About

Dr. Sandeep Mallipattu is a Professor of Medicine at Stony Brook University, specializing in Nephrology. His academic work includes research on mitochondrial regulation in the kidney, with notable publications such as 'KLF 6: a mitochondrial regulator in the kidney' and studies on the role of Kruppel-like factors in renal function. His research focuses on understanding the molecular mechanisms underlying kidney diseases, including diabetic nephropathy, HIV-related kidney disease, and chronic kidney disease, with an emphasis on identifying potential therapeutic targets. Dr. Mallipattu's work also explores the effects of retinoids, advanced glycation end products, and other molecular pathways in renal pathology, contributing to the advancement of nephrology through both clinical and basic science investigations.

Research topics

• Medicine
• Internal medicine
• Biology
• Endocrinology
• Biochemistry
• Cell biology
• Intensive care medicine
• Virology
• Emergency medicine
• Chemistry
• Pathology
• Gastroenterology
• Demography
• Genetics
• Immunology
• Environmental health
• Cardiology
• Neuroscience

Selected publications

• Rapid podocyte loss in murine models triggers the formation of podocyte-parietal epithelial cell intercellular bridges

  American Journal of Physiology-Renal Physiology · 2026-01-02

  articleOpen accessSenior author

  To date, this is the first study to demonstrate that intercellular bridges form between podocytes and parietal epithelial cells in the setting of rapid podocyte loss in subtypes of glomerulonephritis and FSGS.

  PublisherDOI

• Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

  ArXiv.org · 2026-04-27

  articleOpen access

  Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation, 730-day prediction; N=81401; dialysis/ESRD prevalence: 1.1%) and modeled sequences of diagnoses, procedures, and medications with kidney laboratory trends (creatinine, BUN, eGFR). A transformer-based causal multi-head model was trained to estimate drug- and ingredient-level average treatment effects (ATEs) using counterfactual exposure removal and insertion under a full medication history setup. On test set, predictive performance reached an AUC of 0.694 and PR-AUC of 0.094. At the selected decision threshold (0.883), the model achieved an F1 score of 0.201 with a Brier score of 0.018. Post-hoc causal analyses of lab changes (eGFR, creatinine, BUN) using IPTW, AIPW, naive, and covariate-adjusted OLS methods assessed clinical directionality. Results showed partial protective-direction support for ACE/ARB exposures and worsening-direction signals for loop diuretics.

  PublisherOA PDF

• Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

  arXiv (Cornell University) · 2026-04-27

  preprintOpen access

  Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation, 730-day prediction; N=81401; dialysis/ESRD prevalence: 1.1%) and modeled sequences of diagnoses, procedures, and medications with kidney laboratory trends (creatinine, BUN, eGFR). A transformer-based causal multi-head model was trained to estimate drug- and ingredient-level average treatment effects (ATEs) using counterfactual exposure removal and insertion under a full medication history setup. On test set, predictive performance reached an AUC of 0.694 and PR-AUC of 0.094. At the selected decision threshold (0.883), the model achieved an F1 score of 0.201 with a Brier score of 0.018. Post-hoc causal analyses of lab changes (eGFR, creatinine, BUN) using IPTW, AIPW, naive, and covariate-adjusted OLS methods assessed clinical directionality. Results showed partial protective-direction support for ACE/ARB exposures and worsening-direction signals for loop diuretics.

  PublisherDOI

• Mineralocorticoid receptor blockade quiesces parietal epithelial cell activation

  Kidney International · 2025-11-19

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• <b>Supplemental figures for manuscript: </b><b>Rapid Podocyte</b><b> </b><b>Loss</b><b> </b><b>in Murine Models Triggers the Formation of Podocyte-Parietal Epithelial Cell Intercellular Bridges</b>

  Figshare · 2025-01-01

  otherOpen access1st authorCorresponding

  <b>SUPPLEMENTAL TABLE OF CONTENTS</b><b>Supplementary Table 1:</b><b> </b>Primary antibodies for immunofluorescence staining.<b>Supplementary Table 2:</b><b> </b>Secondary antibodies for immunofluorescence staining.<b>Supplementary Figure 1:</b><b> </b>Quantification of UACR, podocyte number, PEC activation, and glomerular Intercellular bridges in P-NTS (Probetex-nephrotoxic serum)-treated versus IgG-treated<i> </i>mice.<b>Supplementary Figure 2:</b> Quantification of UACR, podocyte number, PEC activation, and glomerular intercellular bridges in two murine models: LPS model (LPS versus DMSO model) and<i> </i>DKD model (<i>db/db</i><i> </i>versus <i>db/+</i><i> </i>mice at 8, 16, and 24 weeks of age).<b>Supplementary Video 1:</b><b> </b>Representative video of a coculture of DiO-labeled podocytes (green) and DiD-labeled PECs (red).<b>Supplemental Figure Legends:</b><b>Supplementary Figure 1: </b>Quantification of UACR, podocyte number, PEC activation, and glomerular Intercellular bridges in P-NTS (Probetex-nephrotoxic serum)-treated versus IgG-treated mice. (A) Urine albumin to creatinine ratio (UACR). N = 4 per group; *P &lt; 0.05, Kruskal-Wallis test with Dunn’s post-test, #P &lt; 0.05, Mann-Whitney test. (B)Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 20 glomeruli per mouse; ***P &lt; 0.001, ****P &lt; 0.0001, Kruskal-Wallis test with Dunn’s post-test. (C) Quantification of glomerular CD44 expression. N = 3 mice per group, 20 glomeruli per mouse; ****P &lt; 0.0001, Kruskal-Wallis test with Dunn’s post-test. (D)Representative images of PAS staining. Insets with arrows showing intercellular bridges. Scale bars = 20 µm, inset scale bars = 20 µm. (E) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group; *P &lt; 0.05, Mann-Whitney test. (F) Distribution of the number of bridges per glomerulus. N = 3 per group; 30 glomeruli per mouse. *P &lt; 0.05, Kruskal-Wallis test with Dunn’s post-test.<b>Supplementary Figure 2: </b>Quantification of UACR, podocyte number, PEC activation, and glomerular intercellular bridges in two murine models: LPS model (LPS versus DMSO) and DKD model (db/db versus db/+ mice at 8, 16, and 24 weeks of age). (A) Urine albumin to creatinine ratio (UACR). N = 4 per group; *P &lt; 0.05, Mann-Whitney test. (B)Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 30 glomeruli per mouse; non-significant (NS), Mann-Whitney test. (C) Representative images of PAS staining. Scale bars = 20 µm. (D) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group. (E) UACR ratio of db/+ and db/db mice. N = 3 per group; *P &lt; 0.05, Mann-Whitney test. (F) Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 20 glomeruli per mouse; **P &lt; 0.01, ****P &lt; 0.0001, two-way ANOVA with Tukey’s post-test. (G) Representative images of PAS staining from db/+ and db/db mice. Scale bars = 50 µm. (H) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group.<b>Supplemental Video 1:</b> Representative video of a coculture of DiO-labeled podocytes (green) and DiD-labeled PECs (red).

  PublisherDOI

• Podocyte-Specific Deletion of STAT3 in Krüppel-Like Factor 4–Related Experimental Podocytopathy

  Journal of the American Society of Nephrology · 2025-08-29

  articleOpen accessSenior authorCorresponding

  Key Points Detrimental effects of Krüppel-like factor 4 knockdown in podocytes were eliminated with the inhibition of signal transducer and activator of transcription 3 (STAT3) signaling specifically in podocytes. Human kidney biopsies with renal vasculitis demonstrated a glomerular enrichment of STAT3 downstream genes, which negatively correlated with eGFR. Deconvolution of the bulk RNA-seq from Nephrotic Syndrome Study Network showed an enrichment of STAT3 downstream genes in podocytes as compared with other cell clusters. Background Podocyte loss and parietal epithelial cell activation are features of subtypes of glomerulonephritis and FSGS. We recently reported that the podocyte-specific loss of Krüppel-like factor 4 ( Klf4 ΔPod ) triggers dysregulated glomerular signal transducer and activator of transcription 3 (STAT3) activation, podocyte loss with parietal epithelial cell activation and proliferation, leading to FSGS. Although pharmacologic systemic STAT3 inhibition attenuated this phenotype, it remains unclear whether the detrimental effects of Klf4 loss are primarily a result of dysregulated STAT3 activation intrinsically in podocytes. Methods Mice with the concurrent and conditional knockdown of Stat3 and Klf4 ( Klf4 ΔPod Stat3 ΔPod ) were generated and characterized. Expression arrays from kidney biopsies with various types of glomerular diseases, deposited in Nephroseq, were interrogated for glomerular expression of genes downstream of STAT3 signaling. Cell-specific modulation of STAT3 genes was determined using single-cell RNA sequencing–based proportional cell type deconvolution of bulk RNA-seq obtained from the Nephrotic Syndrome Study Network (NEPTUNE) FSGS and healthy controls. Results Klf4 ΔPod Stat3 ΔPod mice demonstrated no significant podocyte loss, parietal epithelial cell activation and proliferation, FSGS lesions, albuminuria, kidney dysfunction, and tubulointerstitial fibrosis and inflammation compared with the Klf4 ΔPod mice. Klf4 ΔPod Stat3 ΔPod mice also exhibited less glomerular myofibroblasts (+ α -smooth muscle actin) as compared with Klf4 ΔPod mice. Overall survival was restored in Klf4 ΔPod Stat3 ΔPod mice as compared with Klf4 ΔPod mice. Interrogation of expression arrays from human kidney biopsies with renal vasculitis demonstrated a glomerular enrichment of genes involved in canonical STAT3 signaling as compared with healthy controls, which negatively correlated with eGFR. Deconvolution of the bulk RNA-seq data from NEPTUNE showed an enrichment of these STAT3 genes in podocytes as compared with other glomerular cell clusters. Conclusions Collectively, these data demonstrate that inhibiting podocyte-specific STAT3 signaling was sufficient to counter the detrimental effects of Klf4 loss in podocytes and prevented albuminuria, accelerated podocyte loss, activation and proliferation of parietal epithelial cells, FSGS lesions, and kidney failure.

  PublisherDOI

• Characterization and classification of chronic kidney disease by spatial MIST and deep learning algorithm

  American Journal of Physiology-Renal Physiology · 2025-10-24

  articleOpen accessCorresponding

  In this study, we offer a novel spatial analysis of markers relevant to CKD, which may provide useful insights into disease progression. By using this spatial proximity data, we created a GNN model that is capable of classifying disease severity and identifying markers that are most important for its classification. This integrative approach offers a foundation for future studies aimed at developing clinically actionable tools for CKD diagnosis and prognosis.

  PublisherDOI

• A Novel Tool to Study Glomerular Endothelial Cell Biology

  Journal of the American Society of Nephrology · 2025-10-01

  articleSenior author
  PublisherDOI

• Supplemental figures for manuscript:

  Figshare · 2025-01-01

  otherOpen access1st authorCorresponding

  Supplemental Figure LegendsSupplementary Figure 1: Quantification of UACR, podocyte number, PEC activation, and glomerular Intercellular bridges in P-NTS (Probetex-nephrotoxic serum)-treated versus IgG-treated mice. (A) Urine albumin to creatinine ratio (UACR). N = 4 per group; *P &lt; 0.05, Kruskal-Wallis test with Dunn’s post-test, #P &lt; 0.05, Mann-Whitney test. (B)Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 20 glomeruli per mouse; ***P &lt; 0.001, ****P &lt; 0.0001, Kruskal-Wallis test with Dunn’s post-test. (C) Quantification of glomerular CD44 expression. N = 3 mice per group, 20 glomeruli per mouse; ****P &lt; 0.0001, Kruskal-Wallis test with Dunn’s post-test. (D)Representative images of PAS staining. Insets with arrows showing intercellular bridges. Scale bars = 20 µm, inset scale bars = 20 µm. (E) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group; *P &lt; 0.05, Mann-Whitney test. (F) Distribution of the number of bridges per glomerulus. N = 3 per group; 30 glomeruli per mouse. *P &lt; 0.05, Kruskal-Wallis test with Dunn’s post-test. Supplementary Figure 2: Quantification of UACR, podocyte number, PEC activation, and glomerular intercellular bridges in two murine models: LPS model (LPS versus DMSO) and DKD model (db/db versus db/+ mice at 8, 16, and 24 weeks of age). (A) Urine albumin to creatinine ratio (UACR). N = 4 per group; *P &lt; 0.05, Mann-Whitney test. (B)Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 30 glomeruli per mouse; non-significant (NS), Mann-Whitney test. (C) Representative images of PAS staining. Scale bars = 20 µm. (D) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group. (E) UACR ratio of db/+ and db/db mice. N = 3 per group; *P &lt; 0.05, Mann-Whitney test. (F) Quantification of podocyte number (WT1+ cells per glomerular cross-sectional area). N = 3 mice per group, 20 glomeruli per mouse; **P &lt; 0.01, ****P &lt; 0.0001, two-way ANOVA with Tukey’s post-test. (G) Representative images of PAS staining from db/+ and db/db mice. Scale bars = 50 µm. (H) Percent of glomeruli with ≥1 intercellular bridges. Percentage calculated using the total number of glomeruli per tissue cross section. N = 3 per group.Supplemental Movie: Representative video of a coculture of DiO-labeled podocytes (green) and DiD-labeled PECs (red).

  PublisherDOI

• Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

  UNC Libraries · 2025-05-13

  articleOpen access

  Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction &gt;99% or antigen &lt;1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P&thinsp;=&thinsp;.002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve&thinsp;=&thinsp;0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

  PublisherDOI

Recent grants

• Role of KLF15 in podocyte differentiation

  NIH · $58k · 2012–2013

• Podocyte-Proximal Tubule Interactions in Diabetic Kidney Disease

  NIH · $3.5M · 2017–2029

• The Role of KLF15 as a transcriptional regulator of podocyte differentiation

  NIH · $917k · 2014–2019

• Role of KLF15 in proximal tubule metabolism

  NIH · 2018–2028

Frequent coauthors

• John Cijiang He

  Mount Sinai Hospital

  72 shared

• Nehaben A. Gujarati

  Stony Brook University

  66 shared

• Farrukh M. Koraishy

  Stony Brook University

  54 shared

• Chelsea C. Estrada

  Stony Brook School

  44 shared

• Yiqing Guo

  Stony Brook University

  41 shared

• Robert Bronstein

  Stony Brook University

  36 shared

• Joel Saltz

  36 shared

• Richard A. Moffitt

  31 shared