Biology subjects

Gautam, P.

Publications and source records attributed to Gautam, P..

18 recordsLinked to original sources

bioRxiv2026

Concurrent AXL inhibition enhances RAS and ERK inhibitor efficacy in KRAS-mutant pancreatic and lung cancer

Resistance limits the clinical efficacy of RAS inhibitors. We applied chemical and genetic screens and identified the AXL receptor tyrosine kinase as a driver of resistance to RAS-ERK inhibition. We determined that combination treatment with the AXL inhibitor bemcentinib (AXLi) together with the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 (RASi) or the ERK-selective inhibitor SCH772984 (ERKi) significantly enhanced growth suppression in human KRAS-mutant pancreatic and lung cancer models. Combined AXLi and RASi treatment of human KRAS-mutant pancreatic cell line-derived xenograft tumors synergistically suppressed ERK activation and MYC expression, and caused tumor regression. Analyses of immunocompetent mouse allograft pancreatic tumor models revealed a largely tumor cell-intrinsic response to inhibitor treatment. We identified an unexpected mechanism whereby KRAS inhibition upregulated the AXL ligand GAS6, activating AXL but inducing an AXL-dependent adaptive resistance mechanism wherein AXL antagonizes RASi efficacy. Our observations support concurrent AXL inhibition as a strategy to enhance RAS inhibitor clinical efficacy. STATEMENT OF SIGNIFICANCEOur findings identify AXL as a driver of resistance to RAS inhibitors, establishing a combination strategy to overcome resistance and enhance RAS inhibitor therapeutic efficacy in KRAS-mutant cancer by maximally inhibiting oncogenic RAS signaling.

cancer biology↗

bioRxiv2026

EpiESM-GA: Resource-Efficient Protein Foundation Model Features for Equitable B-Cell Epitope Prediction

Prediction of B-cell epitopes can assist in reducing costly wet-lab screening in vaccine design, diagnostics, and antibody discovery. However, current predictors often suffer from noisy labels, weak generalization, and structure-dependent workflows. Here we present EO_SCPLOWPIC_SCPLOWESM-GA, an efficient sequenceonly pipeline for linear B-cell epitope prediction. Positive and negative peptide examples are collected from IEDB, which provides experimentally tested epitopes and distinguishes positive and negative epitope records based on assay evidence(Vita et al., 2019). Each peptide is encoded with a frozen ESM-2 protein language model: a bidirectional transformer producing amino acid embeddings for downstream structure and function tasks (Lin et al., 2023). Mean-pooled embeddings are further compressed into a compact 420-feature representation with a genetic algorithm and classified with lightweight Random Forest, XGBoost, or MLP heads. This avoids foundation-model fine-tuning, reduces the number of trainable parameters, improves interpretability, and enables low-resource deployment. On an IEDB-derived benchmark, EO_SCPLOWPIC_SCPLOWESM-GA attains 0.880{+/-} 0.004 AUC-ROC, 0.852{+/-} 0.005 PR-AUC, 82.0 {+/-} 0.6% accuracy, 0.79 {+/-} 0.01 F1, and 0.74{+/-} 0.01 MCC, outperforming dense ESM-2 features and baselines LBCE-XGB, EpitopeVec, and BepiPred-2.0 (mean{+/-} std over five independent random seeds). The framework shows how frozen protein foundation models can enable pandemic preparedness, peptide vaccine prioritization, diagnostic antigen screening, and equitable computational immunology.

bioinformatics↗

bioRxiv2026

HALPred-B: Host-Aware Linear B-Cell Epitope Prediction: Challenges, Limitations, and Variability Across Species

Predicting linear B-cell epitopes is a basic immunoinformatics task that has a direct impact on vaccine design and antibody engineering. Recent advances in machine learning have improved predictive performance, but most existing approaches are trained on aggregated datasets and assume that antigenic patterns are conserved across host organisms. This assumption ignores the immunological variability depending on the host and prevents generalizing the model across species. This is the first systematic host-wise evaluation where we present a systematic machine learning-based analysis of host-aware linear B-cell epitope prediction using curated datasets from the Immune Epitope Database (IEDB). We build separate datasets for human, mouse, and non-human primate hosts and assess several classification models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, and K-Nearest Neighbors (KNN). The models exploit feature representations derived from sequences, such as AAIndex descriptors, biochemical properties from ExPASy, and dipeptide composition. Our results show that predictive performance differs substantially across hosts. Models achieve up to 86.07% accuracy and 0.93 ROC-AUC on human datasets but lower performance on mouse and non-human primate datasets. This gap underlies dataset bias and sequence distribution differences, as well as the inability of existing features to capture host-specific immunological context. These results indicate that the prediction of linear B-cell epitopes is intrinsically host-specific, and a single global model does not generalize well across species. We propose to incorporate host-aware modeling strategies and organism-specific features for enhanced predictive reliability and biological relevance.

bioinformatics↗

bioRxiv2025

Complex I Drives Glutamine-Dependent TCA Cycle to Support Viability of MYChigh Breast Cancer Cells

In many cancers, stably elevated MYC levels drive persistent and concerted activation of cell growth promoting anabolic programs and the cell cycle in ways that are distinct from normal cells. Therefore, synthetic-lethal strategies to target MYC reprograming of these pathways may identify new selective anticancer therapies for the treatment of MYChigh tumors. Here, we identify enhanced mitochondrial respiration as a hallmark of MYC overexpressing cancer cells. Mitochondrial respiration sustains the TCA cycle by regenerating NAD+ through complex I-mediated oxidation of NADH. Metabolic carbon tracing analysis revealed that MYC shifts TCA cycles carbon source from glucose to glutamine. Inhibition of the glutamine-fueled TCA cycle using NAD+-depleting complex I inhibitors resulted in MYC-dependent synthetic lethality in breast cancer cells. In mouse models of MYChigh tumors, persistent inhibition of tumor growth was achieved through combined inhibition of complex I and glutaminolysis. Our results suggest that the high respiration rate observed in MYChigh cells supports glutamine carbon-enriched TCA cycle, rendering MYChigh tumors selectively vulnerable to inhibitors of mitochondrial respiration and glutaminolysis.

cancer biology↗

bioRxiv2024

Deciphering tumour microenvironment and elucidating the origin of cancer cells in ovarian clear cell carcinoma

Ovarian clear cell carcinoma (CCC) has an East Asian preponderance. It is associated with endometriosis, a benign condition where endometrial (inner lining of the uterus) tissue is found outside the uterus and on the peritoneal surface, in the abdominal or pelvic space. CCC is relatively more resistant to conventional chemotherapy compared to other ovarian cancer subtypes and is associated with a poorer prognosis. In this study, we recruited and obtained tumour tissues from seven patients across the four stages of CCC. The tumour and the tumour microenvironment (TME) from 7 CCC patients spanning clinical stages 1-4 were transcriptionally profiled using high-resolution scRNA-seq to gain insight into CCCs biological mechanisms. Firstly, we built a scRNA-seq resource for the CCC tumour microenvironment (TME). Secondly, we identified the different cell type proportions and found high levels of immune infiltration in CCC. Thirdly, since CCC is associated with endometriosis, we compared CCC with two publicly available endometriosis scRNA-seq datasets. The CCC malignant cells showed similarities with glandular secretory and ciliated epithelial cells found in endometriosis. Finally, we determined the differences in cell-cell communication between various cell types present in CCC TME and endometriosis conditions to gain insights into the transformations in CCC.

cancer biology↗

bioRxiv2024

βA3/A1-crystallin is an epigenetic regulator of histone deacetylase 3 (HDAC3) in the retinal pigmented epithelial (RPE) cells

Introductory paragraphThe retinal pigmented epithelial (RPE) cells maintain retinal homeostasis, and alterations in their function contribute to non-exudative age-related macular degeneration (AMD)1,2. Here, we explore the intricate relationship between RPE cells, epigenetic modifications, and the development of AMD. Importantly, the study reveals a substantial decrease in histone deacetylase 3 (HDAC3) activity and elevated histone acetylation in the RPE of human AMD donor eyes. To investigate epigenetic mechanisms in AMD development, we used a mouse model with RPE-specific Cryba1 knockout3-5, revealing that the loss of {beta}A3/A1-crystallin selectively reduces HDAC3 activity, resulting in increased histone acetylation. {beta}A3/A1-crystallin activates HDAC3 by facilitating its interaction with the casein kinase II (CK2) and phosphorylating HDAC3, as well as by regulating intracellular InsP6 (phytic acid) levels, required for activating HDAC3. These findings highlight a novel function of {beta}A3/A1-crystallin as an epigenetic regulator of HDAC3 in the RPE cells and provide insights into potential therapeutic strategies in non-exudative AMD.

cell biology↗

bioRxiv2023

Genomic perspectives of SARS CoV-2 in liver disease patients with its clinical correlation: A single centre retrospective study

BackgroundSevere Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2), is a causative agent of current global pandemic of Coronavirus disease-19 (COVID-19). Due to propagated outbreak and global vaccination drive an immense immunological selection pressure has been exerted on SARS CoV-2 leading to evolution of new variants. This study was performed to compare the mutational and clinical profile of liver disease patients infected with different variants of SARS CoV-2. MethodologyThis was a single-centre, retrospective, cohort study in which clinicogenomic analysis of liver disease (LD) patients infected with SARS CoV-2 was performed. Complete demographic and clinical details were retrieved from Hospital Information System (HIS). QC-threshold passed FASTA files containing sequences from COVID-19 patients (n=174) were compared with a reference genome of SARS-CoV-2 isolate named Wuhan-Hu-1 (NCBI Reference Sequence: NC_045512.2) for mutational analysis. ResultsOut of 232 finally analysed patients 137 (59.1%) were LD-CoV (+) and 95 (40.9%) were LD-CoV(-). LD patients with comorbidities were affected more with COVID-19 (p=0.002). On comparing the outcome in the terms of mortality, LD-CoV (+) had 2.29 times (OR 2.29, CI 95%, 1.25-4.29) higher of odds of succumbing to COVID-19 (p=0.006). Multivariate regression analysis revealed, abdominal distention (p=0.05), severe COVID-19 pneumonia (p=0.046) and the change in serum bilirubin levels (p=0.005) as well as Alkaline phosphatase (ALP) levels (p=0.003) to have an association with adverse outcome in LD patients with COVID-19. In Delta (22%) and Omicron (48%) groups, Spike gene harboured maximum mutations. On comparing the mutations between LD-CoV(+/D) and LD-CoV(+/O) a total of nine genes had more mutations in LD-CoV(+/O) whereas three genes had more mutations in LD-CoV(+/D). ConclusionWe concluded that LD patients are more susceptible to COVID-19 as compared to a healthy adult with associated adverse clinical outcomes in terms of mortality and morbidity. Therefore this special group should be given priority while devising and introducing new vaccination and vaccination policies. The infection with different variants did not result in different outcome in our group of patients.

microbiology↗

bioRxiv2020

Stem cell basis of a host driven transmission of antigen packed aerosols: a novel mechanism of natural vaccination for tuberculosis

Natural vaccination against pathogens are known to be achieved by herd-immunity i.e. infected human host provide immunity to the community by spreading the pathogen. Whether, infected human hosts transmit vesicle packed aerosols of pathogens antigen for natural vaccination of the community has not yet been considered. We have explored a traditional healing method of aerosol-inoculation against small pox and tuberculosis in the Sualkuchi-Hajo cultural complex of Kamarupa, an ancient Indian region known for tantra-based healing and spirituality. In the aerosol-inoculation method against TB, selected persons with TB (later identified as smear negative TB subject) are encouraged to spread good nigudah in the community by Kirtan chanting; the good Nigudah are thought to be present within bad-nigudah or invisible krimis (tiny flesh eating living being mentioned in ancient Indias medicinal text Caraka Samhita and Atharva Veda). A 15-years of contact TB investigation study, as well as laboratory study of aerosol obtained from smear negative PTB (SN-PTB) subjects led to the identification of good Nigudah as extracellular vesicles (EVs) filled with Mtb-antigen ESAT-6. We then developed a mouse model of aerosol-inoculation using SN-PTB subject derived aerosol EVs, and identified Mtb infected mesenchymal stem cells (MSCs) of the lung as the putative source of the ESAT-6+ EVs. These Mtb infected MSCs reprogram to altruistic stem cell (ASC) phenotype, which then secrete ESAT-6+ EVs to the aerosols; healthy mice receiving the aerosol develop Mtb specific herd immunity. These results expedite our ongoing work on the innate defense mechanism of ASCs against pathogen, and provide a novel mechanism of natural vaccination, where the host extracts appropriate antigens from a pathogen, and then spread it in the community via aerosols.

immunology↗

bioRxiv2020

comboFM: leveraging multi-way interactions for systematic prediction of drug combination effects

We present comboFM, a machine learning framework for predicting the responses of drug combinations in preclinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrated high predictive performance of comboFM in various prediction scenarios using data from cancer cell line drug screening. Subsequent experimental validation of a set of previously untested drug combinations further supported the practical and robust applicability of comboFM. For instance, we confirmed a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

bioinformatics↗

bioRxiv2020

CIP2A interacts with TopBP1 and is selectively essential for DNA damage-induced basal-like breast cancer tumorigenesis

Despite saturated genetic profiling of breast cancers, oncogenic drivers for the clinically challenging basal-like breast cancer (BLBC) subtype are still poorly understood. Here, we demonstrate that CIP2A is selectively essential for DNA damage-induced initiation of mouse BLBC tumors, but not of other cancer types. Mechanistically, CIP2A was discovered genome-widely the closest functional homologue for DNA-damage proteins TopBP1, RHNO, POLQ, NBN and PARP1. CIP2A directly interacts with the ATR-activation domain of TopBP1, and dampens both, chromatin binding of TopBP1 and RAD51, and G2/M checkpoint in DNA-damaged cells. CIP2A also drives BLBC-associated proliferative MYC and E2F1 signaling. Consistently with high DNA-damage response activity BLBCs, and CIP2As novel role in checkpoint signaling, CIP2A was found essential for DNA-damaged, and BRCA-mutant BLBC cells. Selective role for CIP2A as BLBC driver was supported by association of high CIP2A expression with poor patient prognosis only in BLBC, but not in other breast cancer types. Therapeutically, small molecule reactivators of PP2A (SMAPs) phenocopy CIP2A-dependent DNA damage response, and inhibit in vivo growth of patient-derived BLBC xenograft. In summary, we discover sub-type selective essential role for CIP2A in BLBC initiation and maintenance that can be explained by its newly discovered association with DNA-damage response, coordinated with regulation of proliferative signaling. The results also identify therapeutic strategy for CIP2A-dependent BLBCs.

cancer biology↗

bioRxiv2020

Patient-tailored design of AML cell subpopulation-selective drug combinations

The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

systems biology↗

bioRxiv2020

Potential modes of COVID-19 transmission from human eye revealed by single-cell atlas

There is a pressing urgency to understand the entry route of SARS-CoV-2 viruses into the human body. SARS-CoV-2 viruses enter through ACE2 receptors after the S proteins of the virus are primed by proteases such as TMPRSS2. Most studies focused on the airway epithelial and lung alveolar cells as the route of infection, while the mode of transmission through the ocular route is not well established. Here, we profiled the presence of SARS-CoV-2 receptors and receptor-associated enzymes at single-cell resolution of thirty-three human ocular cell types. We identified unique populations of corneal cells with high ACE2 expression, among which the conjunctival cells co-expressed both ACE2 and TMPRSS2, suggesting that they could serve as the entry points for the virus. Integrative analysis further models the signaling and transcription regulon networks involved in the infection of distinct corneal cells. Our work constitutes a unique resource for the development of new treatments and management of COVID-19.

genomics↗

bioRxiv2020

Multiobjective optimization identifies cancer-selective combination therapies

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

bioinformatics↗

bioRxiv2020

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

A caveat of cancer cell line models is that their molecular and functional profiling is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge is how to make an integrated use of omics profiles of cancer cell lines for reliable discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics profiling studies across 12 research laboratories for 2018 cell lines. To account for relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. Extension of the approach identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient cells on RNA helicases. HighlightsO_LIA comprehensive meta-analysis of 53 multi-modal omics profiles of >2000 cancer cell lines from 12 research laboratories C_LIO_LIAn unexpected lack of consistency between TMT-labelled and non-labelled global proteomic profiles C_LIO_LIA non-parametric approach to integrate omics profiles from multiple laboratories and to identify robust molecular patterns in individual cell lines C_LIO_LIThe multi-modal data integration reveals novel drivers and potential therapeutic targets, including ECHDC1 in breast cancers and DDX27 in PTEN mutant cancers. C_LI

cancer biology↗

bioRxiv2019

Diversification of Reprogramming Trajectories Revealed by Parallel Single-cell Transcriptome and Chromatin Accessibility Sequencing

To unravel the mechanism of human cellular reprogramming process at single-cell resolution, we performed parallel scRNA-Seq and scATAC-Seq analysis. Our analysis reveals that the cells undergoing reprogramming proceed in an asynchronous trajectory and diversify into heterogeneous sub-populations. BDD2-C8 fluorescent probe staining and negative staining for CD13, CD44 and CD201 markers, could enrich for the GDF3+ early reprogrammed cells. Combinatory usage of the surface markers enables the fine segregation of the early-intermediate cells with diverse reprogramming propensities. scATAC-Seq analysis further uncovered the genomic partitions and transcription factors responsible for the regulatory phasing of reprogramming process. Binary choice between a FOSL1 or a TEAD4-centric regulatory network determines the outcome of a successful reprogramming. Altogether, our study illuminates the multitude of diverse routes transversed by individual reprogramming cells and presents an integrative roadmap for identifying the mechanistic part-list of the reprogramming machinery.

genomics↗

bioRxiv2019

Parallel Bimodal Single-cell Sequencing of Transcriptome and Chromatin Accessibility

We developed ASTAR-Seq (Assay for Single-cell Transcriptome and Accessibility Regions) integrated with automated microfluidic chips, which allows for parallel sequencing of transcriptome and chromatin accessibility within the same single-cell. Using ASTAR-Seq, we profiled 192 mESCs cultured in serum+LIF and 2i medium, 424 human cell lines including BJ, K562, JK1, and Jurkat, and 480 primary cells undergoing erythroblast differentiation. Integrative analysis using Coupled NMF identified the distinct sub-populations and uncovered sets of regulatory regions and the respective target genes determining their distinctions. Analysis of epigenetic regulomes further unravelled the key transcription factors responsible for the heterogeneity observed.

genomics↗

bioRxiv2018

A normalized drug response metric improves accuracy and consistency of drug sensitivity quantification in cell-based screening

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

bioinformatics↗

bioRxiv2017

Insights into regeneration from the genome, transcriptome and metagenome analysis of Eisenia fetida

Earthworms show a wide spectrum of regenerative potential with certain species like Eisenia fetida capable of regenerating more than two-thirds of their body while other closely related species, such as Paranais litoralis seem to have lost this ability. Earthworms belong to the phylum annelida, in which the genomes of the marine oligochaete Capitella telata, and the freshwater leech Helobdella robusta have been sequenced and studied. The terrestrial annelids, in spite of their ecological relevance and unique biochemical repertoire, are represented by a single rough genome draft of Eisenia fetida (North American isolate), which suggested that extensive duplications have led to a large number of HOX genes in this annelid. Herein, we report the draft genome sequence of Eisenia fetida (Indian isolate), a terrestrial redworm widely used for vermicomposting assembled using short reads and mate-pair reads. An in-depth analysis of the miRNome of the worm, showed that many miRNA gene families have also undergone extensive duplications. Genes for several important proteins such as sialidases and neurotrophins were identified by RNA sequencing of tissue samples. We also used de novo assembled RNA-Seq data to identify genes that are differentially expressed during regeneration, both in the newly regenerating cells and in the adjacent tissue. Sox4, a master regulator of TGF-beta induced epithelial-mesenchymal transition was induced in the newly regenerated tissue. The regeneration of the ventral nerve cord was also accompanied by the induction of nerve growth factor and neurofilament genes. The metagenome of the worm, characterized using 16S rRNA sequencing, revealed the identity of several bacterial species that reside in the nephridia of the worm. Comparison of the bodywall and cocoon metagenomes showed exclusion of hereditary symbionts in the regenerated tissue. In summary, we present extensive genome, transcriptome and metagenome data to establish the transcriptome and metagenome dynamics during regeneration.

genomics↗