Omics Approaches Research Articles

Climate Crisis and Agricultural Response: Climate Resilient Crops for Sustainability in Food Production Systems

Climate change encompasses altered levels of temperature and humidity, variability in the rainfall pattern, fluctuations in weather parameters, rise in ambient CO2 levels, emission of greenhouse gases, global warming, etc. that results in extreme events and disasters as cyclones, floods, droughts, salinity, nutrient and heavy metal stress, change in arthropod diversity and emergence of new invasive pests. This results in un-usual effects in agro-ecosystems leading to changes in cropping patterns, crop diversity, and their interaction with biotic and abiotic stress factors, threatening livelihood, food, and nutritional security. Population displacement, declining food productivity, and vulnerable agro-ecosystems are the major consequences of altered meteorological events that occur due to climate change. Therefore, substitution of traditional crops with crops that exhibit resilience to climate crisis is the need of the hour. Smart breeding approaches and precision farming technologies as remote sensing and spectral analysis, artificial intelligence, machine learning, speed breeding, genome analysis, genetic manipulation, gene drive systems, system biology study, omics approaches, etc can make agricultural production climate resilient and sustainable. Response of biofortified crops under changing climate must also be assessed to improve the crop productivity and output.

Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer's Disease.

Alzheimer's disease (AD), a multifactorial neurodegenerative disorder, is prevalent among the elderly population. It is a complex trait with mutations in multiple genes. Although the US Food and Drug Administration (FDA) has approved a few drugs for AD treatment, a definitive cure remains elusive. Research efforts persist in seeking improved treatment options for AD. Here, a hybrid pipeline is proposed to apply text mining to identify comorbid diseases for AD and an omics approach to identify the common genes between AD and five comorbid diseases-dementia, type 2 diabetes, hypertension, Parkinson's disease, and Down syndrome. We further identified the pathways and drugs for common genes. The rationale behind this approach is rooted in the fact that elderly individuals often receive multiple medications for various comorbid diseases, and an insight into the genes that are common to comorbid diseases may enhance treatment strategies. We identified seven common genes-PSEN1, PSEN2, MAPT, APP, APOE, NOTCH, and HFE-for AD and five comorbid diseases. We investigated the drugs interacting with these common genes using LINCS gene-drug perturbation. Our analysis unveiled several promising candidates, including MG-132 and Masitinib, which exhibit potential efficacy for both AD and its comorbid diseases. The pipeline can be extended to other diseases.

Untangling Structural and Functional Diversity of Prokaryotic Microbial Assemblage on Mangrove Pneumatophores

Untangling Structural and Functional Diversity of Prokaryotic Microbial Assemblage on Mangrove Pneumatophores

Integrating transcriptomic and proteomic data for a comprehensive molecular perspective on the association between sarcopenia and osteoporosis

BackgroundOsteoporosis and sarcopenia are common age-related conditions characterized by the progressive loss of bone density and muscle mass, respectively. Their co-occurrence, often referred to as osteosarcopenia, presents significant challenges in elderly care due to increased fragility and functional impairment. Existing studies have identified shared pathological mechanisms between these conditions, including inflammation, hormonal imbalances, and metabolic dysregulation, but a comprehensive understanding of their molecular interplay remains incomplete. ObjectiveThis study aims to deepen our understanding of the molecular interactions between sarcopenia and osteoporosis through an integrated omics approach, revealing potential therapeutic targets and biomarkers. MethodsEmploying a combination of proteomics and transcriptomics analyses, this study analyzed bone and muscle tissue samples from patients diagnosed with osteoporosis and osteosarcopenia. Techniques included high-throughput sequencing and label-free proteomics, supported by advanced bioinformatics tools for data analysis and functional annotation of genes and proteins. ResultsThe study found marked differences in gene and protein expressions between osteoporosis and osteosarcopenia tissues. Specifically, genes like PDIA5, TUBB1, and CYFIP2 in bone, along with MYH7 and NCAM1 in muscle, exhibited differential expression at both mRNA and protein levels. Pathway analyses revealed the significance of oxidative-reduction balance, cellular metabolism, and immune response in the progression of these conditions. Importantly, the study pinpointed osteoclast differentiation and NF-kappa B signaling pathways as critical in the molecular dynamics of osteosarcopenia, suggesting potential targets for therapy. ConclusionsThis study utilized transcriptomics and proteomics to identify key genes and proteins impacting sarcopenia and osteoporosis, employing advanced network tools to delineate interaction networks and crucial signaling pathways. It highlighted genes like PDIA5 and TUBB1, consistently expressed in both analyses, involved in pathways such as osteoclast differentiation and cytokine interactions. These insights enhance understanding of the molecular interplay in bone and muscle degeneration with aging, suggesting directions for future research into therapeutic interventions and prevention strategies for age-related degenerative diseases.

Molecular mechanisms and clinical relevance of endothelial cell cross-talk in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer in adults and stands out as one of the most vascularized and immune-infiltrated solid tumors. Overproduction of vascular endothelial growth factor A promotes uncontrolled growth of abnormal vessels and immunosuppression, and the tumor microenvironment (TME) has a prominent role in disease progression, drug targeting and drug response, and for patient outcome. Studies of experimental models, large-scale omics approaches, and patient prognosis and therapy prediction, using gene expression signatures and tissue biomarker analysis, have been reviewed for enhanced understanding of the endothelium in ccRCC and the interplay with the surrounding TME. Preclinical and clinical studies have discovered molecular mechanisms of endothelial cross-talk of relevance for disease progression, patient prognosis, and therapy prediction. There is, however, a lack of representative ccRCC experimental models. Omics approaches have identified clinically relevant subsets of angiogenic and immune-infiltrated tumors with distinct molecular signatures and distinct endothelial cell and immune cell populations in patients. Recent genetically engineered ccRCC mouse models together with emerging evidence from single cell RNA sequencing data open up for future validation studies, including multiplex imaging of ccRCC patient cohorts. These studies are of importance for therapy benefit and personalized treatment of ccRCC patients.

Broadening the horizon of immune thrombocytopenia through Omics approaches.

Immune thrombocytopenia (ITP) is a highly heterogeneous autoimmune bleeding disorder characterized by low platelet counts due to an immune-mediated platelet destruction and impaired platelet production. The pathophysiology is multifactorial and remains to be fully unravelled. Consequently, disease trajectories and responses to therapeutics, despite the availability of multiple agents, can be unpredictable and differing between patients. There is an urgent need for the identification of diagnostic and therapeutic biomarkers, but this has proven to be challenging to achieve. To shed light on this, two studies in this issue of the British Journal of Haematology have recognized the opportunity of using high-throughput Omics technologies in ITP. Sun etal. performed proteomics, and Li etal. metabolomics, on bone marrow biopsy samples of patients with ITP. This was conducted using mass spectrometry and, due to the generation of large datasets, in combination with machine learning. These studies set the stage for further investigations exploring the high potential of multi-omics technologies in order to shed light on the heterogeneity in ITP, accelerating the path towards a much needed personalized medicine approach. Commentary on: Li etal. Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study. Br J Haematol 2024;204:2405-2417. Commentary on: Sun etal. Proteomics landscape and machine learning prediction of long-term response to splenectomy in primary immune thrombocytopenia. Br J Haematol 2024;204:2418-2428.

An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients.

Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches. The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis. In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions. In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.

Unlocking Genetic Diversity and Germplasm Characterization with Molecular Markers: Strategies for Crop Improvement

Molecular markers have emerged as the most useful tools in assessment of genetic diversity and characterizing germplasm, for crop improvement. This review paper comprehensively analyzes the applications of molecular markers in diversity analysis and germplasm characterization. It underscores the significance of genetic diversity as the bedrock for plant breeding programs, enabling the development of improved varieties with desirable attributes such as higher yields, stress tolerance, and enhanced nutritional profiles. The paper provides an overview of various types of molecular markers, including hybridization-based markers (e.g., RFLPs) and PCR-based markers (e.g., RAPDs, AFLPs, SSRs, and SNPs). It discusses marker selection strategies, emphasizing the consideration of factors like polymorphism, informativeness, and the potential for multiplexing and high-throughput genotyping. Diversity analysis techniques, including principal component analysis (PCA), cluster analysis methods (UPGMA and Neighbor-Joining), and population structure analysis (model-based approaches like STRUCTURE), are detailed. These methods enable the assessment of genetic relationships, identification of subpopulations, and selection of diverse parents based on molecular marker data. The review further explores the applications of molecular markers in germplasm management, conservation, and utilization. It discusses the role of markers in targeted introgression of desirable traits from diverse sources, as well as the integration of genotypic and phenotypic data for association mapping, genomic prediction, and selection. Additionally, the paper addresses emerging technologies, such as next-generation sequencing (NGS) and the integration of molecular markers with other omics data. It also highlights the practical applications and impact of molecular markers in crop improvement programs, including marker-assisted selection (MAS), genomic selection, and the development of genetically modified crops. Finally, the review outlines challenges and future perspectives, including limitations0 of current technologies, the potential of emerging techniques like NGS, and the integration of molecular markers with other omics approaches for a comprehensive understanding of complex traits.

Extremophiles and their expanding biotechnological applications.

Microbial life is not restricted to any particular setting. Over the past several decades, it has been evident that microbial populations can exist in a wide range of environments, including those with extremes in temperature, pressure, salinity, and pH. Bacteria and Archaea are the two most reported types of microbes that can sustain in extreme environments, such as hot springs, ice caves, acid drainage, and salt marshes. Some can even grow in toxic waste, organic solvents, and heavy metals. These microbes are called extremophiles. There exist certain microorganisms that are found capable of thriving in two or more extreme physiological conditions simultaneously, and are regarded as polyextremophiles. Extremophiles possess several physiological and molecular adaptations including production of extremolytes, ice nucleating proteins, pigments, extremozymes and exopolysaccharides. These metabolites are used in many biotechnological industries for making biofuels, developing new medicines, food additives, cryoprotective agents etc. Further, the study of extremophiles holds great significance in astrobiology. The current review summarizes the diversity of microorganisms inhabiting challenging environments and the biotechnological and therapeutic applications of the active metabolites obtained as a response to stress conditions. Bioprospection of extremophiles provides a progressive direction with significant enhancement in economy. Moreover, the introduction to omics approach including whole genome sequencing, single cell genomics, proteomics, metagenomics etc., has made it possible to find many unique microbial communities that could be otherwise difficult to cultivate using traditional methods. These findings might be capable enough to state that discovery of extremophiles can bring evolution to biotechnology.

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this ‘omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

Don’t lose your transporters! Optimizing renal transporter recovery after kidney freezing

Renal ion transporters are critical to maintain fluid and electrolyte homeostasis and are primary targets for (patho)physiology studies and drug discovery. Transporter abundance and covalent modifications are determined by immunoblots. Our standard protocol is quick kidney removal and homogenization in buffer containing 0.2 mM PMSF, 0.9 ug/ml aprotinin, and Sigma P0044 phosphatase inhibitors followed by isolation of a low speed supernatant (the homogenate). Collaborations with other labs requires shipping frozen kidneys. We tested whether flash freezing (FZ) Sprague Dawley (SD) kidneys impacts ion transporter recovery compared to quick removal without freezing (QR) defined as 100% recovery; assessed by immunoblot with specific antibodies. FZ and ~ 3 d storage before homogenization in inhibitors significantly reduced recoveries of proximal tubule (PT) NHE3 and megalin to 20% and Cldn2 to 40%. Medullary NHE3 and NKCC2 remained at 100%, DCT/CD proteins were reduced ~ 10-60% after FZ vs. QR. Renal perfusion pre-FZ further reduced recovery of all transporters vs. non-perfused kidneys. To test whether Fz activates proteases not inhibited in our standard buffer, Roche protease inhibitor (minitabs) were added to homogenization buffer. Adding minitabs to FZ kidneys increased NHE3 recovery to 85% and Cldn2 recovery to 100% of QR, NKCC2 and ENaC-beta to 85% and NCC to100%. Lastly, we tested, in male and female (M,F) SD rats, whether minitab addition to QR renal cortex homogenates followed by Fz prevents proteolysis vs. minitab addition to QR homog without Fz. In F, minitab addition with Fz preserved 100% recovery of transporters: NHE3, Cldn2, AQP1, megalin, NKCC2, NCC, ENaC-beta, Cldn-7 and AQP2. In M, minitab addition with Fz preserved 100% recovery of only AQP1, villin, Cldn7 and AQP2; all other M transporters exhibited ~40% less recovery. In conclusion, flash freezing kidney tissue differentially activates proteolysis of ion transporters - up to 80% in cortex and not at all in medulla - despite inclusion of standard inhibitors in homogenizing buffers. Strong sex differences are evident: addition of minitabs prevents proteolysis of F not M cortical ion transporters. Single cell ‘omics approaches may be useful to identify tubule and sex specific proteases. NIH: DK083785, DK134695. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Decoding the molecular mechanism underlying salicylic acid (SA)-mediated plant immunity: an integrated overview from its biosynthesis to the mode of action.

Salicylic acid (SA) is an important phytohormone, well-known for its regulatory role in shaping plant immune responses. In recent years, significant progress has been made in unravelling the molecular mechanisms underlying SA biosynthesis, perception, and downstream signalling cascades. Through the concerted efforts employing genetic, biochemical, and omics approaches, our understanding of SA-mediated defence responses has undergone remarkable expansion. In general, following SA biosynthesis through Avr effectors of the pathogens, newly synthesized SA undergoes various biochemical changes to achieve its active/inactive forms (e.g. methyl salicylate). The activated SA subsequently triggers signalling pathways associated with the perception of pathogen-derived signals, expression of defence genes, and induction of systemic acquired resistance (SAR) to tailor the intricate regulatory networks that coordinate plant immune responses. Nonetheless, the mechanistic understanding of SA-mediated plant immune regulation is currently limited because of its crosstalk with other signalling networks, which makes understanding this hormone signalling more challenging. This comprehensive review aims to provide an integrated overview of SA-mediated plant immunity, deriving current knowledge from diverse research outcomes. Through the integration of case studies, experimental evidence, and emerging trends, this review offers insights into the regulatory mechanisms governing SA-mediated immunity and signalling. Additionally, this review discusses the potential applications of SA-mediated defence strategies in crop improvement, disease management, and sustainable agricultural practices.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets.

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

Integrative Omic Approaches to Link PTSD Risk Variation to Function

Integrative Omic Approaches to Link PTSD Risk Variation to Function

Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease.

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

From flesh to bones: Multi-omics approaches in forensic science.

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

An integrated omics approach highlights how epigenetic events can explain and predict response to neoadjuvant chemotherapy and bevacizumab in breast cancer.

Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression-methylation quantitative trait loci analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-epithelial-to-mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell-specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.

Biodegradation of acetaminophen: Microcosm centric genomic-proteomic-metabolomics evidences

Acetaminophen (APAP) is a frequently used, over-the-counter analgesic and antipyretic medication. Considering increase in global consumption, its ubiquity in environment with potential toxic impacts has become a cause of great concern. Hence, bioremediation of this emerging contaminant is of paramount significance. The present study incorporates a microcosm centric omics approach to gain in-depth insights into APAP degradation by Paracoccus sp. APAP_BH8. It can metabolize APAP (300 mg kg−1) within 16 days in soil microcosms. Genome analysis revealed potential genes capable of mediating degradation includes M20 aminoacylase family protein, guanidine deaminase, 4-hydroxybenzoate 3-monooxygenase, and 4-hydroxyphenylpyruvate dioxygenase. Whole proteome analysis showed differential expression of enzymes and bioinformatics provided evidence for stable binding of intermediates at the active site of considered enzymes. Metabolites identified were 4-aminophenol, hydroquinone, and 3-hydroxy-cis, cis-muconate. Therefore, Paracoccus sp. APAP_BH8 with versatile enzymatic and genetic attributes can be a promising candidate for formulating improved in situ APAP bioremediation strategies.

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches.

The maturation of B cells is a complex, multi-step process. During B cell differentiation, errors can occur, leading to the emergence of aberrant versions of B cells that, finally, constitute a malignant tumor. These B cell malignancies are classified into three main groups: leukemias, myelomas, and lymphomas, the latter being the most heterogeneous type. Since their discovery, multiple biological studies have been performed to characterize these diseases, aiming to define their specific features and determine potential biomarkers for diagnosis, stratification, and prognosis. The rise of advanced -omics approaches has significantly contributed to this end. Notably, proteomics strategies appear as promising tools to comprehensively profile the final molecular effector of these cells. In this narrative review, we first introduce the main B cell malignancies together with the most relevant proteomics approaches. Then, we describe the core studies conducted in the field and their main findings and, finally, we evaluate the advantages and drawbacks of flow cytometry, mass cytometry, and mass spectrometry for the profiling of human B cell disorders.

Bioinformatic Analysis of Metabolomic Data: From Raw Spectra to Biological Insight

Metabolites are at the end of the gene–transcript–protein–metabolism cascade. As such, metabolomics is the omics approach that offers the most direct correlation with phenotype. This allows, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics to possibly provide an answer. Complex phenotypes, which are determined by the influence of multiple small-effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased exponentially in recent years. As a newer discipline, metabolomic bioinformatic analysis pipelines are not as standardized as in the other omics approaches. In this review, we synthesized the different steps that need to be carried out to obtain biological insight from annotated metabolite abundance raw data. These steps were grouped into three different modules: preprocessing, statistical analysis, and metabolic pathway enrichment. We included within each one of them the different state-of-the-art procedures and tools that can be used depending on the characteristics of the study, providing details about each method’s characteristics and the issues the reader might encounter. Finally, we introduce genome-scale metabolic modeling as a tool for obtaining pseudo-metabolomic data in situations where their acquisition is difficult, enabling the analysis of the resulting data with the modules of the described workflow.