RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation.

BACKGROUND: Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg). METHODS: Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets. RESULTS: Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival. CONCLUSION: The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Single-cell and bulk transcriptional profiling of mouse ovaries reveals novel genes and pathways associated with DNA damage response in oocytes.

Immature oocytes enclosed in primordial follicles stored in female ovaries are under constant threat of DNA damage induced by endogenous and exogenous factors. Checkpoint kinase 2 (CHEK2) is a key mediator of the DNA damage response in all cells. Genetic studies have shown that CHEK2 and its downstream targets, p53 and TAp63, regulate primordial follicle elimination in response to DNA damage, however the mechanism leading to their demise is still poorly characterized. Single-cell and bulk RNA sequencing were used to determine the DNA damage response in wildtype and Chek2-deficient ovaries. A low but oocyte-lethal dose of ionizing radiation induces a DNA damage response in ovarian cells that is solely dependent on CHEK2. DNA damage activates multiple ovarian response pathways related to apoptosis, p53, interferon signaling, inflammation, cell adhesion, and intercellular communication. These pathways are differentially employed by different ovarian cell types, with oocytes disproportionately affected by radiation. Novel genes and pathways are induced by radiation specifically in oocytes, shedding light on their sensitivity to DNA damage, and implicating a coordinated response between oocytes and pre-granulosa cells within the follicle. These findings provide a foundation for future studies on the specific mechanisms regulating oocyte survival in the context of aging, as well as therapeutic and environmental genotoxic exposures.

Harnessing large language models (LLMs) for candidate gene prioritization and selection.

BACKGROUND: Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection. METHODS: In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene's biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene. RESULTS: Of the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module. CONCLUSIONS: Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Coliphage cocktails for controlling antimicrobial-resistant Escherichia coli: emphasizing polyphage and multihost interactions at different levels of multiplicity of infection.

AIM: This study elucidates the in-vitro bactericidal effectiveness of polyphage cocktail combinations of 2, 4, 6, 8, and 10 individual coliphages against a cocktail of 20 AMR Escherichia coli. METHODS AND RESULTS: Different polyphage cocktails viz., 45 two-phage combinations, 28 four-phage combinations, 15 six-phage combinations, 6 eight-phage combinations, and 1 ten-phage combination were formulated using a pool of ten coliphages that were isolated from two different geographical locations (East and West coasts of India). The different polyphage cocktails were tested at four different levels of Multiplicity of Infection (MOI) viz., MOI-1, MOI-10, MOI-100, and MOI-1000. All the 2, 4, 6, 8, and 10-phage cocktails were found to be effective in controlling the growth of a cocktail of 20 AMR bacteria when tested at MOI-1000 and MOI-100 but variations in antibacterial activity were observed at lower MOIs of 10 and 1. The ten coliphage cocktail showed lytic activity against 100% of AMR E. coli from farmed brackish water shrimp, 96% of laboratory collection of AMR E. coli, 92% of AMR E. coli from farmed freshwater fish, and 85% of AMR E. coli from market shrimp. CONCLUSION: Polyphage cocktails of 2, 4, 6, 8, and 10 coliphages applied at an MOI of 1000 effectively suppressed the growth of antimicrobial-resistant E. coli. The results indicated phage-phage synergy in the lytic activity of several coliphage combinations at higher MOIs of 1000 and 100 while phage-phage antagonism was evidenced at lower MOIs of 10 and 1.

Tetracycline resistance potential of heterotrophic bacteria isolated from freshwater fin-fish aquaculture system.

AIMS: This study investigated the tetracycline resistance potential of heterotrophic bacteria isolated from twenty-four freshwater fin-fish culture ponds in Andhra Pradesh, India. METHODS AND RESULTS: A total of 261 tetracycline resistant bacteria (tetR) were recovered from pond water, pond sediment, fish gills, fish intestine, and fish feed. Bacteria with high tetracycline resistance (tetHR) (n = 30) that were resistant to tetracycline concentrations above 128  µg mL-1 were predominantly Lactococcus garvieae followed by Enterobacter spp., Lactococcus lactis, Enterobacter hormaechei, Staphylococcus arlettae, Streptococcus lutetiensis, Staphylococcus spp., Brevundimonas faecalis, Exiguobacterium profundum, Lysinibacillus spp., Stutzerimonas stutzeri, Enterobacter cloacae, and Lactococcus taiwanensis. Resistance to 1024 µg mL-1 of tetracycline was observed in L. garvieae, S. arlettae, Enterobacter spp., B. faecalis. Tet(A) (67%) was the predominant resistance gene in tetHR followed by tet(L), tet(S), tet(K), and tet(M). At similar concentrations of exposure, tetracycline procured at the farm level (69.5% potency) exhibited lower inhibition against tetHR bacteria compared to pure tetracycline (99% potency). The tetHR bacteria showed higher cross-resistance to furazolidone (100%) followed by co-trimoxazole (47.5%) and enrofloxacin (11%). CONCLUSIONS: The maximum threshold of tetracycline resistance at 1024 µg mL-1 was observed in S. arlettae, Enterobacter spp., B. faecalis, and L. garvieae and tet(A) was the major determinant found in this study.

Adaptive Sentinel Testing in Workplace for COVID-19 Pandemic.

Testing and isolation of infectious employees is one of the critical strategies to make the workplace safe during the pandemic for many organizations. Adaptive testing frequency reduces cost while keeping the pandemic under control at the workplace. However, most models aimed at estimating test frequencies were structured for municipalities or large organizations such as university campuses of highly mobile individuals. By contrast, the workplace exhibits distinct characteristics: employee positivity rate may be different from the local community because of rigorous protective measures at workplace, or self-selection of co-workers with common behavioral tendencies for adherence to pandemic mitigation guidelines. Moreover, dual exposure to COVID-19 occurs at work and home that complicates transmission modeling, as does transmission tracing at the workplace. Hence, we developed bi-modal SEIR (Susceptible, Exposed, Infectious, and Removed) model and R-shiny tool that accounts for these differentiating factors to adaptively estimate the testing frequency for workplace. Our tool uses easily measurable parameters: community incidence rate, risks of acquiring infection from community and workplace, workforce size, and sensitivity of testing. Our model is best suited for moderate-sized organizations with low internal transmission rates, no-outward facing employees whose position demands frequent in-person interactions with the public, and low to medium population positivity rates. Simulations revealed that employee behavior in adherence to protective measures at work and in their community, and the onsite workforce size have large effects on testing frequency. Reducing workplace transmission rate through workplace mitigation protocols and higher sensitivity of the test deployed, although to a lesser extent. Furthermore, our simulations showed that sentinel testing leads to only marginal increase in the number of infections even for high community incidence rates, suggesting that this may be a cost-effective approach in future pandemics. We used our model to accurately guide testing regimen for three campuses of the Jackson Laboratory.

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer.

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research. SIGNIFICANCE: Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Multiplex immunofluorescence-guided laser capture microdissection for spatial transcriptomics of metastatic melanoma tissues.

We describe a pipeline for optimized and streamlined multiplexed immunofluorescence-guided laser capture microdissection allowing the harvest of individual cells based on their phenotype and tissue localization for transcriptomic analysis with next-generation RNA sequencing. Here, we analyze transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages, and melanoma cells in non-dissociated metastatic melanoma tissue. While this protocol is described for melanoma tissues, we successfully applied it to human tonsil, skin, and breast cancer tissues as well as mouse lung tissues. For complete details on the use and execution of this protocol, please refer to Martinek et al. (2022).

Transcriptional profiling of macrophages in situ in metastatic melanoma reveals localization-dependent phenotypes and function.

Modulation of immune function at the tumor site could improve patient outcomes. Here, we analyze patient samples of metastatic melanoma, a tumor responsive to T cell-based therapies, and find that tumor-infiltrating T cells are primarily juxtaposed to CD14+ monocytes/macrophages rather than melanoma cells. Using immunofluorescence-guided laser capture microdissection, we analyze transcriptomes of CD3+ T cells, CD14 + monocytes/macrophages, and melanoma cells in non-dissociated tissue. Stromal CD14+ cells display a specific transcriptional signature distinct from CD14+ cells within tumor nests. This signature contains LY75, a gene linked with antigen capture and regulation of tolerance and immunity in dendritic cells (DCs). When applied to TCGA cohorts, this gene set can distinguish patients with significantly prolonged survival in metastatic cutaneous melanoma and other cancers. Thus, the stromal CD14+ cell signature represents a candidate biomarker and suggests that reprogramming of stromal macrophages to acquire DC function may offer a therapeutic opportunity for metastatic cancers.

3D patient-derived tumor models to recapitulate pediatric brain tumors In Vitro.

Brain tumors are the leading cause of cancer-related deaths in children. Tailored therapies need preclinical brain tumor models representing a wide range of molecular subtypes. Here, we adapted a previously established brain tissue-model to fresh patient tumor cells with the goal of establishing3D in vitro culture conditions for each tumor type.Wereported our findings from 11 pediatric tumor cases, consisting of three medulloblastoma (MB) patients, three ependymoma (EPN) patients, one glioblastoma (GBM) patient, and four juvenile pilocytic astrocytoma (Ast) patients. Chemically defined media consisting of a mixture of pro-neural and pro-endothelial cell culture medium was found to support better growth than serum-containing medium for all the tumor cases we tested. 3D scaffold alone was found to support cell heterogeneity and tumor type-dependent spheroid-forming ability; both properties were lost in 2D or gel-only control cultures. Limited in vitro models showed that the number of differentially expressed genes between in vitro vs. primary tissues, are 104 (0.6%) of medulloblastoma, 3,392 (20.2%) of ependymoma, and 576 (3.4%) of astrocytoma, out of total 16,795 protein-coding genes and lincRNAs. Two models derived from a same medulloblastoma patient clustered together with the patient-matched primary tumor tissue; both models were 3D scaffold-only in Neurobasal and EGM 1:1 (v/v) mixture and differed by a 1-mo gap in culture (i.e., 6wk versus 10wk). The genes underlying the in vitrovs. in vivo tissue differences may provide mechanistic insights into the tumor microenvironment. This study is the first step towards establishing a pipeline from patient cells to models to personalized drug testing for brain cancer.

Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target.

A major rate-limiting step in developing more effective immunotherapies for GBM is our inadequate understanding of the cellular complexity and the molecular heterogeneity of immune infiltrates in gliomas. Here, we report an integrated analysis of 201,986 human glioma, immune, and other stromal cells at the single cell level. In doing so, we discover extensive spatial and molecular heterogeneity in immune infiltrates. We identify molecular signatures for nine distinct myeloid cell subtypes, of which five are independent prognostic indicators of glioma patient survival. Furthermore, we identify S100A4 as a regulator of immune suppressive T and myeloid cells in GBM and demonstrate that deleting S100a4 in non-cancer cells is sufficient to reprogram the immune landscape and significantly improve survival. This study provides insights into spatial, molecular, and functional heterogeneity of glioma and glioma-associated immune cells and demonstrates the utility of this dataset for discovering therapeutic targets for this poorly immunogenic cancer.

Comprehensive Molecular Characterization of Adenocarcinoma of the Gastroesophageal Junction Between Esophageal and Gastric Adenocarcinomas.

OBJECTIVE: To investigate the molecular characteristics of AGEJ compared with EAC and gastric adenocarcinoma. SUMMARY OF BACKGROUND DATA: Classification of AGEJ based on differential molecular characteristics between EAC and gastric adenocarcinoma has been long-standing controversy but rarely conducted due to anatomical ambiguity and epidemiologic difference. METHODS: The molecular classification model with Bayesian compound covariate predictor was developed based on differential mRNA expression of EAC (N = 78) and GCFB (N = 102) from the Cancer Genome Atlas (TCGA) cohort. AGEJ/cardia (N = 48) in TCGA cohort and AGEJ/upper third GC (N = 46 pairs) in Seoul National University cohort were classified into the EAC-like or GCFB-like groups whose genomic, transcriptomic, and proteomic characteristics were compared. RESULTS: AGEJ in both cohorts was similarly classified as EAC-like (31.2%) or GCFB-like (68.8%) based on the 400-gene classifier. The GCFB-like group showed significantly activated phosphoinositide 3-kinase-AKT signaling with decreased expression of ERBB2. The EAC-like group presented significantly different alternative splicing including the skipped exon of RPS24, a significantly higher copy number amplification including ERBB2 amplification, and increased protein expression of ERBB2 and EGFR compared with GCFB-like group. High-throughput 3D drug test using independent cell lines revealed that the EAC-like group showed a significantly better response to lapatinib than the GCFB-like group (P = 0.015). CONCLUSIONS: AGEJ was the combined entity of the EAC-like and GCFB-like groups with consistently different molecular characteristics in both Seoul National University and TCGA cohorts. The EAC-like group with a high Bayesian compound covariate predictor score could be effectively targeted by dual inhibition of ERBB2 and EGFR.

High-throughput visual assessment of sleep stages in mice using machine learning.

STUDY OBJECTIVES: Sleep is an important biological process that is perturbed in numerous diseases, and assessment of its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice. METHODS: We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods. RESULTS: Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 ± 0.05 (mean ± SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to noninvasively detect that sleep stage disturbances induced by amphetamine administration. CONCLUSIONS: We conclude that machine learning-based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable noninvasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Cancer stem cells, not bulk tumor cells, determine mechanisms of resistance to SMO inhibitors.

The emergence of treatment resistance significantly reduces the clinical utility of many effective targeted therapies. Although both genetic and epigenetic mechanisms of drug resistance have been reported, whether these mechanisms are stochastically selected in individual tumors or governed by a predictable underlying principle is unknown. Here, we report that the dependence of cancer stem cells (CSCs), not bulk tumor cells, on the targeted pathway determines the molecular mechanism of resistance in individual tumors. Using both spontaneous and transplantable mouse models of sonic hedgehog (SHH) medulloblastoma (MB) treated with an SHH/Smoothened inhibitor, sonidegib/LDE225, we show that genetic-based resistance occurs only in tumors that contain SHH-dependent CSCs (SD-CSCs). In contrast, SHH MBs containing SHH-dependent bulk tumor cells but SHH-independent CSCs (SI-CSCs) acquire resistance through epigenetic reprogramming. Mechanistically, elevated proteasome activity in SMOi-resistant SI-CSC MBs alters the tumor cell maturation trajectory through enhanced degradation of specific epigenetic regulators, including histone acetylation machinery components, resulting in global reductions in H3K9Ac, H3K14Ac, H3K56Ac, H4K5Ac, and H4K8Ac marks and gene expression changes. These results provide new insights into how selective pressure on distinct tumor cell populations contributes to different mechanisms of resistance to targeted therapies. This insight provides a new conceptual framework to understand responses and resistance to SMOis and other targeted therapies.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Local versus systemic control of bone and skeletal muscle mass by components of the transforming growth factor-ß signaling pathway.

Skeletal muscle and bone homeostasis are regulated by members of the myostatin/GDF-11/activin branch of the transforming growth factor-ß superfamily, which share many regulatory components, including inhibitory extracellular binding proteins and receptors that mediate signaling. Here, we present the results of genetic studies demonstrating a critical role for the binding protein follistatin (FST) in regulating both skeletal muscle and bone. Using an allelic series corresponding to varying expression levels of endogenous Fst, we show that FST acts in an exquisitely dose-dependent manner to regulate both muscle mass and bone density. Moreover, by employing a genetic strategy to target Fst expression only in the posterior (caudal) region of the animal, we show that the effects of Fst loss are mostly restricted to the posterior region, implying that locally produced FST plays a much more important role than circulating FST with respect to regulation of muscle and bone. Finally, we show that targeting receptors for these ligands specifically in osteoblasts leads to dramatic increases in bone mass, with trabecular bone volume fraction being increased by 12- to 13-fold and bone mineral density being increased by 8- to 9-fold in humeri, femurs, and lumbar vertebrae. These findings demonstrate that bone, like muscle, has an enormous inherent capacity for growth that is normally kept in check by this signaling system and suggest that the extent to which this regulatory mechanism may be used throughout the body to regulate tissue mass may be more significant than previously appreciated.

Predominance of genetically diverse ESBL Escherichia coli identified in resistance mapping of Vembanad Lake, the largest fresh-cum-brackishwater lake of India.

Antimicrobial resistance (AMR) burden in Escherichia coli along the 90 km stretch of Vembanad Lake, Kerala, India, was assessed. Seventy-seven percent of water samples drawn from 35 different stations of the lake harbored E. coli. Antibiotic susceptibility test performed on 116 E. coli isolates revealed resistance to ≥ one antibiotic with 39 AMR profiles in 81%, multidrug resistance in 30%, and extended spectrum ß-lactamase (ESBL) producers in 32%. Of all the 15 antibiotics tested, the probability of isolating cefotaxime-resistant E. coli was the highest (P ≤ 0.05) in the lake. Genetically diverse ESBL types, namely blaTEM-116, blaCTX-M -152, blaCTX-M -27, blaCTX-M -55, blaCTX-M-205, and blaSHV-27, were identified in the lake. This is probably the first report in India for the presence of blaCTX-M-205 (blaCTX-M-group 2) in the Vembanad Lake. ST11439 and single and double loci variants of ST443 and ST4533 were identified in multilocus sequence typing (MLST). Inc plasmids (B/O, F, W, I1, FIIA, HI1, P-1α, K/B, and N) identified in the lake evidences the resistance transmission potential of the E. coli isolated from the lake. Molecular typing (ERIC-PCR, MLST, and PBRT) delineated diverse E. coli, both between and within the sampling stations. Low multiple antibiotic resistance index (average MAR< 0.2) indicates a lower risk of the lake to the human population, but the occurrence of genetically diverse ESBL E. coli in the Vembanad Lake signals health hazards and necessitates pragmatic control measures.

A revisited two-step microtiter plate assay: Optimization of in vitro multiplicity of infection (MOI) for Coliphage and Vibriophage.

A 2-step microtiter plate assay was developed to simultaneously check wide values of MOIs of bacteriophages, ranging between MOI-0.0001 and MOI-10000 in the first step and optimize the most suitable MOI (lowest quantity of phage) for inhibiting the growth of the target bacteria in the second step. The results of the first step revealed that the effective MOI of coliphage-ɸ5 for controlling the growth of antimicrobial resistant (AMR) E. coli was between 4.36 and 43.6 for E.coli-EC-3; between 38.2 and 382 for E.coli-EC-7 and between 81.5 and 815 for E.coli-EC-11. The optimum MOI of coliphage-ɸ5 determined in the second step was 17.44, 191 and 326 for controlling the growth of E.coli-EC-3; E.coli-EC-7 and E.coli-EC-11, respectively. The effective MOI of vibriophage-ɸLV6 for controlling luminescent Vibrio harveyi in the first step was found to be between 18.3 and 183 and the optimum MOI as determined in the second step was 79. The sequential 2-step microtiter plate method yielded faster optimization of MOI and was economical compared to the conventional flask method. The measurement of OD values at 550 nm and 600 nm showed similar trend and replicate data from 5-wells and 3-wells yielded identical pattern indicating that the measuring absorbance data in 3-replicate wells at either OD550 or OD600 is sufficient to generate quantifiable phage lysis data. The 2-step microtiter plate assay finds application in phage therapy in human health care, agriculture and animal agriculture for determining the optimum MOIs for selected bacteriophages.

Human KIT+ myeloid cells facilitate visceral metastasis by melanoma.

Metastasis of melanoma significantly worsens prognosis; thus, therapeutic interventions that prevent metastasis could improve patient outcomes. Here, we show using humanized mice that colonization of distant visceral organs with melanoma is dependent upon a human CD33+CD11b+CD117+ progenitor cell subset comprising <4% of the human CD45+ leukocytes. Metastatic tumor-infiltrating CD33+ cells from patients and humanized (h)NSG-SGM3 mice showed converging transcriptional profiles. Single-cell RNA-seq analysis identified a gene signature of a KIT/CD117-expressing CD33+ subset that correlated with decreased overall survival in a TCGA melanoma cohort. Thus, human CD33+CD11b+CD117+ myeloid cells represent a novel candidate biomarker as well as a therapeutic target for metastatic melanoma.