Biology subjects

Bharti, V.

Publications and source records attributed to Bharti, V..

5 recordsLinked to original sources

bioRxiv2026

StickForStats: automated statistical assumption validation for reproducible computational biology

Reproducible computational biology depends on statistical decisions that routine workflows often skip: verifying that a differential-expression tests assumptions hold across all genes, that a strategy-comparison ANOVA is robust to non-normality, or that a meta-analysis is not distorted by publication bias. Surveys consistently find that fewer than 20% of published biomedical studies report checking these assumptions, and existing statistical software leaves validation to the analyst as an optional step. We present StickForStats, an open-source web platform that reframes assumption validation as a default precondition for every analysis. Its Guardian system--a middleware pipeline of eight validators (normality, variance homogeneity, independence, outliers, sample size, modality, linearity, homoscedasticity)--checks assumptions before execution and, on critical violations, reroutes to an appropriate nonparametric alternative with a documented decision trail. At genome scale, applying Guardian to a 91-sample synovial-sarcoma RNA-seq study (GSE271517) cascaded 90.6% of 27,221 genes to a rank-based test and flipped the differential-expression verdict for 553 genes--479 rescued from an under-powered t-test and 74 outlier-driven false positives rejected--materially changing the gene list a biologist would act on. The same automatic validation generalizes across domains: a CRISPR editing-strategy comparison (ANOVA F = 1122, with Guardian recommending Kruskal-Wallis H = 36.6), an ordinal correlation (Pearson r = 0.476 corrected to Spearman {rho} = 0.479), and a sixteen-trial clinical meta-analysis revealing severe publication bias (Eggers t = -5.78, p < 0.001); a complementary module extends the same validators to published manuscripts, checking claims against CONSORT, STROBE, ICH-E9, and JARS-Quant reporting standards. By making assumption validation automatic and transparent, StickForStats targets a tractable, under-served contributor to irreproducibility. The platform is MIT-licensed, validated against SciPy and R, and freely available at https://github.com/visvikbharti/stickforstats_new. Author summaryMost scientific conclusions rest on statistical tests, and every test comes with fine print: assumptions about the data that must hold for the result to be trustworthy. In practice, this fine print is often left unchecked. Surveys find that fewer than one in five published studies reports verifying these assumptions, partly because popular software treats the check as an optional extra that busy researchers easily skip. When the assumptions are ignored, a study can report a difference that is not really there. We built StickForStats to make this checking automatic. Before it runs any statistical test, our platform inspects the data, reports whether each assumption is met, and--if a serious problem is found--switches to a more appropriate method and records why. On four real biomedical datasets, including a gene-editing comparison and a large gene-expression study, we show that this safety net changes which findings are flagged as reliable. A companion tool applies the same checks to finished manuscripts, helping catch reporting problems before publication. By turning assumption checking from something you must remember into something that happens by default, we aim to make everyday analyses more reproducible.

bioinformatics↗

bioRxiv2026

CDK4/6 inhibition sensitizes breast cancer to NK cell therapy by inducing immune-interactive surface proteins

CDK4/6 inhibitors are standard-of-care for metastatic estrogen receptor-positive (ER+) breast cancer, yet the development of resistance remains a significant clinical hurdle. While CDK4/6 inhibitors are primarily recognized for their ability to induce cytostasis, their role in modulating innate immune responses remains poorly defined. Here, we demonstrated that CDK4/6i treatment remodels the tumor cell surface to favor recognition and elimination by Natural Killer (NK) cells. Using a diverse biobank of patient-derived organoids (PDOs), we found that CDK4/6 inhibition robustly upregulated the adhesion molecule ICAM-1 and the NKG2D stress ligands (ULBP2/5/6 and MICA/B). This NK-engaging cell surface phenotype was driven by a bifurcated signaling network: NF-{kappa}B signaling orchestrated ICAM-1 induction, while the PI3K/mTOR pathway regulated the expression of stress ligands. Functional assays confirmed that these ligands were indispensable for NK cell-mediated elimination of breast cancer cells. In vivo studies using ER+ PDX models revealed that a brief seven-day primer treatment with the CDK4/6 inhibitor abemaciclib was sufficient to sensitize tumors to NK cell therapy, significantly inhibiting tumor growth and prolonging survival. We also observed efficacy with a concurrent dosing strategy that delayed the onset of acquired resistance. These findings provide a mechanistic rationale for combining CDK4/6 inhibitors with NK cell therapy. This "prime and kill" approach offers a promising strategy to overcome therapeutic resistance and improve outcomes for patients with metastatic ER+ breast cancer.

cancer biology↗

bioRxiv2025

Evaluation of natural killer cell tumor homing and effector function in response to CDK4/6 and AURKA inhibition in a melanoma tumor-on-a-chip platform

Natural killer (NK) cells have emerged as an important clinical tool cellular immunotherapy. Whereas immune checkpoint blockade (ICB) or chimeric antigen receptor (CAR) T-cell therapy (CAR-T) therapy have been adopted as a first line treatments in different malignancies, such as melanoma, these approaches do not work for all patients. T cells require proper antigen presentation on tumor cells for recognition and to carry out their corresponding cytotoxic functions. Deficiency of tumor antigens, or high variability in those present, make T cell-based CAR-T and ICB ineffective. By contrast, NK cells are not limited by antigen presentation deficiencies, offering a potential alternative approach, yet their efficacy can suffer from immunosuppressive signals. Herein, we sought to develop in vitro and on-chip platforms to identify strategies for enhance, rather than suppress, NK cell homing to tumor cells. We explored the use of inhibition of kinases such as CK4/6 and AURKA to induce tumor cell production of chemokines that NK cells migrate towards in aggressive melanoma models. We evaluated chemokine-aided NK cell migration-homing capabilities and their therapeutic efficacy and found that treatment of both melanoma cell line and patient-tumor constructs (PTCs) with CDK4/6 and AURKA generally resulted in improved NK cell homing to tumor cells and accompanying tumor cell killing. Interestingly, this chemokine-guided NK cell migration did not generate as effective outcomes in models using a mildly aggressive melanoma cell line. For our studies, we used 3D tumor constructs in both static Transwell models and then in a bioengineered NK cell-functionalized tumor-on-a-chip (NK-TOC) platform. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/662476v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@17bf445org.highwire.dtl.DTLVardef@e1e1d9org.highwire.dtl.DTLVardef@1b29c2corg.highwire.dtl.DTLVardef@12b1f03_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Macromolecular Diamidobenzimidazole Conjugates Activate STING

Pharmacologic activation of the stimulator of interferon genes (STING) pathway has broad potential applications, including the treatment of cancer and viral infections, which has motivated the synthesis and testing of a diversity of STING agonists as next generation immunotherapeutics. A promising class of STING agonists are the non-nucleotide, small molecule, dimeric-amidobenzimidazoles (diABZI), which have been recently used in the synthesis of polymer- and antibody-drug conjugates to improve pharmacokinetics, modulate biodistribution, and to confer other favorable properties for specific disease applications. These approaches have leveraged diABZI variants functionalized with reactive handles and enzyme-cleavable linkers at the 7-position of the benzimidazole for conjugation to and tunable drug release from carriers. However, since this position does not interact with STING and is exposed from the binding pocket when bound in an "open lid" configuration, we sought to evaluate the activity of macromolecular diABZI conjugates that lack enzymatic release and are instead conjugated to polymers via a stable linker. By covalently ligating diABZI to 5 or 20 kDa mPEG chains via an amide bond, we surprisingly found that these conjugates could activate STING in vitro. To further evaluate this phenomenon, we designed a diABZI-functionalized RAFT chain transfer agent that provided an enabling tool for synthesis of large, hydrophilic, dimethylacrylamide (DMA) polymers directly from a single agonist and we found that these conjugates also elicited STING activation in vitro with similar kinetics to highly potent small molecule analogs. We further demonstrated the in vivo activity of these macromolecular diABZI platforms, which inhibited tumor growth to a similar extent as small molecule variants. Using flow cytometry and fluorescence microscopy to evaluate intracellular uptake and distribution of Cy5-labeled analogs, our data indicate that although diABZI-DMA conjugates enter cells via endocytosis, they can still colocalize with the ER, suggesting that intracellular trafficking processes can promote delivery of endocytosed macromolecular diABZI compounds to STING. In conclusion, we have described new chemical strategies for the synthesis of stable macromolecular diABZI conjugates with unexpectedly high immunostimulatory potency, findings with potential implications for the design of polymer-drug conjugates for STING agonist delivery that also further motivate investigation of endosomal and intracellular trafficking as an alternative route for achieving STING activation.

bioengineering↗

bioRxiv2025

MLC1 alteration in iPSCs give rise to disease-like cellular vacuolation phenotype in the astrocyte lineage

BackgroundMegalencephalic leukoencephalopathy with subcortical cysts (MLC), a rare and progressive neurodegenerative disorder involving the white matter, is not adequately recapitulated by current disease models. Somatic cell reprogramming, along with advancements in genome engineering, may allow the establishment of in-vitro human models of MLC for disease modeling and drug screening. In this study, we utilized cellular reprogramming and gene-editing techniques to develop induced pluripotent stem cell (iPSC) models of MLC to recapitulate the cellular context of the classical MLC-impacted nervous system. MethodsSomatic cell reprogramming of peripheral patient-derived blood mononuclear cells (PBMCs) was used to develop iPSC models of MLC. CRISPR-Cas9 system-based genome engineering was also utilized to create the MLC1 knockout model of the disease. Directed differentiation of iPSCs to neural stem cells (NSCs) and astrocytes was performed in a 2D cell culture format, followed by various cellular and molecular biology approaches, to characterize the disease model. ResultsMLC iPSCs established by somatic cell reprogramming and genome engineering were well characterized for pluripotency. iPSCs were subsequently differentiated to disease-relevant cell types: neural stem cells (NSCs) and astrocytes. RNA sequencing profiling of MLC NSCs revealed a set of differentially expressed genes related to neurological disorders and epilepsy, a common clinical finding within MLC disease. This gene set can serve as a target for drug screening for the development of a potential therapeutic for this disease. Upon differentiation to the more disease relevant cell type-astrocytes, MLC-characteristic vacuoles were clearly observed, which were distinctly absent from controls. This emergence recapitulated a distinguishing phenotypic marker of the disease. ConclusionThrough the creation and analyses of iPSC models of MLC, our work addresses a critical need for relevant cellular models of MLC for use in both disease modeling and drug screening assays. Further investigation can utilize MLC iPSC models, as well as generated transcriptomic data sets and analyses, to identify potential therapeutic interventions for this debilitating disease.

cell biology↗