed. C_LIO_LIBGC-Prophet is the first ultrahigh-throughput (UHT) method that enables pan-phylogenetic screening and whole-metagenome screening of BGCs. C_LIO_LIBGC-Prophet builds the comprehensive profile of BGCs on 85,203 genomes and 9,428 metagenomes from the majority of bacterial and archaeal lineages. C_LIO_LIBGC-Prophet reveals the profound enrichment pattern of BGCs after important geological events. C_LI

bioinformatics↗

bioRxiv2023

Deep Learning enables reliable and comprehensive profiling of invertible promoters in microbes

Invertible promoters (invertons) are regulatory elements found in bacteria, with inverted repeat sequences at both ends, leading to alternating changes in the expression of the regulated genes. Since invertons were present in more than 20% of bacterial genomes, while they regulated more than 5% of genes in these genomes, they are of pivotal importance for microbial functional dynamics especially when under stress. However, the prevalence of invertons, as well as the full spectrum of gene functions regulated by them, remain poorly understood. In this study, we developed DeepInverton, a deep learning model capable of accurately identifying novel inverton sequences without sequencing reads, which could profile inverton sequences from large genomic and metagenomic datasets. We conducted a pan-genomic and pan-metagenomic analysis of invertons on 68,969 bacterial genomes and 8,516 metagenome samples, resulting in a comprehensive overview of more than 200,000 nonredundant invertons and their regulated gene functional patterns. This result suggests that invertons, as a key player for bacterial adaptation to environmental stresses, are prevalent in bacterial genomes. Among the genomes analyzed, we observed a profound enrichment of invertons in pathogen such as Bordetella pertussis, and discovered a significant increase of inverton enrichment rates in strains associated with recent pertussis outbreaks, as well as novel evolving strains, unveiling a hidden link between the evolution of Bordetella pertussis and its inverton enrichment. We also utilized DeepInverton to explore inverton profiles mong human and marine metagenomes. Results revealed an unprecedented diversity of functional genes regulated by invertons, including antimicrobial resistance, biofilm formation and flagella, indicating their potential role in facilitating environmental adaptation. The in vitro experiments have confirmed the functions of tens of novel invertons that we have identified. Overall, we developed the DeepInverton model for exploration of invertons at unprecedented scale, which enabled our comprehensive profiling of invertons and their regulated genes. The comprehensive inverton profiles have deepen our understanding of invertons at pan-genome and metagenome scale, and could enabled a broad spectrum of inverton-related applications in microbial ecology and synthetic biology.

bioinformatics↗

bioRxiv2023

Identification of Host Restriction Factors Critical for Recombinant AAV Transduction of Polarized Human Airway Epithelium

Recombinant (r)AAV2.5T was selected from the directed evolution of an AAV capsid library in human airway epithelium (HAE). The capsid gene of rAAV2.5T is a chimera of the N-terminal unique coding sequence of AAV2 VP1 unique (VP1u) and the VP2- and VP3-coding sequence of AAV5 with a single amino acid mutation of A581T. We conducted two rounds of genome wide CRISPR gRNA library screening for host factors limiting rAAV2.5T transduction in HeLa S3 cells. The screen identified several genes that are critical for rAAV2.5T transduction in HeLa S3 cells, including previously reported genes KIAA0319L, TM9SF2, VPS51, and VPS54, as well as a novel gene WDR63. We verified the role of KIAA0319L and WDR63 in rAAV2.5T transduction of polarized HAE by utilizing CRISPR gene knockouts. Although KIAA0319L, a proteinaceous receptor for multiple AAV serotypes, played an essential role in rAAV2.5T transduction of polarized HAE either from apical or basolateral side, our findings demonstrated that the internalization of rAAV2.5T was independent of KIAA0319L. Importantly, we confirmed WDR63 is an important player in rAAV2.5T transduction of HAE, while not being involved in vector internalization and nuclear entry. Furthermore, we identified that the basal stem cells of HAE can be significantly transduced by rAAV2.5T. SignificanceThe essential steps of a successful gene delivery by rAAV include vector internalization, intracellular trafficking, nuclear import, uncoating, double-stranded (ds)DNA conversion, and transgene expression. rAAV2.5T has a chimeric capsid of AAV2 VP1u and AAV5 VP2 and VP3 with the mutation A581T. Our investigation revealed that KIAA0319L, the multiple AAV serotype receptor, is not essential for vector internalization but remains critical for efficient vector transduction to human airway epithelia. Additionally, we identified that a novel gene WDR63, whose cellular function is not well understood, plays an important role in vector transduction of human airway epithelia but not vector internalization and nuclear entry. Our study also discovered the substantial transduction potential of rAAV2.5T in basal stem cells of human airway epithelia, underscoring its utility in gene editing of human airways. Thus, the knowledge derived from this study holds promise for the advancement of gene therapy in the treatment of pulmonary genetic diseases.

microbiology↗

bioRxiv2023

Utilizing tumor microenvironment microbial profiles and host gene expressions for survival subtyping in diverse types of cancers

The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiome and patient survival. In this study, we aimed to decode this complex relationship by developing ASD-cancer (autoencoder-based subtypes detector for cancer), a semi-supervised deep learning framework that could extract survival-related features from tumor microbiome and transcriptome data, and identify patients survival subtypes. By using samples from The Cancer Genome Atlas database, we identified two statistically distinct survival subtypes across all 20 types of cancer. Our framework provided improved risk-stratification (e.g., for Liver hepatocellular carcinoma, LIHC, log-rank test, P = 8.12E-6) compared to PCA (e.g., for LIHC, log-rank test, P = 0.87), predicted survival subtypes accurately, and identified biomarkers for survival subtypes. Additionally, we identified potential interactions between microbes and genes that may play roles in survival. For instance, in LIHC, Arcobacter, Methylocella, and Isoptericola may regulate host survival through interactions with host genes enriched in the HIF-1 signaling pathway, indicating these species as potential therapy targets. Further experiments on validation dataset have also supported these patterns. Collectively, ASD-cancer has enabled accurate survival subtyping and biomarker discovery, which could facilitate personalized treatment for a broad-spectrum types of cancers.

bioinformatics↗

bioRxiv2022

Coupled functional physiological phenotyping and simulation model to infer the transpiration sensitivity traits and its implications for transpiration ideotype design

As agricultural drought becomes more frequent worldwide, it is essential to improve crop productivity whilst reducing the water consumption to achieve a sustainable production. Plant transpiration rate and water use efficiency (WUE) collectively determine the yield performance, yet it is challenging to balance the two in breeding programs due to still insufficient mechanistic understanding of the traits. Here we demonstrate the feasibility and effectiveness of calculating dynamic and momentary WUE by coupling WUE model and the state-of-the-art functional physiological phenotyping (FPP). We also present the method of quantifying genotype-specific traits reflecting sensitivity of transpiration to radiation (STr-Rad) and vapor pressure deficit (STr-VPD), under evolving developmental stage and water availability. Using these methods, we revealed the genotypic difference of STr-Rad and STr-VPD in three watermelon accessions, the dramatic change in each of them across the drought treatment phases, and the quantitative impacts of them on dynamic WUE patterns. Based on our results and computational simulations, a general principle for transpiration ideotype design is proposed, which highlights the benefits of lowering STr-VPD to increase WUE and increasing STr-Rad to offset the decline of Tr. FPP-enabled phenomic selection will help screen for elite crops lines with desired transpiration sensitivities.

physiology↗

bioRxiv2022

Improving biome labeling for tens of thousands of inaccurately annotated microbial community samples based on neural network and transfer learning

Microbiome samples are accumulating at a fast speed, leading to millions of accessible microbiome samples in the public databases. However, due to the lack of strict meta-data standard for data submission and other reasons, there is currently a non-neglectable proportion of microbiome samples in the public database that have no annotations about where these samples were collected, how they were processed and sequenced, etc., among which the missing information about collection niches (biome) is one of the most prominent. The lack of sample biome information has created a bottleneck for mining of the microbiome data, making it difficult in applications such as sample source tracking and biomarker discovery. Here we have designed Meta-Sorter, a neural network and transfer learning enabled AI method for improving the biome labeling of thousands of microbial community samples without detailed biome information. Results have shown that out of 16,507 samples that have no detailed biome annotations, 96.65% could be correctly classified, largely solving the missing biome labeling problem. Interestingly, we succeeded in classify 250 samples, which were sampled from benthic and water column but vaguely labeled as "Marine" in MGnify, in more details and with high fidelity. Whats more, many of successfully predicted sample labels were from studies that involved human-environment interactions, for which we could also clearly differentiated samples from environment or human. Taken together, we have improved the completeness of biome label information for thousands of microbial community samples, facilitating sample classification and knowledge discovery from millions of microbiome samples.

bioinformatics↗

bioRxiv2022

HDL-C as a potential medium between depletion of Lachnospiraceae genera and hypertension under high-calorie diet

Gut microbial dysbiosis has been associated with hypertension. An extremely high incidence of essential hypertension was found in the Han and the Yugur who resided in Sunan county in East Asias nomadic steppes with little population movement. In attempt to investigate the gut microbial role in hypertension, we recruited a total of 1, 242 Yugur and Han people, who had resided in Sunan County for more than 15 years and accounted for 3% of the local population. The epidemiological survey of 1,089 individuals indicated their nearly 1.8 times higher prevalence of hypertension (38.2-43.3%) than the average in China (23.2%), under a special high-calorie diet based on wheat, cattle, mutton, and animal offal. The 16S rRNA gene sequencing on the fecal samples of 153 individuals revealed that certain Lachnospiraceae genera were negatively correlated with high-density lipoprotein cholesterol (HDL-C, P = 5.46 x 10-6), systolic blood pressure (SBP, P = 7.22 x 10-3), diastolic blood pressure (DBP, P = 1.8 x 10-3). HDL-C was positively correlated with SBP (P = 0.023). We further observed that serum butyrate content was lower in both Han (P = 1.99 x 10-3) and Yugur people (P = 0.031) with hypertension than those without hypertension. This study gives a novel insight into the role of gut microbial dysbiosis in hypertension modulation under a high-calorie diet, where the notable depletion of Lachnospiraceae genera might lead to less production of butyrate, contributing to the lower level of HDL-C, and elevating blood pressure in hypertension. IMPORTANCEDietary nutrients can be converted by gut microbiota into metabolites such as short-chain fatty acids, which may serve as disease-preventing agents in hypertension. Due to limited population mobility and a unique high-calorie dietary habit, the recruited cohort in this study could be a representative for elucidating the associations between gut microbiota and hypertension under high-calorie diet. Moreover, low levels of HDL-C have previously been associated with an increased risk of various cardiovascular diseases (CVDs). Our findings provide a new insight that low levels of HDL-C may be a potential medium between depletion of Lachnospiraceae genera and hypertension under high-calorie diet, which might also be a potential candidate for other CVDs.

microbiology↗

bioRxiv2022

Quality Control Framework of TCM Preparations based on Multi-type Fingerprints using a Source Proportion Estimation Model

Traditional Chinese Medicine (TCM) preparations have been widely used in clinical practice for the treatment of various diseases. The quality of TCM preparations is related to clinical efficacy and safety and is highly valued by researchers. The authenticity of TCM preparation can be guaranteed objectively by accurate quality control according to the composition. Here, we proposed a quality control framework of TCM preparations, which is based on multi-type fingerprints using the source proportion estimation model (SPEM). The high-performance liquid chromatography (HPLC) analysis and the high-throughput sequencing analysis are employed to acquire the chemical and taxonomic fingerprints of samples, respectively. The quality of TCM preparations among different manufacturers or batches is evaluated by using SPEM, which is an unsupervised method for source identification of TCM samples. Results showed the good performance of the quality control framework, for example, SPEM achieved a mean accuracy of 0.778 based on the ITS2 taxonomic fingerprint when differentiating manufacturer of BazhenYimu Wan pill. Applications of the quality control framework revealed the batch effect in TCM samples, and environmental factors, such as geography have a profound impact on the consistency of TCM preparations. In summary, this study is an exploration in the field of digital development of TCM preparations and provide a new insight to quantify the batch effect among different batches of TCM samples.

pharmacology and toxicology↗

bioRxiv2022

Characterizing brain imaging features associated with ADAS-Cog13 sub-scores with 3D convolutional neural networks

To date, few advanced machine learning models have been developed for investigating the associations between features from brain imaging and individual Alzheimers disease (AD) related cognitive functional changes. Additionally, how these associations differ among different imaging modalities is unclear. Here we investigated 3D convolutional neural network (CNN) models which were trained to predict sub-scores in 13-item Alzheimers Disease Assessment Scale - Cognitive Subscale (ADAS-Cog13) based on MRI and FDG-PET brain imaging data obtained from the ADNI database. We found that each key ADAS-Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Overall, sub-scores were strongly associated with structural changes of subcortical regions including amygdala, hippocampus, and putamen, and were associated with metabolic changes of cortical regions including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. Our findings provided insights into complex AD etiology. Our analytical pipeline can also be utilized to study other brain diseases.

neuroscience↗

bioRxiv2022

Microbial dysbiosis and metabolic disorders promote rheumatoid arthritis progression across successive stages

BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease that typically progresses through four stages, from mild stiffness to severe disability. The critical involvement of gut microbial dysbiosis in promoting RA has been intriguing. The aim of this study was to investigate stage-specific roles of microbial dysbiosis and metabolic disorders in pathogenesis across successive stages of RA. MethodsIn this multi-omics cohort study, fecal metagenomic, plasma metabolomic data were collected from 76 patients grouped into four RA stages, 19 patients with osteoarthritis, and 27 healthy individuals in China. A non-redundant gene catalogue was constructed, functionally annotated, and clustered into metagenomic species (MGS). Leave-one-out analysis was performed to determine the MGS that most drove the microbial dysfunctions with RA across stages. Random forest algorithm was performed to investigate diagnostic potential of microbial or metabolic features for each stage. Additionally, to verify the bacterial invasion of the joint synovial fluid, we conducted 16S rRNA gene sequencing, bacterial isolation, and scanning electron microscopy on joint synovial fluid from another cohort of 271 RA patients grouped into four RA stages. FindingsWe found that microbial dysbiosis and metabolic disorders had stage-specific effects on RA progression. For instance, arginine succinyltransferase pathway was elevated mainly in the second and third stages of RA (p=1{middle dot}4x10-3), which was driven by Escherichia coli, and it was correlated with the increase of the rheumatoid factor (p=1{middle dot}35x10-3). Considerable elevation of methoxyacetic acid (p=1{middle dot}28x10-8) and cysteine-S-sulfate (p=4{middle dot}66x10-12) might inhibit osteoblasts in the second stage and enhance osteoclasts in the third stage, respectively. Moreover, fecal microbes Eggerthella lenta and Bifidobacterium longum that were exclusively elevated in the fourth stage, were also detected in the joint synovial fluid. InterpretationOur findings elucidate for the first time the stage-specific roles of microbial dysbiosis and metabolic disorders across successive stages of RA, which open up new avenues for RA prognosis and therapy. We demonstrate the buildup of these effects might induce microbial invasion of the joint synovial fluid in the fourth stage of RA.

microbiology↗

bioRxiv2021

Wear and Tear of the Intestinal Visceral Musculature by Intrinsic and Extrinsic Factors

The gut visceral musculature plays essential roles in not only moving substances through the lumen but also maintaining the function and physiology of the gut. Although the development of the visceral musculature has been studied in multiple model organisms, how it degenerates is poorly understood. Here, we employ the Drosophila midgut as a model to demonstrate that the visceral musculature is disrupted by intrinsic and extrinsic factors, such as aging, feeding, chemical-induced tissue damage, and oncogenic transformation in the epithelium. Notably, we define four prominent visceral musculature disruption phenotypes, which we refer as sprout, discontinuity, furcation, and crossover of the longitudinal muscle. Given that the occurrence of these phenotypes is increased during aging and under various stresses, we propose that these phenotypes can be used as quantitative readouts of deterioration of the visceral musculature. Intriguingly, administration of a tissue-damaging chemical dextran sulfate sodium (DSS) induced similar visceral musculature disruption phenotypes in zebrafish larvae, indicating that ingestion of a tissue-damaging chemical can disrupt the visceral musculature in a vertebrate as well. Our study provides insights into the deterioration of the gut visceral musculature and lays a groundwork for investigating the underlying mechanisms in Drosophila as well as other animals.

developmental biology↗

bioRxiv2021

Ontology-Aware Deep Learning Enables Novel Antibiotic Resistance Gene Discovery Towards Comprehensive Profiling of ARGs

Antibiotic resistance genes (ARGs) have emerged in pathogens and arousing a worldwide concern, which is estimated to cause millions of deaths each year globally. Accurately identifying and classifying ARGs is a formidable challenge in studying the generation and spread of antibiotic resistance. Current methods could identify close homologous ARGs, have limited utility for discovery of novel ARGs, thus rendering the profiling of ARGs incomprehensive. Here, an ontology-aware neural network (ONN) approach, ONN4ARG, is proposed for comprehensive ARG discovery. Systematic evaluation shows ONN4ARG is advanced than previous methods such as DeepARG in efficiency, accuracy, and comprehensiveness. Experiments using 200 million candidate microbial genes collected from 815 microbial community samples from diverse environments or hosts have resulted in 120,726 candidate ARGs, out of which more than 20% are not yet present in public databases. These comprehensive set of ARGs have clarified the environment-specific and host-specific patterns. The wet-experimental functional validation, together with structural investigation of docking sites, have also validated a novel streptomycin resistance gene from oral microbiome samples, confirming ONN4ARGs ability for novel ARGs identification. In summary, ONN4ARG is superior to existing methods in efficiency, accuracy, and comprehensiveness. It enables comprehensive ARG discovery, which is helpful towards a grand view of ARGs worldwide. ONN4ARG is available at https://github.com/HUST-NingKang-Lab/ONN4ARG, and online web service is available at http://onn4arg.xfcui.com/.

bioinformatics↗

bioRxiv2021

Generative Adversarial Network augmented the gut microbiome-based health index by profoundly improved discrimination power

Gut microbiome-based health index (GMHI) has been applied with success, while the discrimination powers of GMHI varied for different diseases, limiting its utility on a broad-spectrum of diseases. In this work, a generative adversarial network (GAN) model is proposed to improve the discrimination power of GMHI. Built based on the batch corrected data through GAN, GAN-GMHI has largely reduced the batch effects, and profoundly improved the performance for distinguishing healthy individuals and different diseases. GAN-GMHI has provided a solution to unravel the strong association of gut microbiome and diseases, and indicated a more accurate venue toward microbiome-based disease monitoring. The code for GAN-GMHI is available at https://github.com/HUST-NingKang-Lab/GAN-GMHI. ImportanceThe association of gut microbiome and diseases has been proven for many diseases, while the transformation of such association to a robust and universal disease prediction model has remained illusive, largely due to the batch effects presents in multiple microbiome cohorts. Our analyses have indicated a plausible venue, which is based on GAN technique, towards batch effect removal for microbiome datasets. GAN-GMHI is a novel method built based on the batch corrected data through GAN, as well as GMHI for prediction of a broad-spectrum of diseases.

bioinformatics↗

bioRxiv2021

Decoding microbiome and protein family linkage to improve protein structure prediction

Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil and Fermentor), we proposed a MetaSource model to decode the inherent link of microbial niches with protein homologous families. Large-scale protein family folding experiments showed that a targeted approach using predicted biomes significantly outperform combined metagenome datasets in both speed of MSA collection and accuracy of deep-learning structure assembly. These results revealed the important link of biomes with protein families and provided a useful bluebook to guide future microbiome sequence database and modeling development for protein structure and function prediction.

bioinformatics↗

bioRxiv2021

Safety and efficacy of C9ORF72-repeat RNA nuclear export inhibition in amyotrophic lateral sclerosis

BackgroundLoss of motor neurons in amyotrophic lateral sclerosis (ALS) leads to progressive paralysis and death. Dysregulation of thousands of RNA molecules with roles in multiple cellular pathways hinders the identification of ALS-causing alterations over downstream changes secondary to the neurodegenerative process. How many and which of these pathological gene expression changes require therapeutic normalisation remains a fundamental question. MethodsHere, we investigated genome-wide RNA changes in C9ORF72-ALS patient-derived neurons and Drosophila, as well as upon neuroprotection taking advantage of our gene therapy approach which specifically inhibits the SRSF1-dependent nuclear export of pathological C9ORF72-repeat transcripts. This is a critical study to evaluate (i) the overall safety and efficacy of the partial depletion of SRSF1, a member of a protein family involved itself in gene expression, and (ii) a unique opportunity to identify neuroprotective RNA changes. ResultsOur study demonstrates that manipulation of 362 transcripts out of 2,257 pathological changes in C9ORF72-ALS patient-derived neurons is sufficient to confer neuroprotection upon partial depletion of SRSF1. In particular, expression of 90 disease-altered transcripts is fully reverted upon neuroprotection leading to the characterisation of a human C9ORF72-ALS disease-modifying gene expression signature. These findings were further investigated in vivo in diseased and neuroprotected Drosophila transcriptomes, highlighting a list of 21 neuroprotective changes conserved with 16 human orthologues in patient-derived neurons. We also functionally validated the high therapeutic potential of one of these disease-modifying transcripts, demonstrating that inhibition of ALS-upregulated human KCNN1-3 (Drosophila SK) voltage-gated potassium channel orthologs mitigates degeneration of human motor neurons as well as Drosophila motor deficits. ConclusionsStrikingly, manipulating the expression levels of a small proportion of RNAs is sufficient to induce a therapeutic effect, further indicating that the SRSF1-targeted gene therapy approach is safe in the above preclinical models as it does not disrupt globally gene expression. The efficacy of this intervention is also validated at genome-wide level with therapeutically-induced RNA changes involved in the vast majority of biological processes affected in C9ORF72-ALS. Finally, the identification of a characteristic signature with key RNA changes modified in both the disease state and upon neuroprotection also provides potential new therapeutic targets and biomarkers.

systems biology↗

bioRxiv2021

Enabling technology for microbial source tracking based on transfer learning: From ontology-aware general knowledge to context-aware expert systems

Microbial source tracking quantifies the potential origin of microbial communities, facilitates better understanding of how the taxonomic structure and community functions were formed and maintained. However, previous methods involve a tradeoff between speed and accuracy, and have encountered difficulty in source tracking under many context-dependent settings. Here, we present EXPERT for context-aware microbial source tracking, in which we adopted a Transfer Learning approach to profoundly elevate and expand the applicability of source tracking, enabling biologically informed novel microbial knowledge discovery. We demonstrate that EXPERT can predict microbial sources with performance superior to other methods in efficiency and accuracy. More importantly, we demonstrate EXPERTs context-aware ability on several applications, including tracking the progression of infant gut microbiome development and monitoring the changes of gut microbiome for colorectal cancer patients. Broadly, transfer learning enables accurate and context-aware microbial source tracking and has the potential for novel microbial knowledge discovery.

bioinformatics↗

bioRxiv2020

Meta-Prism 2.0: Enabling algorithm for ultra-fast, accurate and memory-efficient search among millions of microbial community samples

MotivationMicrobial community samples and sequencing data have been accumulated at a speed faster than ever, with tens of thousands of samples been sequenced each year. Mining such a huge amount of multi-source heterogeneous data is becoming more and more difficult. Among several sample mining bottlenecks, efficient and accurate search of samples is one of the most prominent: Faced with millions of samples in the data repository, traditional sample comparison and search approaches fall short in speed and accuracy. ResultsHere we proposed Meta-Prism 2.0, a microbial community sample search method based on smart pair-wise sample comparison, which pushed the time and memory efficiency to a new limit, without the compromise of accuracy. Based on memory-saving data structure, time-saving instruction pipeline, and boost scheme optimization, Meta-Prism 2.0 has enabled ultra-fast, accurate and memory-efficient search among millions of samples. Meta-Prism 2.0 has been put to test on several datasets, with largest containing one million samples. Results have shown that firstly, as a distance-based method, Meta-Prism 2.0 is not only faster than other distance-based methods, but also faster than unsupervised methods. Its 0.00001s per sample pair search speed, as well as 8GB memory needs for searching against one million samples, have enabled it to be the most efficient method for sample comparison. Additionally, Meta-Prism 2.0 could achieve the comparison accuracy and search precision that are comparable or better than other contemporary methods. Thirdly, Meta-Prism 2.0 can precisely identify the original biome for samples, thus enabling sample source tracking. ConclusionIn summary, Meta-Prism 2.0 can perform accurate searches among millions of samples with very low memory cost and fast speed, enabling knowledge discovery from samples at a massive scale. It has changed the traditional resource-intensive sample comparison and search scheme to a cheap and effective procedure, which could be conducted by researchers everyday even on a laptop, for insightful sample search and knowledge discovery. Meta-Prism 2.0 could be accessed at: https://github.com/HUST-NingKang-Lab/Meta-Prism-2.0.

bioinformatics↗