Biology subjects

Rachid Zaim, S.

Publications and source records attributed to Rachid Zaim, S..

9 recordsLinked to original sources

bioRxiv2026

Deconvolution of the mechanisms of T cell drug response in multiple myeloma induction therapy

Lenalidomide (Revlimid), bortezomib (Velcade), and dexamethasone, are used alone or in combination (RVd therapy) as first-line therapies for hematologic malignancies including the plasma cell dyscrasia, Multiple Myeloma (MM). The effects of RVd treatment on tumor cells have been documented, but little is known about their impacts on "healthy" immune cells. New therapeutics, including chimeric antigen receptor T cell (CAR-T) and bispecific T-cell engagers (BiTEs) depend on robust T cell function but these are often harvested for cellular therapies after or in tandem with RVd therapy. Understanding the molecular effects of these drugs on healthy T cells is therefore of particular importance. Since the molecular effects of each drug includes regulation of key transcription factors, we used a tri-modal, single-cell assay (TEA-seq), simultaneously profiling mRNA transcripts, cell surface proteins, and chromatin accessibility in primary human peripheral blood T cells treated for 4, 24, or 72 hours in vitro. Synergies and conflicts potentially arising from combinatorial therapy were identified and validated by flow cytometry. Doublet and triplet drug combinations were used to assess effects on cell-surface marker expression and on T cell activation. Our results suggest that T cell function can be optimized by the administration of these drugs individually and that sequential administration may offer an opportunity to enhance T cell function by affecting localization and altering T cell states. This work provides those interested in immunology, oncology, and cell therapies an opportunity to explore molecular pathways that could be manipulated by these commonly used drugs to modify T cell function.

immunology↗

bioRxiv2026

Myeloma and therapy reshape the bone marrow niche to durably constrain immune reconstitution and vaccine responsiveness

Infections are the most common cause of non-relapse mortality in multiple myeloma (MM), but the basis of persistent immune dysfunction is obscured by patient heterogeneity and complex treatment regimens, including autologous stem cell transplant (ASCT). We performed longitudinal multi-omic profiling of matched bone marrow and peripheral blood from MM patients across diagnosis, induction, ASCT, and recovery. We found the tumor imposes a compartment-specific immune program where the marrow exhibits metabolic and inflammatory changes that bias hematopoiesis and alter cytotoxic effector programs not mirrored in blood. Adaptive immune reconstitution is impaired up to two years post-ASCT. Half of patients fail to mount IgG responses to high-dose non-adjuvanted influenza vaccine, a defect overcome by the lipid nanoparticle (LNP) adjuvanted COVID mRNA vaccine, which elicited responses in all patients, supporting adjuvanted influenza vaccine strategies in MM. Together these findings define how myeloma and its treatment durably reshape immunity from the marrow outward. HighlightsO_LIMultiple Myeloma marrow and blood show opposing metabolic and inflammatory states C_LIO_LIInduction therapy selects durable myeloma plasma-cell transcriptional states C_LIO_LIB cell and follicular helper T deficits blunt antigen responses after transplant C_LIO_LICOVID-19 vaccination builds immune memory with variable responses to flu vaccination C_LI eTOCMultiple myeloma and its treatment leave a lasting imprint on the bone marrow niche. By profiling bone marrow and blood longitudinally at diagnosis, through induction, autologous transplant, and recovery, we show that marrow-local metabolic and inflammatory constraints persist and help explain why influenza vaccination often fails while mRNA vaccination succeeds.

cancer biology↗

bioRxiv2025

Dissecting type I and II interferon impacts on human immune cells in disease by a cell type-specific interferon response atlas

Interferons (IFNs) orchestrate diverse immune responses, but distinguishing individual IFN contributions in human transcriptomic data is challenging due to overlapping interferon-stimulated gene (ISG) signatures and limited cell-type-specific datasets. To address this, we generated a single-cell transcriptomic atlas of IFN responses by stimulating primary human T, B, NK, and CD14 monocytes with IFN-I, IFN-II, and IFN-III. This revealed core and cell-type-specific ISG programs across 13 subsets, highlighting distinct functions of IFNs. We developed an algorithm to separate IFN-I and IFN-II activity in transcriptomic data. Applied to multiple myeloma samples, it showed elevated IFN-I and IFN-II responses, with induction therapy reducing only IFN-I. Extending to multiple disease datasets provided a cross-disease overview of IFN-I and IFN-II activities and revealed increased IFN-II activities in T cells during lupus flares. This resource and the accompanying analytical framework enable dissection of IFN-driven transcriptional programs in a cell-type specific manner in human disease.

immunology↗

bioRxiv2024

Systemic inflammation and lymphocyte activation precede rheumatoid arthritis

Some autoimmune diseases, including rheumatoid arthritis (RA), are preceded by a critical subclinical phase of disease activity. Proactive clinical management is hampered by a lack of biological understanding of this subclinical at-risk state and the changes underlying disease development. In a cross-sectional and longitudinal multi-omics study of peripheral immunity in the autoantibody-positive at-risk for RA period, we identified systemic inflammation, proinflammatory-skewed B cells, expanded Tfh17-like cells, epigenetic bias in naive T cells, TNF+IL1B+ monocytes resembling a synovial macrophage population, and CD4 T cell transcriptional features resembling those suppressed by abatacept (CTLA4-Ig) in RA patients. Our findings characterize pathogenesis prior to clinical diagnosis and suggest the at-risk state exhibits substantial immune alterations that could potentially be targeted for early intervention to delay or prevent autoimmunity. We provide a suite of tools at https://apps.allenimmunology.org/aifi/insights/ra-progression/ to facilitate exploration and enhance accessibility of this extensive dataset. One Sentence SummaryACPA+ at-risk individuals show RA-like inflammation and multi-compartment immune dysregulation during transition to clinically active RA

immunology↗

bioRxiv2023

MOCHA: Advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human disease cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has been increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. We developed MOCHA (Model-based single cell Open CHromatin Analysis) with major advances over existing analysis tools, including: 1) improved identification of sample-specific open chromatin, 2) proper handling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) transcription factor-gene network construction from longitudinal scATAC-seq data. These advances provide a robust framework to study gene regulatory programs in human disease. We benchmarked MOCHA with four state-of-the-art tools to demonstrate its advances. We also constructed cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

bioinformatics↗

bioRxiv2021

"Single-subject studies"-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases

MotivationIdentifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g., treated vs pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful biomechanism differences between very small cohorts by using the effect size of "single-subject-study"-derived responsive biomechanisms. ResultsIn each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g., diseased vs unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 vs 20 to 2 vs 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 vs 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 vs 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. AvailabilityR software is available at Lussierlab.net/BSSD. ContactLussier.y@gmail.com, Piegorsch@math.arizona.edu

bioinformatics↗

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↗