Biology subjects

Chakravarty, D.

Publications and source records attributed to Chakravarty, D..

15 recordsLinked to original sources

bioRxiv2026

Innate Immune Dysregulation Precedes Clinical Onset in Neuromyelitis Optica

Neuromyelitis optica spectrum disorder (NMOSD) is a disabling inflammatory central nervous system (CNS) disease characterized by pathogenic aquaporin 4 (AQP4) autoantibodies. The biological events that precede clinical onset remain unknown. In a unique longitudinal NMOSD cohort from the U.S. Department of Defense Serum Repository, with samples collected up to 24 years before onset, we applied discovery proteomics (~5,400 proteins), pan-viral antibody profiling (VirScan), and in silico analyses to characterize preclinical NMOSD versus matched healthy controls. Innate immune activation, particularly CXCL1 elevation, was the dominant immunological feature of preclinical NMOSD, preceding onset by ~12.7 years. AQP4-IgG was detectable more than 3 years before onset, while CNS injury markers increased only after disease manifestation. VirScan identified enhanced CMV-specific antibody reactivity following AQP4- IgG seroconversion, and network analyses revealed interactions between NMOSD-associated and CMV-derived proteins. Together, these findings establish a novel multi-hit model of NMOSD, with implications for understanding antibody-mediated autoimmunity more broadly.

immunology↗

bioRxiv2026

CX3CR1+ microglia/macrophages, activated T cells, and IFN-γ-driven stimulation confer age-dependent protective immunity in young-adult mice following β-coronavirus infection.

Age-dependent variation in the immune response is a critical determinant of host susceptibility, disease severity, and long-term sequelae in coronavirus infections, a principle strikingly demonstrated by the COVID-19 pandemic caused by SARS-CoV-2. Although primarily pneumotropic, coronaviruses carry significant neurotropic potential, driving neurological complications whose severity is profoundly determined by host age. Elderly individuals and often children suffer disproportionately severe disease, whereas young adults relatively mount protective responses; yet the cellular and molecular determinants of this age-dependent neuroprotection remain poorly characterized. Using juvenile and young-adult C57BL/6 mice intracranially inoculated with {beta}-coronavirus MHV-RSA59, a murine equivalent of human coronaviruses, we examined immune dynamics at days 5 (innate-acute), 7 (innate-to-adaptive transition), and 30 (chronic) post-infection. Young-adult mice exhibited only occasional demyelination, in stark contrast to the extensive demyelination observed across all spinal cord levels in juveniles. This differential outcome was attributable to enhanced age-dependent immune maturation, characterized by efficient T cell-microglia/macrophage crosstalk enabling effective viral control by day 7 post-infection. Young adults displayed greater glial activation, heightened cytokine release, CX3CR1+ microglia activation, and augmented CNS trafficking of CX3CR1+ and MHC II+ monocytes/macrophages, IFN-{gamma}+ CD4+/CD8+ T cells, and CXCR3+ effector T cells relative to juveniles. Reduced naive T cell frequencies and elevated effector/central memory T cell populations in cervical lymph nodes further indicate a robust adaptive memory response conferring long-term protection. Additionally, greater regulatory T cell accumulation in young adults facilitates timely suppression of excessive neuroinflammation as viral burden subsides. Together, these findings define the age-dependent immune landscape that underpins neuroprotection in {beta}-coronavirus infection.

immunology↗

bioRxiv2026

A dramatic protein fold switch powers a bactericidal nanomachine

Fold switching, where a protein region interconverts between entirely distinct three-dimensional structures, is emerging as vital for certain protein functions. Here, we report a remarkable example in the F7 pyocin, a phage tail-like bactericidal nanomachine. Cryogenic electron microscopy and tomography reveal that a 163-residue segment of the central tail fiber undergoes a dramatic transition--from a trimeric -helical coiled-coil to a triangular {beta}-prism--upon binding to the bacterial cell surface. This massive fold switch remodels the tail tip, ejects the internal tape measure protein, and drives membrane puncture. Site-directed mutations that selectively destabilize the {beta}-prism conformation completely abolish bactericidal activity without impairing particle assembly, implying that the energy released during this transition powers penetration. AlphaFold-based analyses further predict similar large-scale coiled-coil to {beta}-prism switches in diverse non-contractile phage tails. This discovery reveals a sophisticated, ATP-independent strategy for microbial warfare and opens exciting possibilities for engineering next-generation bacteriocins to combat multidrug-resistant pathogens.

microbiology↗

bioRxiv2024

Microbial lysates as low-cost serum replacements in cellular agriculture media formulation

Cultivated meat, the process of generating meat in vitro without sacrificing animals, is a promising alternative to the traditional practice of livestock agriculture. However, the success of this field depends on finding sustainable and economical replacements for animal-derived and expensive fetal bovine serum (FBS) that is typically used in cell culture processes. Here, we outline an effective screening process to vet the suitability of microbial lysates to support the growth of immortalized bovine satellite cells (iBSCs) and mackerel (Mack1) cells. We show that easily producible, low-cost whole-cell lysates from Vibrio natriegens can be used to create serum-free media for the long-term growth of iBSCs. The optimized medium, named "VN40" (basal B8 media containing Vibrio natriegens lysate proteins at 40 {micro}g/mL), outperforms previously established serum-free media while maintaining cell phenotype and myogenicity. Overall, this study shows a novel approach to producing serum-free media for cultivated meat production using microbially-derived lysates.

bioengineering↗

bioRxiv2024

Machine learning predictions improve identification of real-world cancer driver mutations

Characterizing and validating which mutations influence development of cancer is challenging. Machine learning has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluated multiple computational methods for identifying cancer driver alterations. For identifying known drivers, methods incorporating protein structure or functional genomic data outperformed methods trained only on evolutionary data. We further validated VUSs annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N=7,965 and 977). "Pathogenic" VUSs in KEAP1 and SMARCA4 identified by several methods were associated with worse survival, unlike "benign" VUSs. "Pathogenic" VUSs exhibited mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. Despite training primarily on germline, rather than somatic, mutation data, computational predictions contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.

genomics↗

bioRxiv2024

Sequence clustering confounds AlphaFold2

Though typically associated with a single folded state, some globular proteins remodel their secondary and/or tertiary structures in response to cellular stimuli. AlphaFold21 (AF2) readily generates one dominant protein structure for these fold-switching (a.k.a. metamorphic) proteins2, but it often fails to predict their alternative experimentally observed structures3,4. Wayment-Steele, et al. steered AF2 to predict alternative structures of a few metamorphic proteins using a method they call AF-cluster5. However, their Paper lacks some essential controls needed to assess AF-clusters reliability. We find that these controls show AF-cluster to be a poor predictor of metamorphic proteins. First, closer examination of the Papers results reveals that random sequence sampling outperforms sequence clustering, challenging the claim that AF-cluster works by "deconvolving conflicting sets of couplings." Further, we observe that AF-cluster mistakes some single-folding KaiB homologs for fold switchers, a critical flaw bound to mislead users. Finally, proper error analysis reveals that AF-cluster predicts many correct structures with low confidence and some experimentally unobserved conformations with confidences similar to experimentally observed ones. For these reasons, we suggest using ColabFold6-based random sequence sampling7-augmented by other predictive approaches-as a more accurate and less computationally intense alternative to AF-cluster.

bioinformatics↗

bioRxiv2023

AlphaFold2 has more to learn about protein energy landscapes

Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two published enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins whose experimentally determined conformations were likely in AF2s training set. Combining all models, AF2 predicted fold switching with a modest success rate of [~]25%, indicating that it does not readily sample both experimentally characterized conformations of most fold switchers. Further, AF2s confidence metrics selected against models consistent with experimentally determined fold-switching conformations in favor of inconsistent models. Accordingly, these confidence metrics-though suggested to evaluate protein energetics reliably-did not discriminate between low and high energy states of fold-switching proteins. We then evaluated AF2s performance on seven fold-switching proteins outside of its training set, generating >159,000 models in total. Fold switching was accurately predicted in one of seven targets with moderate confidence. Further, AF2 demonstrated no ability to predict alternative conformations of two newly discovered targets without homologs in the set of 93 fold switchers. These results indicate that AF2 has more to learn about the underlying energetics of protein ensembles and highlight the need for further developments of methods that readily predict multiple protein conformations.

bioinformatics↗

bioRxiv2023

Colabfold predicts alternative protein structures from single sequences, coevolution unnecessary for AF-cluster

Though typically associated with a single folded state, globular proteins are dynamic and often assume alternative or transient structures important for their functions1,2. Wayment-Steele, et al. steered ColabFold3 to predict alternative structures of several proteins using a method they call AF-cluster4. They propose that AF-cluster "enables ColabFold to sample alternate states of known metamorphic proteins with high confidence" by first clustering multiple sequence alignments (MSAs) in a way that "deconvolves" coevolutionary information specific to different conformations and then using these clusters as input for ColabFold. Contrary to this Coevolution Assumption, clustered MSAs are not needed to make these predictions. Rather, these alternative structures can be predicted from single sequences and/or sequence similarity, indicating that coevolutionary information is unnecessary for predictive success and may not be used at all. These results suggest that AF-clusters predictive scope is likely limited to sequences with distinct-yet-homologous structures within ColabFolds training set.

bioinformatics↗

bioRxiv2022

Identification of a covert evolutionary pathway between two protein folds

Although homologous protein sequences are expected to adopt similar structures, some amino acid substitutions can interconvert -helices and {beta}-sheets. Such fold switching may have occurred over evolutionary history, but supporting evidence has been limited by the: (1) abundance and diversity of sequenced genes, (2) quantity of experimentally determined protein structures, and (3) assumptions underlying the statistical methods used to infer homology. Here, we overcame these barriers by applying multiple statistical methods to a family of ~600,000 bacterial response regulator proteins. We found that their homologous DNA-binding subunits assume divergent structures: helix-turn-helix versus -helix+{beta}-sheet (winged helix). Phylogenetic analyses, ancestral sequence reconstruction, and AlphaFold2 models indicated that amino acid substitutions facilitated a switch from helix-turn-helix into winged helix. This structural transformation likely expanded DNA-binding specificity. Our approach uncovers an evolutionary pathway between two protein folds and provides methodology to identify secondary structure switching in other protein families.

bioinformatics↗

bioRxiv2022

Ifit2 regulates murine-coronavirus spread to the spinal cord white matter and its associated myelin pathology

Ifit2, an interferon-induced protein with tetratricopeptide repeats 2, plays a critical role in restricting neurotropic murine {beta}-coronavirus RSA59 infection. RSA59 intracranial injection of Ifit2 deficient (-/-) compared to wild type (WT) mice results in impaired acute microglial activation, associated with reduced CX3CR1 expression, which consecutively limits migration of peripheral lymphocytes into the brain, leading to impaired virus control followed by severe morbidity and mortality. While the protective role of Ifit2 is established for acute viral encephalitis, less is known about its influence on demyelination during the chronic phase of RSA59 infection. Our current study demonstrates that Ifit2 deficiency causes extensive RSA59 viral spread throughout both the spinal cord grey and white matter and is associated with impaired CD4+ T cell infiltration. Cervical lymph nodes of RSA59 infected Ifit2-/- mice showed reduced activation of CD4+ T cells and impaired IFN{gamma} expression during acute encephalomyelitis. Furthermore, blood-brain-barrier integrity was preserved in the absence of Ifit2 as evidenced by integral, tight junction protein ZO-1 expression surrounding the meninges and blood vessels and decreased Texas red dye uptake. In contrast to WT mice exhibiting only sparse myelin loss, the chronic disease phase in Ifit2-/- mice was associated with severe demyelination and persistent viral load, even at low infection doses. Overall, our study highlights that Ifit2 provides antiviral functions by promoting acute neuroinflammation and thereby aiding virus control and limiting severe demyelination. Author SummaryThe role of interferons in providing protective immunity against viral spread and pathogenesis is well known. Interferons execute their function by inducing certain genes collectively called as interferon stimulated genes (ISGs) among which Interferon-induced protein with tetratricopeptide repeats 2, Ifit2, is known for restricting neurotropic viral replication and spread in the brain. So far, not much has been investigated about its role in viral spread to the spinal cord and its associated myelin pathology. Towards this our study using neurotropic murine-{beta}-coronavirus and Ifit2 deficient mice demonstrate that Ifit2 deficiency causes extensive viral spread throughout grey and white matter of spinal cord accompanied by impaired microglial activation and CD4+ T cell infiltration. Furthermore, infected Ifit2 deficient mice showed impaired activation of T cells in cervical lymph node and Blood-Brain-Barrier was relatively intact. Ifit2 deficient mice developed viral induced severe chronic neuroinflammatory demyelination accompanied by the presence of ameboid shaped phagocytotic microglia/macrophages.

neuroscience↗

bioRxiv2022

AlphaFold2 fails to predict protein fold switching

AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that contribute to this inaccuracy, we tested AlphaFold2s performance on 98 fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted and two experimentally determined structures of each fold-switching protein. Overall, 94% of AlphaFold2 predictions captured one experimentally determined conformation but not the other. Despite these biased results, AlphaFold2s estimated confidences were moderate-to-high for 74% of fold-switching residues, a result that contrasts with overall low confidences for intrinsically disordered proteins, which are also structurally heterogeneous. To investigate factors contributing to this disparity, we quantified sequence variation within the multiple sequence alignments used to generate AlphaFold2s predictions of fold-switching and intrinsically disordered proteins. Unlike intrinsically disordered regions, whose sequence alignments show low conservation, fold-switching regions had conservation rates statistically similar to canonical single-fold proteins. Furthermore, intrinsically disordered regions had systematically lower prediction confidences than either fold-switching or single-fold proteins, regardless of sequence conservation. AlphaFold2s high prediction confidences for one fold-switching conformation corroborate previous work showing that machine-learning-based structure predictors fail to capture other fundamental biophysical features of proteins such as their folding pathways. Our results emphasize the need to look at protein structure as an ensemble and suggest that systematic examination of fold-switching sequences may reveal propensities for multiple stable secondary and tertiary structures.

bioinformatics↗

bioRxiv2020

PT150 is a modulator of glucocorticoid and androgen receptors with antiviral activity against SARS-CoV-2.

PT150 is a clinical stage molecule, taken orally, with a strong safety profile having completed Phase 1 and Phase 2 clinical trials for its original use as an anti-depressant. It has an active IND for COVID-19. Antiviral activities have been found for PT150 and other members of its class in a variety of virus families; thus, it was now tested against SARS-CoV-2 in human bronchial epithelial lining cells and showed effective 90% inhibitory antiviral concentration (EC90) of 5.55 M. PT150 is a member of an extended platform of novel glucocorticoid receptor (GR) and androgen receptor (AR) binding molecules. In vivo, their predominant net effect is one of systemic glucocorticoid antagonism, but they also show direct downregulation of AR and minor GR agonism at the cellular level. We hypothesize that anti-SARS-CoV-2 activity depends in part on this AR downregulation through diminished TMPRSS2 expression and modulation of ACE2 activity. Given that hypercortisolemia is now suggested to be a significant co-factor for COVID-19 progression, we also postulate an additive role for its potent immunomodulatory effects through systemic antagonism of cortisol.

microbiology↗

bioRxiv2019

Evolutionary Origins of Recurrent Pancreatic Cancer

Surgery is the only curative option for Stage I/II pancreatic cancer, nonetheless most patients will recur after surgery and die of their disease. To identify novel opportunities for management of recurrent pancreatic cancer we performed whole exome or targeted sequencing of 10 resected primary cancers and matched intrapancreatic recurrences or distant metastases. We identified that adjuvant or first-line platinum therapy corresponds to an increased mutational burden of recurrent disease. Recurrent disease is enriched for mutations that activate Mapk/Erk and PI3K/AKT signaling and develops from a monophyletic or polyphyletic origin. Treatment induced genetic bottlenecks lead to a modified genetic landscape and subclonal heterogeneity for driver gene alterations in part due to intermetastatic seeding. In one patient what was believed to be recurrent disease was an independent (second) primary tumor. These findings advocate for combination therapies with immunotherapy and routine post-treatment sampling as a component of management of recurrent pancreatic cancer.

cancer biology↗

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗