Biology subjects

Zhang, H. H.

Publications and source records attributed to Zhang, H. H..

12 recordsLinked to original sources

bioRxiv2026

SEMFA: A General Framework for Inferring Statistical Significance of Mahalanobis Similarity between Multi-Omics Profiled Samples Built on Multiple Factor Analysis

MotivationWith rapid advances in sequencing technologies, many heterogeneous omics datasets have been generated, as seen in the Encyclopedia of DNA Elements (ENCODE) and many single-cell multi-omics sequencing projects, bringing substantial challenges to existing integrative methods. In this article, we report a novel multi-omics fusion and analysis software SEMFA which performs general parametric tests for the Mahalanobis Similarity of samples based on the factor scores generated by an Extended version of conventional Multiple Factor Analysis. ResultsOur developed method is effective and robust under both Gaussian and non-Gaussian assumptions. The mean F1 scores are over 0.8 when the column similarity level is 0.9 and the noise level ranges between 0.1 and 0.2, using simulation studies based on ENCODE count data. It was also efficient and effective at handling large-scale single-cell multi-omics data, as demonstrated in colon cancer cases as it unveiled signature network organization patterns of cells for stages III and IV.

bioinformatics↗

bioRxiv2024

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

Nucleotide modifications deviate nanopore sequencing readouts, therefore generating artifacts during the basecalling of sequence backbones. Here, we present an iterative approach to polish modification-disturbed basecalling results. We show such an approach is able to promote the basecalling accuracy of both artificially-synthesized and real-world molecules. With demonstrated efficacy and reliability, we exploit the approach to precisely basecall therapeutic RNAs consisting of artificial or natural modifications, as the basis for quantifying the purity and integrity of vaccine mRNAs which are transcribed in vitro, and for determining modification hotspots of novel therapeutic RNA interference (RNAi) molecules which are bioengineered (BioRNA) in vivo.

bioinformatics↗

bioRxiv2024

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a novel paradigm for building modification-tolerant nanopore sequencing basecallers.

bioinformatics↗

bioRxiv2023

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression

The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS (https://ccg.epfl.ch/snp2tfbs/) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcriptions dependence on SP1 protein levels, incorporating the SNPs influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

bioinformatics↗

bioRxiv2023

A Spatiotemporal Molecular Atlas of the Ovulating Mouse Ovary

Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell-type- and ovulation-stage-specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified new molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte-cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.

genomics↗

bioRxiv2023

Machines learn ecological networks: automated discovery of ecological networks based on empirical data

Constructing ecological networks is known to be important and challenging in community ecology. In particular, to construct the holistic structure of ecological networks, identifying species interaction is essential but often costly and impalpable. Recent studies providing major challenges in assembling ecological networks have highlighted the need of new and more powerful approaches to reconstruct biological networks, including species interaction networks. In literature, there are no promising verifications in using machine leaning (ML) approaches to reconstruct ecological networks. In this work, we develop and employ a variety of ML methods, including penalized regression and graphical tools, to reconstruct ecological networks. For evaluation, we apply the methods to empirical time series data sets of 20 species abundances collected at Lake Constance in central Europe. We use resampled data to identify highly-ranked interactions among species and measure their consistency across 7 ML methods and 5,000 learning processes. We show that the best precision, recall, and F1 score were 0.48, 0.97, and 0.64, respectively, among all penalized regression methods under comparison. In summary, our study shows that machine learning methods offer promising data-driven and automated tools for reconstructing ecological networks and discovering underlying biological interactions among species.

ecology↗

bioRxiv2023

Chemokine positioning determines mutually exclusive roles for their receptors in extravasation of pathogenic human T cells

Pro-inflammatory T cells co-express multiple chemokine receptors, but the distinct functions of individual receptors on these cells are largely unknown. Human Th17 cells uniformly express the chemokine receptor CCR6, and we discovered that the subgroup of CD4+CCR6+ cells that co-express CCR2 possess a pathogenic Th17 signature, can produce inflammatory cytokines independent of TCR activation, and are unusually efficient at transendothelial migration (TEM). The ligand for CCR6, CCL20, was capable of binding to activated endothelial cells (ECs) and inducing firm arrest of CCR6+CCR2+ cells under conditions of flow - but CCR6 could not mediate TEM. By contrast, CCL2 and other ligands for CCR2, despite being secreted from both luminal and basal sides of ECs, failed to bind to the EC surfaces - and CCR2 could not mediate arrest. Nonetheless, CCR2 was required for TEM. To understand if CCR2s inability to mediate arrest was due solely to an absence of EC-bound ligands, we generated a CCL2-CXCL9 chimeric chemokine that could bind to the EC surface. Although display of CCL2 on the ECs did indeed lead to CCR2-mediated arrest of CCR6+CCR2+ cells, activating CCR2 with surface-bound CCL2 blocked TEM. We conclude that mediating arrest and TEM are mutually exclusive activities of chemokine receptors and/or their ligands that depend, respectively, on chemokines that bind to the EC luminal surfaces versus non-binding chemokines that form transendothelial gradients under conditions of flow. Our findings provide fundamental insights into mechanisms of lymphocyte extravasation and may lead to novel strategies to block or enhance their migration into tissue.

immunology↗

bioRxiv2023

Human CCR6+ Th cells show both an extended stable gradient of Th17 activity and imprinted plasticity

Th17 cells have been investigated in mice primarily for their contributions to autoimmune diseases. However, the pathways of differentiation of Th17 and related (type 17) cells and the structure of the type 17 memory population in humans are not well understood; such understanding is critical for manipulating these cells in vivo. By exploiting differences in levels of surface CCR6, we found that human type 17 memory cells, including individual T cell clonotypes, form an elongated continuum of type 17 character along which cells can be driven by increasing ROR{gamma}t. This continuum includes cells preserved within the memory pool with potentials that reflect the early preferential activation of multiple over single lineages. The CCR6+ cells phenotypes and epigenomes are stable across cell divisions under homeostatic conditions. Nonetheless, activation in polarizing and non-polarizing conditions can yield additional functionalities, revealing, respectively, both environmentally induced and imprinted mechanisms that contribute differentially across the continuum to yield the unusual plasticity ascribed to type 17 cells.

immunology↗

bioRxiv2019

binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions

BackgroundIn this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcript) than samples (i.e., mice or human samples) in a study, this poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest1 (RF) classifiers are widely used2-7 due to their flexibility, powerful performance, and robustness to "P predictors >> subjects N" difficulties and their ability to rank features. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions. MethodsbinomialRF treats each tree in a RF as a correlated but exchangeable binary trial. It determines importance by constructing a test statistic based on a features selection frequency to compute its rank, nominal p-value, and multiplicity-adjusted q-value using a one-sided hypothesis test with a correlated binomial distribution. A distributional adjustment addresses the co-dependencies among trees as these trees subsample from the same dataset. The proposed algorithm efficiently identifies multiway nonlinear interactions by generalizing the test statistic to count sub-trees. ResultsIn simulations and in the Madelon benchmark datasets studies, binomialRF showed computational gains (up to 30 to 600 times faster) while maintaining competitive variable precision and recall in identifying biomarkers main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone. ConclusionbinomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide path-way-level feature selection from gene expression input data. AvailabilityGithub: https://github.com/SamirRachidZaim/binomialRF Supplementary informationSupplementary analyses and results are available at https://github.com/SamirRachidZaim/binomialRF_simulationStudy

bioinformatics↗

bioRxiv2018

Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

BackgroundGene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more precision approach that integrates individual variability including omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression analysis requires methodological advancements. One need is for users to confidently be able to make individual-level inferences from whole transcriptome data. We propose that biological replicates in isogenic conditions can provide a framework for testing differentially expressed genes (DEGs) in a single subject (ss) in absence of an appropriate external reference standard or replicates.\n\nMethodsEight ss methods for identifying genes with differential expression (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) were compared in Yeast (parental line versus snf2 deletion mutant; n=42/condition) and MCF7 breast-cancer cell (baseline and stimulated with estradiol; n=7/condition) RNA-Seq datasets where replicate analysis was used to build reference standards from NOISeq, DEGseq, edgeR, DESeq, DESeq2. Each dataset was randomly partitioned so that approximately two-thirds of the paired samples were used to construct reference standards and the remainder were treated separately as single-subject sample pairs and DEGs were assayed using ss methods. Receiver-operator characteristic (ROC) and precision-recall plots were determined for all ss methods against each RSs in both datasets (525 combinations).\n\nResultsConsistent with prior analyses of these data, ~50% and ~15% DEGs were respectively obtained in Yeast and MCF7 reference standard datasets regardless of the analytical method. NOISeq, edgeR and DESeq were the most concordant and robust methods for creating a reference standard. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the type of reference standard (>90% in Yeast, >0.75 in MCF7).\n\nConclusionBetter and more consistent accuracies are obtained by an ensemble method applied to singlesubject studies across different conditions. In addition, distinct specific sing-subject methods perform better according to different proportions of DEGs. Single-subject methods for identifying DEGs from paired samples need improvement, as no method performs with both precision>90% and recall>90%. http://www.lussiergroup.org/publications/EnsembleBiomarker

bioinformatics↗

bioRxiv2018

iDEG: A single-subject method for assessing gene differential expression from two transcriptomes of an individual

BackgroundAccurate profiling of gene expression in a single subject has the potential to be a powerful precision medicine tool, useful for unveiling individual disease mechanisms and responses. However, expression analysis tools for RNA-sequencing (RNA-Seq) data require replicate samples to estimate gene-wise data variability and make inferences, which is costly and not easily obtainable in clinical practice. Strategies to implement DEGSeq, DESeq, and edgeR for comparing two conditions without replicates (TCWR) have been proposed without evaluation, while NOISeq-sim was validated in a restricted way using qPCR on 400 transcripts. These methods impose restrictive assumptions in TCWR limiting inferential opportunities.\n\nMethodsWe propose a new method that borrows information across different genes from the same individual using a partitioned window to strategically bypass the requirement of replicates per condition. We termed this method \"iDEG\", which identifies individualized Differentially Expressed Genes in a single subject sampled under two conditions without replicates, i.e., a baseline sample (unaffected tissue) vs. a case sample (tumor). iDEG transforms RNA-Seq data such that, under the null hypothesis, differences of transformed expression counts follow a distribution and variance calculated across a local partition of related transcripts at baseline expression. This transformation enables modeling genes with a two-group mixture model from which the probability of differential expression for each gene is then estimated by an empirical Bayes approach with a local false discovery rate control. To compare the performance of iDEG to other methods applied to TCWR, we conducted simulations assuming a Negative Binomial distribution with varying dispersion parameters and percentages of differentially expressed genes (DEGs).\n\nResultsOur extensive simulation studies demonstrate that iDEGs F1 accuracy scores better than the other methods at 5% 90% and recall>75% and low false positive rate (<1%) in most conditions.\n\nConclusionThe partitioned window strategy provides a novel and accurate way to borrow information across genes locally and would probably increase the accuracy of all relevant methods.

bioinformatics↗

bioRxiv2018

A lysosomal dimmer switch regulates cellular quiescence depth

Numerous physiological and pathological phenomena are associated with the quiescent state of a cell. Cellular quiescence is a heterogeneous resting state; cells in deep than shallow quiescence require stronger growth stimulation to exit quiescence and reenter the cell cycle. Despite the importance of quiescent cells such as stem and progenitor cells to tissue homeostasis and repair, cellular mechanisms controlling the depth of cellular quiescence are poorly understood. Here we began by analyzing transcriptome changes as rat embryonic fibroblasts moved progressively deeper into quiescence under increasingly longer periods of serum starvation. We found that lysosomal gene expression was significantly upregulated in deep than shallow quiescence, which compensated for gradually reduced autophagy flux observed during quiescence deepening. Consistently, we show that inhibiting lysosomal function drove cells deeper into quiescence and eventually into a senescence-like irreversibly arrested state. By contrast, increasing lysosomal function progressively pushed cells into shallower quiescence. That is, lysosomal function modulates quiescence depth continuously like a dimmer switch. Mechanistically, we show that lysosomal function prevents quiescence deepening by reducing oxidative stress in the cell. Lastly, we show that a gene expression signature developed by comparing deep and shallow quiescent cells can correctly classify senescent and aging cells in a wide array of cell lines in vitro and tissues in vivo, suggesting that quiescence deepening, senescence, and aging may share common regulatory mechanisms.

cell biology↗